# Best Data Labeling Software

## How Many Data Labeling Software Products Does G2 Track?

**Total Products under this Category:** 130

### Category Stats (Aug 2026)

- **Average Rating:** 4.49/5 (↓0.01 vs Jul 2026) The average rating of products in this category, based on all submitted ratings
- **Top Trending Product:** FiftyOne (+1.01%) - Among all products in this category, FiftyOne recorded the largest rating increase compared to last month

_Last updated: August 20, 2026_

## How Does G2 Rank Data Labeling Software Products?

**Why You Can Trust G2's Software Rankings:**

- 30 Analysts and Data Experts
- 1,800+ Authentic Reviews
- 130+ Products
- Unbiased Rankings

G2's software rankings are built on verified user reviews, rigorous moderation, and a consistent research methodology maintained by a team of analysts and data experts. Each product is measured using the same transparent criteria, with no paid placement or vendor influence. While reviews reflect real user experiences, which can be subjective, they offer valuable insight into how software performs in the hands of professionals. Together, these inputs power the G2 Score, a standardized way to compare tools within every category.

## G2 Grid® for Data Labeling Software
 ![G2 Grid® for Data Labeling Software plotting products by satisfaction and market presence](https://www.g2.com/categories/data-labeling/grids.png?focus%5B%5D=128515&focus%5B%5D=125020&focus%5B%5D=168222&focus%5B%5D=87452&focus%5B%5D=1315312&focus%5B%5D=125450&focus%5B%5D=78925&focus%5B%5D=126287)

Highlighted products: SuperAnnotate, Roboflow, Encord, Amazon Sagemaker Ground Truth, CVAT, Datasaur, Labelbox, and V7 Darwin.

Underlying data: [Grid® JSON](https://www.g2.com/categories/data-labeling/grids.json?focus%5B%5D=superannotate&focus%5B%5D=roboflow&focus%5B%5D=encord&focus%5B%5D=amazon-sagemaker-ground-truth&focus%5B%5D=cvat&focus%5B%5D=datasaur&focus%5B%5D=labelbox&focus%5B%5D=v7-darwin)

**Sponsored**

### Amazon SageMaker

Amazon SageMaker is a fully managed service that enables data scientists and developers to build, train, and deploy machine learning (ML) models at scale. It provides a comprehensive suite of tools and infrastructure, streamlining the entire ML workflow from data preparation to model deployment. With SageMaker, users can quickly connect to training data, select and optimize algorithms, and deploy models in a secure and scalable environment. Key Features and Functionality: - Integrated Development Environments (IDEs): SageMaker offers a unified, web-based interface with built-in IDEs, including JupyterLab and RStudio, facilitating seamless development and collaboration. - Pre-built Algorithms and Frameworks: It includes a selection of optimized ML algorithms and supports popular frameworks like TensorFlow, PyTorch, and Apache MXNet, allowing flexibility in model development. - Automated Model Tuning: SageMaker can automatically tune models to achieve optimal accuracy, reducing the time and effort required for manual adjustments. - Scalable Training and Deployment: The service manages the underlying infrastructure, enabling efficient training of models on large datasets and deploying them across auto-scaling clusters for high availability. - MLOps and Governance: SageMaker provides tools for monitoring, debugging, and managing ML models, ensuring robust operations and compliance with enterprise security standards. Primary Value and Problem Solved: Amazon SageMaker addresses the complexity and resource-intensive nature of developing and deploying ML models. By offering a fully managed environment with integrated tools and scalable infrastructure, it accelerates the ML lifecycle, reduces operational overhead, and enables organizations to derive insights and value from their data more efficiently. This empowers businesses to innovate rapidly and implement AI solutions without the need for extensive in-house expertise or infrastructure management.

[Visit website](https://www.g2.com/external_clickthroughs/record?secure%5Bad_program%5D=ppc&secure%5Bad_slot%5D=category_product_list_llm&secure%5Bcategory_id%5D=2361&secure%5Bchosen_at%5D=2026-08-28T23%3A07%3A17Z&secure%5Bdisplayable_resource_id%5D=2361&secure%5Bdisplayable_resource_type%5D=Category&secure%5Bmedium%5D=sponsored&secure%5Bplacement_reason%5D=page_category&secure%5Bplacement_resource_ids%5D%5B%5D=2361&secure%5Bprioritized%5D=false&secure%5Bproduct_id%5D=52115&secure%5Bresource_id%5D=2361&secure%5Bresource_type%5D=Category&secure%5Bsource_type%5D=category_page&secure%5Bsource_url%5D=https%3A%2F%2Fwww.g2.com%2Fcategories%2Fdata-labeling%3Fsource%3Dsearch&secure%5Btoken%5D=00be1eaeebfa6908e6cfb8f7543c9f8209aa2425f26026448a587156d217658b&secure%5Burl%5D=https%3A%2F%2Faws.amazon.com%2Fsagemaker%2F%3Ftrk%3De054ba95-b51d-4594-98bc-aa0239b1797a%26sc_channel%3Ddisplay%2Bads&secure%5Burl_type%5D=custom_url)

### [SuperAnnotate](https://www.g2.com/products/superannotate/reviews)

SuperAnnotate bridges the gap between cutting-edge AI innovation and the high-quality human data that powers it - helping advanced AI teams build more intelligent models. With a global network of thousands of rigorously vetted experts, ethical and scalable managed operations, precise talent matching, and purpose‑built technology, SuperAnnotate delivers full project visibility and unmatched data quality. SuperAnnotate powers complex annotation, evaluation, and reinforcement learning workflows to build, evaluate and align frontier AI. Trusted by innovators like Databricks, IBM and ServiceNow - and backed by NVIDIA, Dell Technologies Capital, Databricks Ventures, Cox Enterprises, and Lionel Messi’s Play Time VC - SuperAnnotate enables the world’s top AI teams to build responsible and state‑of‑the‑art models with human data.

**Average Rating:** 4.8/5.0

**Total Reviews:** 356

#### How Do G2 Users Rate SuperAnnotate?

- **Labeler Quality:** 9.6/10 (Category avg: 8.9/10)
- **Object Detection:** 9.4/10 (Category avg: 8.9/10)
- **Data Types:** 9.5/10 (Category avg: 8.8/10)
- **Ease of Use:** 9.5/10 (Category avg: 8.8/10)

#### Who Is the Company Behind SuperAnnotate?

- **Seller:** [SuperAnnotate](https://www.g2.com/sellers/superannotate)
- **Company Website:** superannotate.com
- **Year Founded:** 2018
- **HQ Location:** San Francisco, CA
- **Twitter:** @superannotate  
720 Twitter followers
- **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=ed4d3a394b0ca2eaac6c63041f9bd1bf14ee26538d356cd977a2b0f50c15f4d1&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2F18999422%2F&secure%5Burl_type%5D=linkedin_company_website)  
361 employees on LinkedIn®

#### Who Uses This Product?

- **Who Uses This:** Student, Data Trainer
- **Top Industries:** Information Technology and Services, Computer Software
- **Company Size:** 57% Small, 23% Medium

#### What Do G2 Reviewers Say About SuperAnnotate?

_AI-generated summary from verified user reviews_

##### Pros

- Users enjoy the **intuitive interface** of SuperAnnotate, which simplifies large-scale annotation projects and boosts collaboration.
- Users enjoy the **user-friendly interface** of SuperAnnotate, facilitating efficient and accurate annotations with powerful tools.
- Users praise the **annotation efficiency** of SuperAnnotate, appreciating its time-saving features and user-friendly interface.
- Users highlight the **efficiency** of SuperAnnotate, enabling quick, high-quality annotations with user-friendly tools and collaboration features.
- Users value the **high-quality annotations** provided by SuperAnnotate, enhancing efficiency and ensuring consistency across projects.

##### Cons

- Users have noted **performance issues** with SuperAnnotate, including slow loading times for large projects and technical glitches.
- Users often face **slow performance** , experiencing lag and hanging when cropping images and labeling tasks on SuperAnnotate.
- Users find the **difficult learning curve** challenging, particularly with advanced features and large datasets requiring time to master.
- Users find the **complexity for new users** of SuperAnnotate challenging, especially with advanced tools and features.
- Users find the **lack of guidance** challenging, making the learning curve steep for new users of SuperAnnotate.

#### What Are Recent G2 Reviews of SuperAnnotate?

**["Efficient and User-Friendly Data Annotation Tool"](https://www.g2.com/survey_responses/superannotate-review-13350481)**

**Rating:** 5.0/5.0 stars

_— Aggunuru V._

[Read full review](https://www.g2.com/survey_responses/superannotate-review-13350481)

**["Easy-to-Use Data Organization and Powerful Image-Splitting Tools"](https://www.g2.com/survey_responses/superannotate-review-13146852)**

**Rating:** 5.0/5.0 stars

_— Doniaa K._

[Read full review](https://www.g2.com/survey_responses/superannotate-review-13146852)

#### What Are G2 Users Discussing About SuperAnnotate?

- [What is your experience with SuperAnnotate for data annotation, and what would you like to see improved?](https://www.g2.com/discussions/what-is-your-experience-with-superannotate-for-data-annotation-and-what-would-you-like-to-see-improved) - 1 comment
- [How do I annotate an image in OpenCV?](https://www.g2.com/discussions/how-do-i-annotate-an-image-in-opencv)
- [Is SuperAnnotate free?](https://www.g2.com/discussions/is-superannotate-free)
- [How do you use SuperAnnotate?](https://www.g2.com/discussions/how-do-you-use-superannotate)
- [What is SuperAnnotate?](https://www.g2.com/discussions/what-is-superannotate) - 1 comment, 2 upvotes

### [Roboflow](https://www.g2.com/de/products/roboflow/reviews)

Roboflow hat alles, was Sie benötigen, um Computer-Vision-Anwendungen zu erstellen und bereitzustellen. Über 1.000.000 Nutzer aus Unternehmen jeder Größe – von Startups bis hin zu börsennotierten Unternehmen – nutzen die End-to-End-Plattform des Unternehmens für die Sammlung, Organisation, Annotation, Vorverarbeitung, Modelltraining und Bereitstellung von Bildern und Videos. Roboflow bietet Werkzeuge für jeden Schritt im Lebenszyklus der Computer-Vision-Bereitstellung und integriert sich in Ihre bestehenden Lösungen, sodass Sie Ihre Pipeline an Ihre Bedürfnisse anpassen können.

**Average Rating:** 4.7/5.0

**Total Reviews:** 158

#### How Do G2 Users Rate Roboflow?

- **Qualität des Etikettierers:** 9.0/10 (Category avg: 8.9/10)
- **Objekt-Erkennung:** 9.1/10 (Category avg: 8.9/10)
- **Datentypen:** 8.7/10 (Category avg: 8.8/10)
- **Einfache Bedienung:** 9.3/10 (Category avg: 8.8/10)

#### Who Is the Company Behind Roboflow?

- **Verkäufer:** [Roboflow](https://www.g2.com/de/sellers/roboflow)
- **Gründungsjahr:** 2019
- **Hauptsitz:** Remote, US
- **Twitter:** @roboflow  
13,577 Twitter-Follower
- **LinkedIn®-Seite:** [www.linkedin.com](https://www.g2.com/de/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=660f87d85fdd82e0f1cecfe2354a16103bb5a6f5508134496575ec655201678c&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2F36096640&secure%5Burl_type%5D=linkedin_company_website)  
144 Mitarbeiter\*innen auf LinkedIn®

#### Who Uses This Product?

- **Who Uses This:** Gründer, Forscher
- **Top Industries:** Computersoftware, Forschung
- **Company Size:** 78% Small, 14% Medium

#### What Do G2 Reviewers Say About Roboflow?

_AI-generated summary from verified user reviews_

##### Pros

- Benutzer schätzen die **Benutzerfreundlichkeit** von Roboflow, die effizientes Modelltraining und Zusammenarbeit mit einer benutzerfreundlichen Oberfläche ermöglicht.
- Benutzer heben die **Effizienz** von Roboflow im Datenmanagement hervor, Aufgaben zu optimieren und erheblich Zeit zu sparen und Fehler zu reduzieren.
- Benutzer schätzen die **Annotationseffizienz** von Roboflow, da sie Zeitersparnis und weniger Fehler bei der Datenverwaltung genießen.
- Benutzer lieben, wie Roboflows **Datenkennzeichnung** die Zusammenarbeit, Annotation und Exportprozesse vereinfacht, Zeit spart und Fehler reduziert.
- Benutzer schätzen die **leistungsstarken und vielseitigen Funktionen** von Roboflow, was es ideal für akademische und groß angelegte Projekte macht.

##### Cons

- Benutzer finden die **Kosten unerschwinglich** für fortgeschrittene Funktionen, insbesondere Studenten, die budgetfreundliche Optionen benötigen.
- Benutzer bemerken die **begrenzten Funktionen** von Roboflow, da einige erweiterte Optionen höhere Tarifpläne erfordern und Einschränkungen bestehen.
- Benutzer erleben **eingeschränkte Funktionalität** in Roboflow, insbesondere bei fortgeschrittenen Funktionen und Flexibilität für komplexe Aufgaben.
- Benutzer finden **Annotierungsprobleme** mit Roboflow, insbesondere beim automatischen Labeling und der Polygonmarkierung für komplexe Bilder.
- Benutzer finden **ineffiziente Kennzeichnungs** prozesse umständlich, insbesondere in Teamumgebungen mit einem Mangel an Automatisierung und Abkürzungen.

#### What Are Recent G2 Reviews of Roboflow?

**["Roboflow macht Computer-Vision-Projekte einfach zu erstellen, zu trainieren und bereitzustellen."](https://www.g2.com/de/survey_responses/roboflow-review-12984362)**

**Rating:** 5.0/5.0 stars

_— noah r._

[Read full review](https://www.g2.com/de/survey_responses/roboflow-review-12984362)

**["Roboflow beschleunigte unseren Computer Vision POC mit einfacher Daten- und Modelliteration."](https://www.g2.com/de/survey_responses/roboflow-review-13277088)**

**Rating:** 4.0/5.0 stars

_— Verifizierter Benutzer in Geschäftsausstattung und -bedarf_

[Read full review](https://www.g2.com/de/survey_responses/roboflow-review-13277088)

### [Encord](https://www.g2.com/de/products/encord/reviews)

Encord ist die universelle Datenschicht für KI. Die Plattform hilft KI-Teams, ihre Modelle mit den richtigen Daten zu trainieren und auszuführen - indem sie Daten über den gesamten KI-Lebenszyklus verwalten, kuratieren, annotieren und ausrichten. Encord arbeitet mit über 300 führenden KI-Teams zusammen, darunter Woven by Toyota, Zipline, AXA und Flock Safety. Vertraulich Produktions-KI mit reichhaltigen multimodalen Daten aufbauen. Encord ist SOC 2, AICPA SOC, HIPAA und DSGVO konform.

**Average Rating:** 4.8/5.0

**Total Reviews:** 65

#### How Do G2 Users Rate Encord?

- **Qualität des Etikettierers:** 9.4/10 (Category avg: 8.9/10)
- **Objekt-Erkennung:** 9.3/10 (Category avg: 8.9/10)
- **Datentypen:** 9.7/10 (Category avg: 8.8/10)
- **Einfache Bedienung:** 9.5/10 (Category avg: 8.8/10)

#### Who Is the Company Behind Encord?

- **Verkäufer:** [Encord](https://www.g2.com/de/sellers/encord)
- **Gründungsjahr:** 2020
- **Hauptsitz:** San Francisco, US
- **Twitter:** @encord\_team  
1,014 Twitter-Follower
- **LinkedIn®-Seite:** [www.linkedin.com](https://www.g2.com/de/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=b5b79c871099fa074d165dec60c3345148d0ae005f29a546864b838e8a7500a8&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2F69557125&secure%5Burl_type%5D=linkedin_company_website)  
205 Mitarbeiter\*innen auf LinkedIn®

#### Who Uses This Product?

- **Top Industries:** Computersoftware, Krankenhaus & Gesundheitswesen
- **Company Size:** 51% Small, 40% Medium

#### What Do G2 Reviewers Say About Encord?

_AI-generated summary from verified user reviews_

##### Pros

- Benutzer loben Encord für seinen **reaktiven Kundensupport** , der schnelle Lösungen und nahtlose Zusammenarbeit während der Projekte gewährleistet.
- Benutzer loben Encord für seine **Annotationseffizienz** und schätzen den reibungslosen Arbeitsablauf sowie die intuitive Benutzeroberfläche, die die Produktivität steigert.
- Benutzer schätzen die **intuitive Benutzeroberfläche und die leistungsstarken KI-Funktionen** von Encord, die ihre Effizienz bei der Datenannotation erheblich steigern.
- Benutzer schätzen die **Effizienz** von Encord, da sie reibungslose Arbeitsabläufe und schnelle Datenintegration erleben, die ihre Prozesse beschleunigen.
- Benutzer schätzen die **intuitive Benutzeroberfläche** und die umfassenden Funktionen von Encord, die die Effizienz bei der Datenkurierung und -annotation verbessern.

##### Cons

- Benutzer finden, dass **benutzerdefinierte Workflows herausfordernd sein können** , aber Unterstützung vom Team hilft, die Schwierigkeiten zu mildern.
- Benutzer finden es herausfordernd, mit den **häufigen Updates** von Encord Schritt zu halten, trotz Unterstützung durch ihr Customer-Success-Team.
- Benutzer finden es herausfordernd, mit den **Best Practices** Schritt zu halten, da Encord häufig Funktionen aktualisiert.

#### What Are Recent G2 Reviews of Encord?

**["Starke Videokennzeichnungsplattform mit exzellentem Support"](https://www.g2.com/de/survey_responses/encord-review-12281672)**

**Rating:** 5.0/5.0 stars

_— Angela S._

[Read full review](https://www.g2.com/de/survey_responses/encord-review-12281672)

**["Entwickelt für schnelle Modellentwicklungszyklen"](https://www.g2.com/de/survey_responses/encord-review-12219596)**

**Rating:** 5.0/5.0 stars

_— Brian E._

[Read full review](https://www.g2.com/de/survey_responses/encord-review-12219596)

### [CVAT](https://www.g2.com/de/products/cvat/reviews)

Unternehmensübersicht: CVAT.ai ist ein globaler Anbieter von Datenannotationswerkzeugen und -dienstleistungen, bekannt für die Entwicklung eines der beliebtesten Open-Source-Annotationstools, CVAT. Zusätzlich zur Open-Source-Plattform bieten wir professionelle Datenkennzeichnungsdienste, eine Enterprise-Version von CVAT sowie Beratungs- und Anpassungsdienste, um spezifische Kundenbedürfnisse zu erfüllen. Unser Team unterstützt Unternehmen und KI-Forscher weltweit bei der effizienten Verwaltung der Datenannotation für Computer-Vision-Projekte. Hauptmerkmale: - Beliebtes Open-Source-Tool: CVAT wird von Tausenden von Entwicklern und Organisationen weltweit vertraut. - Datenkennzeichnungsdienste: Wir bieten professionelle Datenkennzeichnungsdienste, um Projekte von Anfang bis Ende zu bearbeiten. - Enterprise-Version von CVAT: Die Enterprise-Version bietet erweiterte Funktionen, Unterstützung und Skalierbarkeit für größere Organisationen. - Beratung und Anpassung: Wir bieten Beratungsdienste an und können CVAT an Ihre Projektanforderungen anpassen. Erfahren Sie hier mehr über unseren Ansatz zur Beratung und zu Funktionsanfragen. - KI-unterstützte Automatisierung: Unsere Plattform nutzt KI, um die Kennzeichnungseffizienz und -genauigkeit zu verbessern. - Teamzusammenarbeit: Teams können nahtlos an groß angelegten Projekten zusammenarbeiten. - Anpassbar und skalierbar: CVAT kann an Ihre Projektgröße und -anforderungen angepasst werden. - Sicher: Wir erfüllen globale Datenschutz- und Sicherheitsstandards. Was wir lösen: CVAT.ai hilft Benutzern, den manuellen Aufwand zu reduzieren, indem die Datenannotation schneller, genauer und einfacher zu verwalten ist. Durch unsere Open-Source-Plattform, professionelle Kennzeichnungsdienste, Beratung und die Enterprise-Version bieten wir eine flexible, umfassende Lösung für jedes Computer-Vision-Projekt.

**Average Rating:** 4.6/5.0

**Total Reviews:** 46

#### How Do G2 Users Rate CVAT?

- **Qualität des Etikettierers:** 9.5/10 (Category avg: 8.9/10)
- **Objekt-Erkennung:** 9.3/10 (Category avg: 8.9/10)
- **Datentypen:** 8.6/10 (Category avg: 8.8/10)
- **Einfache Bedienung:** 8.7/10 (Category avg: 8.8/10)

#### Who Is the Company Behind CVAT?

- **Verkäufer:** [CVAT.ai](https://www.g2.com/de/sellers/cvat-ai)
- **Unternehmenswebsite:** www.cvat.ai
- **Gründungsjahr:** 2022
- **Hauptsitz:** Wilmington, US
- **LinkedIn®-Seite:** [www.linkedin.com](https://www.g2.com/de/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=3fcff5bc744a0faed9f68d2f3d4c21c790b4a51f28c113fb6952ea0700f32084&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2Fcvat-ai%2F&secure%5Burl_type%5D=linkedin_company_website)  
114 Mitarbeiter\*innen auf LinkedIn®

#### Who Uses This Product?

- **Top Industries:** Computersoftware, Forschung
- **Company Size:** 79% Small, 13% Medium

#### What Do G2 Reviewers Say About CVAT?

_AI-generated summary from verified user reviews_

##### Pros

- Benutzer loben den **ausgezeichneten Kundensupport** von CVAT.ai, der reibungslose Lösungen für technische Herausforderungen ermöglicht.
- Benutzer loben die **Annotationseffizienz** von CVAT.ai und heben die beeindruckende Geschwindigkeit und Qualität bei der Bearbeitung komplexer Aufgaben hervor.
- Benutzer schätzen die **Anpassungsoptionen** von CVAT.ai, die maßgeschneiderte Projekte und vielseitige Einrichtungsszenarien ermöglichen.
- Benutzer loben die **effizienten Datenverwaltungs** funktionen von CVAT.ai, die die Produktivität durch nahtlose Integration und intuitive Werkzeuge steigern.
- Benutzer schätzen die **vielseitigen Annotationsoptionen** von CVAT.ai und heben die umfassende Unterstützung für verschiedene Computer-Vision-Algorithmen hervor.

##### Cons

- Benutzer finden die Plattform **schwer zu erlernen** , insbesondere für Anfänger, die sich von ihren Funktionen überwältigt fühlen könnten.
- Benutzer finden die **Komplexität** von CVAT.ai herausfordernd, insbesondere für Anfänger, die Schwierigkeiten haben, seine Funktionen zu navigieren.
- Benutzer stehen vor **Kennzeichnungsproblemen** aufgrund inkonsistenter Ansätze, aber der Support geht diese Bedenken umgehend an, um die Genauigkeit zu verbessern.
- Benutzer bemerken den **Mangel an Unterstützung für große 16-Bit-Bilder** , was die Funktionalität und Benutzerfreundlichkeit in ihren Arbeitsabläufen einschränkt.
- Benutzer erleben **langsame Leistung** beim Umgang mit sehr großen Videodateien und Tausenden von Bildern auf CVAT.ai.

#### What Are Recent G2 Reviews of CVAT?

**["CVat Makes the Annotation Work Times Faster"](https://www.g2.com/de/survey_responses/cvat-review-13347987)**

**Rating:** 5.0/5.0 stars

_— Ekaterina V._

[Read full review](https://www.g2.com/de/survey_responses/cvat-review-13347987)

**["Fast, User-Friendly Annotation with Powerful Auto-Labeling in CVAT"](https://www.g2.com/de/survey_responses/cvat-review-13358714)**

**Rating:** 5.0/5.0 stars

_— Annotationbd M._

[Read full review](https://www.g2.com/de/survey_responses/cvat-review-13358714)

## FAQs About Data Labeling Software

Generated using AI

Last updated: June 3, 2026

### Which Data Labeling platforms support computer vision models using bounding boxes and point cloud annotation

Based on G2 reviews, these products are the clearest fits for image and point cloud annotation needs.

- [Taskmonk](https://www.g2.com/products/taskmonk) — LiDAR projects with quality checks.
- [SuperAnnotate](https://www.g2.com/products/superannotate) — bounding boxes, segmentation, review workflows.
- [Segments.ai](https://www.g2.com/products/segments-ai) — multi-sensor and point cloud labeling.
- [Roboflow](https://www.g2.com/products/roboflow) — computer vision datasets and annotations.

### Data Labeling platforms that maintain annotation quality while handling high-volume image processing without slowdowns

According to verified users, teams evaluating data labeling software often look for a balance between speed, consistency, and review controls. Recent G2 reviews highlight strengths such as built-in quality checks, version control, structured review steps, and collaboration features that help maintain labeling quality at scale. Reviewers also repeatedly call out limits to watch for, including lag with very large datasets, slower uploads or exports, and occasional responsiveness issues on heavy image workloads. In this category, buyers tend to compare how well platforms support organized dataset management, annotation review, and reliable throughput when image volume rises, rather than looking for raw processing speed alone.

### Which Data Labeling tools avoid performance issues and instability when processing large image batches

Based on G2 reviews, these products are commonly mentioned for managing larger image workloads with structured workflows.

- [SuperAnnotate](https://www.g2.com/products/superannotate) — large datasets with quality controls.
- [Roboflow](https://www.g2.com/products/roboflow) — image annotation and dataset versioning.
- [Taskmonk](https://www.g2.com/products/taskmonk) — scalable labeling with built-in QC.
- [Encord](https://www.g2.com/products/encord) — video and data pipeline workflows.

### What are the most important features in data labeling software

According to verified users, the most important features in data labeling software are annotation tools that match the data type, clear review and quality control workflows, dataset organization, and collaboration support. Recent G2 reviews also point to versioning, preprocessing or augmentation support, export flexibility, and AI-assisted labeling as recurring decision factors. For computer vision use cases, buyers often mention support for bounding boxes, polygons, segmentation, and video or point cloud workflows. Reviewers also care about how easily teams can move from labeling into model training or downstream pipelines. In practice, the best-fit platforms reduce manual effort while still helping teams maintain consistency, traceability, and clean handoffs.

### How do teams use Data Labeling for quality control

According to verified users, teams use data labeling for quality control by building review steps directly into annotation workflows. Recent G2 reviews describe practices such as peer review, verify-before-submit checkpoints, role-based task assignment, change tracking, and version control to catch mistakes early and improve consistency across annotators. Buyers also look for tools that centralize labeling, feedback, and project management so fewer issues slip through handoffs. In image-heavy workflows, users value features that help compare annotations, review edge cases, and maintain standards across large batches. The common theme is that quality control works best when it is embedded in the labeling process rather than handled as a separate cleanup step later.

### [Datasaur](https://www.g2.com/products/datasaur/reviews)

Datasaur offers the most intuitive interface for all your Natural Language Processing related tasks.

**Average Rating:** 4.5/5.0

**Total Reviews:** 77

#### How Do G2 Users Rate Datasaur?

- **Labeler Quality:** 9.2/10 (Category avg: 8.9/10)
- **Object Detection:** 8.4/10 (Category avg: 8.9/10)
- **Data Types:** 8.3/10 (Category avg: 8.8/10)
- **Ease of Use:** 9.3/10 (Category avg: 8.8/10)

#### Who Is the Company Behind Datasaur?

- **Seller:** [Datasaur](https://www.g2.com/sellers/datasaur)
- **Year Founded:** 2019
- **HQ Location:** San Francisco Bay Area, California
- **Twitter:** @datasaurai  
261 Twitter followers
- **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=07e24f5105db75a643ab99013e56cec5bb628429f73712ae498f72c63729a68a&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2Fdatasaur%2F&secure%5Burl_type%5D=linkedin_company_website)  
67 employees on LinkedIn®

#### Who Uses This Product?

- **Top Industries:** Information Technology and Services, Computer Software
- **Company Size:** 36% Medium, 35% Small

#### What Are Recent G2 Reviews of Datasaur?

**["Centralized Project Oversight for Data Labeling Workflows"](https://www.g2.com/survey_responses/datasaur-review-13366988)**

**Rating:** 4.0/5.0 stars

_— Yash R._

[Read full review](https://www.g2.com/survey_responses/datasaur-review-13366988)

**["Well-Defined Annotation Workflow with Handy Bulk Labeling"](https://www.g2.com/survey_responses/datasaur-review-13367754)**

**Rating:** 4.0/5.0 stars

_— Balaji S._

[Read full review](https://www.g2.com/survey_responses/datasaur-review-13367754)

#### What Are G2 Users Discussing About Datasaur?

- [What is Datasaur used for?](https://www.g2.com/discussions/what-is-datasaur-used-for) - 1 comment, 1 upvote

### [Amazon Sagemaker Ground Truth](https://www.g2.com/de/products/amazon-sagemaker-ground-truth/reviews)

Amazon SageMaker Ground Truth hilft Ihnen, hochgenaue Trainingsdatensätze für maschinelles Lernen schnell zu erstellen. SageMaker Ground Truth bietet einfachen Zugang zu öffentlichen und privaten menschlichen Labelern und stellt ihnen integrierte Workflows und Schnittstellen für gängige Kennzeichnungsaufgaben zur Verfügung.

**Average Rating:** 4.1/5.0

**Total Reviews:** 19

#### How Do G2 Users Rate Amazon Sagemaker Ground Truth?

- **Qualität des Etikettierers:** 10.0/10 (Category avg: 8.9/10)
- **Objekt-Erkennung:** 10.0/10 (Category avg: 8.9/10)
- **Datentypen:** 10.0/10 (Category avg: 8.8/10)
- **Einfache Bedienung:** 8.3/10 (Category avg: 8.8/10)

#### Who Is the Company Behind Amazon Sagemaker Ground Truth?

- **Verkäufer:** [Amazon Web Services (AWS)](https://www.g2.com/de/sellers/amazon-web-services-aws-3e93cc28-2e9b-4961-b258-c6ce0feec7dd)
- **Gründungsjahr:** 2006
- **Hauptsitz:** Seattle, WA
- **Twitter:** @awscloud  
2,232,483 Twitter-Follower
- **LinkedIn®-Seite:** [www.linkedin.com](https://www.g2.com/de/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=072881eee28a2afe24f8d1bda9f20e3e146b9fb4b214f216411ce2ed6898b31e&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2Famazon-web-services%2F&secure%5Burl_type%5D=linkedin_company_website)  
147,094 Mitarbeiter\*innen auf LinkedIn®
- **Eigentum:** NASDAQ: AMZN

#### Who Uses This Product?

- **Top Industries:** Informationstechnologie und Dienstleistungen
- **Company Size:** 37% Large, 37% Small

#### What Are Recent G2 Reviews of Amazon Sagemaker Ground Truth?

**["Großartig für Agile-Liebhaber."](https://www.g2.com/de/survey_responses/amazon-sagemaker-ground-truth-review-5146806)**

**Rating:** 4.0/5.0 stars

_— Verifizierter Benutzer in Computersoftware_

[Read full review](https://www.g2.com/de/survey_responses/amazon-sagemaker-ground-truth-review-5146806)

**["Der beste vollständig verwaltete Datenkennzeichnungsdienst aller Zeiten"](https://www.g2.com/de/survey_responses/amazon-sagemaker-ground-truth-review-4971883)**

**Rating:** 5.0/5.0 stars

_— Vithushan S._

[Read full review](https://www.g2.com/de/survey_responses/amazon-sagemaker-ground-truth-review-4971883)

#### What Are G2 Users Discussing About Amazon Sagemaker Ground Truth?

- [Which of these types of workforce are available in Amazon SageMaker ground truth?](https://www.g2.com/de/discussions/which-of-these-types-of-workforce-are-available-in-amazon-sagemaker-ground-truth)
- [How much does ground truth cost?](https://www.g2.com/de/discussions/how-much-does-ground-truth-cost)
- [Which type of data are included in Amazon SageMaker ground truth manifest file?](https://www.g2.com/de/discussions/which-type-of-data-are-included-in-amazon-sagemaker-ground-truth-manifest-file)

### [Labelbox](https://www.g2.com/de/products/labelbox/reviews)

Labelbox ist die führende datenzentrierte KI-Plattform für den Aufbau intelligenter Anwendungen. Teams, die die neuesten Fortschritte in generativer KI und LLMs nutzen möchten, verwenden die Labelbox-Plattform, um diese Systeme mit dem richtigen Maß an menschlicher Aufsicht und Automatisierung auszustatten. Egal, ob sie KI-Produkte mit benutzerdefinierten oder grundlegenden Modellen entwickeln oder KI zur Automatisierung von Datenaufgaben oder zur Gewinnung von Geschäftseinblicken einsetzen, Labelbox ermöglicht es Teams, dies effektiv und schnell zu tun. Die Plattform wird von Fortune-500-Unternehmen wie Walmart, P&G, Genentech und Adobe sowie von Hunderten führender KI-Teams genutzt. Labelbox wird von führenden Investoren unterstützt, darunter SoftBank, Andreessen Horowitz, B Capital, Gradient Ventures (Googles KI-fokussierter Fonds) und Databricks Ventures.

**Average Rating:** 4.5/5.0

**Total Reviews:** 48

#### How Do G2 Users Rate Labelbox?

- **Qualität des Etikettierers:** 9.1/10 (Category avg: 8.9/10)
- **Objekt-Erkennung:** 8.6/10 (Category avg: 8.9/10)
- **Datentypen:** 8.8/10 (Category avg: 8.8/10)
- **Einfache Bedienung:** 9.0/10 (Category avg: 8.8/10)

#### Who Is the Company Behind Labelbox?

- **Verkäufer:** [Labelbox](https://www.g2.com/de/sellers/labelbox)
- **Gründungsjahr:** 2018
- **Hauptsitz:** San Francisco, California
- **Twitter:** @labelbox  
3,489 Twitter-Follower
- **LinkedIn®-Seite:** [www.linkedin.com](https://www.g2.com/de/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=46b35e20f0b8a1bbee233e1f6d5a6d1a72e4e49ac4f1901248ae276388e42159&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2Flabelbox%2F&secure%5Burl_type%5D=linkedin_company_website)  
469 Mitarbeiter\*innen auf LinkedIn®

#### Who Uses This Product?

- **Top Industries:** Computersoftware, Informationstechnologie und Dienstleistungen
- **Company Size:** 46% Small, 38% Medium

#### What Do G2 Reviewers Say About Labelbox?

_AI-generated summary from verified user reviews_

##### Pros

- Benutzer finden Labelbox äußerst **einfach zu bedienen** , was das Projektmanagement vereinfacht und die Produktivität für das Training von Daten verbessert.
- Benutzer schätzen den **einfachen und effizienten Datenkennzeichnungsprozess** von Labelbox, der den Arbeitsablauf und die Modellgenauigkeit verbessert.
- Benutzer schätzen die **Effizienz** von Labelbox und loben die reibungslose Einrichtung und nahtlose Projektmanagement-Fähigkeiten.
- Benutzer schätzen die **KI-Fähigkeiten** von Labelbox, die die Datenkennzeichnung vereinfachen und die Modellgenauigkeit mühelos verbessern.
- Benutzer finden **einfache Integrationen** in Labelbox vorteilhaft, da sie eine nahtlose Einrichtung und benutzerfreundliche Erfahrung für alle ermöglichen.

##### Cons

- Benutzer äußern Frustration über den **Mangel an Funktionen** und verweisen auf begrenzte Aufgabenansprüche und minimale Anpassungsoptionen.
- Benutzer berichten von **langsamer Leistung** beim Umgang mit großen Datensätzen, was die Effizienz und das Benutzererlebnis erheblich beeinträchtigt.
- Benutzer finden, dass Labelbox eine **schwierige Lernkurve** hat, aufgrund seiner Komplexität und der langsameren Verarbeitung bei großen Datensätzen.
- Benutzer finden die **hohen Kosten** abschreckend, insbesondere Kleinanwender, die trotz großartiger Funktionen mit der Erschwinglichkeit zu kämpfen haben könnten.
- Benutzer erleben **langsame Verarbeitung** mit Labelbox, was zu langen Wartezeiten bei der Projektinitiierung und -abwicklung führt.

#### What Are Recent G2 Reviews of Labelbox?

**["Professionelle Benutzeroberfläche, einfache Einrichtung, benötigt Datenaktualisierung"](https://www.g2.com/de/survey_responses/labelbox-review-12625977)**

**Rating:** 4.0/5.0 stars

_— Ashish S._

[Read full review](https://www.g2.com/de/survey_responses/labelbox-review-12625977)

**["LLM-Training vom Feinsten!"](https://www.g2.com/de/survey_responses/labelbox-review-11265400)**

**Rating:** 4.5/5.0 stars

_— Staci T._

[Read full review](https://www.g2.com/de/survey_responses/labelbox-review-11265400)

#### What Are G2 Users Discussing About Labelbox?

- [How do I create a labeled dataset?](https://www.g2.com/de/discussions/how-do-i-create-a-labeled-dataset)
- [What is label tool?](https://www.g2.com/de/discussions/what-is-label-tool)
- [How do I download Labelbox?](https://www.g2.com/de/discussions/how-do-i-download-labelbox) - 2 comments
- [Is Labelbox open source?](https://www.g2.com/de/discussions/is-labelbox-open-source)

### [V7 Darwin](https://www.g2.com/de/products/v7-darwin/reviews)

V7 Darwin ist eine spezialisierte KI-Plattform zur Erstellung hochwertiger Trainingsdaten und zur Verwaltung von Annotations-Workflows. Sie ist für Teams konzipiert, die anspruchsvolle Computer-Vision-Modelle entwickeln und komplexe, domänenspezifische Herausforderungen mit KI lösen. V7 Darwin bietet eine umfassende Suite von Werkzeugen für die Datenbeschriftung, Videoannotation und medizinische Bildannotation. - Erstellen Sie pixelgenaue Bild- und Videoannotationen mit Auto-Annotate und SAM für semantische Masken, Instanzsegmentierung, Schlüsselpunkte und Polygone. - Entwickeln Sie medizinische KI mit Werkzeugen für DICOM-, NIfTI- und WSI-Annotationen, die eine Schnittstelle mit MPR, 3D-Rendering, präzisen Fadenkreuzen, Fensterung und schrägen Ansichten bieten. - Beschleunigen Sie die Videoannotation um bis zu 10x mit KI-unterstütztem Auto-Tracking für Objekte über mehrere Frames hinweg. - Verwalten Sie lange Videos, Multi-Kamera-Ansichten und verschachtelte Annotationsklassen. - Entwerfen Sie mehrstufige Überprüfungs-Workflows mit bedingter Logik, Konsens und Aufgabenverteilung für Ihre Datenbeschriftungspipeline. - Organisieren, filtern und verwalten Sie große Datensätze mit benutzerdefinierten Ansichten und Tags, die eine Echtzeit-Teamzusammenarbeit für Annotatoren, Prüfer und ML-Ingenieure ermöglichen. - Skalieren Sie Ihre Annotationsprojekte mit professionellen Datenbeschriftungsdiensten, einschließlich zertifizierter Annotatoren und Experten in verschiedenen Bereichen (medizinisch, Video, LLMs, wissenschaftlich). Sie können V7 Darwin nahtlos in Ihren bestehenden Tech-Stack integrieren und Annotationen mühelos importieren/exportieren. Erhalten Sie vollständige Kontrolle über Ihre Modelle, Aufgaben und Datensätze über die offene API, das Darwin-py SDK und die CLI.

**Average Rating:** 4.7/5.0

**Total Reviews:** 55

#### How Do G2 Users Rate V7 Darwin?

- **Qualität des Etikettierers:** 9.4/10 (Category avg: 8.9/10)
- **Objekt-Erkennung:** 9.4/10 (Category avg: 8.9/10)
- **Datentypen:** 9.2/10 (Category avg: 8.8/10)
- **Einfache Bedienung:** 9.5/10 (Category avg: 8.8/10)

#### Who Is the Company Behind V7 Darwin?

- **Verkäufer:** [V7](https://www.g2.com/de/sellers/v7)
- **Gründungsjahr:** 2018
- **Hauptsitz:** London, England
- **Twitter:** @v7labs  
3,471 Twitter-Follower
- **LinkedIn®-Seite:** [www.linkedin.com](https://www.g2.com/de/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=de877e1b154c6b0c137e7416f2b5d21ab1d53e5f5fc06f8706ec44d415a22436&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2Fv7labs%2F&secure%5Burl_type%5D=linkedin_company_website)  
106 Mitarbeiter\*innen auf LinkedIn®

#### Who Uses This Product?

- **Top Industries:** Informationstechnologie und Dienstleistungen, Computersoftware
- **Company Size:** 55% Small, 35% Medium

#### What Do G2 Reviewers Say About V7 Darwin?

_AI-generated summary from verified user reviews_

##### Pros

- Benutzer bewundern die **Benutzerfreundlichkeit** von V7 Darwin und finden es intuitiv und effizient für die Verwaltung von HR-Aufgaben.
- Benutzer schätzen die **Annotationseffizienz** von V7 Darwin, die sich wiederholende Aufgaben rationalisiert und die Genauigkeit in Prozessen verbessert.
- Benutzer loben die **benutzerfreundlichen Anmerkungswerkzeuge** von V7 Darwin, die die Projekteffizienz und Qualitätskontrolle verbessern.
- Benutzer loben die **intuitive Benutzeroberfläche und umfassenden Funktionen** von V7 Darwin, die das HR-Management und die Effizienz verbessern.
- Benutzer schätzen die **Effizienz** von V7 Darwin, die Aufgaben vereinfacht und die Produktivität im HR-Management steigert.

##### Cons

- Benutzer finden den **Mangel an Funktionen** in V7 Darwin, wie Dateimanipulation und intuitive Navigation, enttäuschend.
- Benutzer wünschen sich **fehlende Funktionen** in V7 Darwin, wie z.B. Polygonmanipulation und verbesserte Exportoptionen für Datensätze.
- Benutzer finden die **begrenzten Funktionen** von V7 Darwin frustrierend, insbesondere bei Anmerkungen und den Fähigkeiten zur Datenverwaltung.
- Benutzer erleben **Annotierungsprobleme** , einschließlich der Unfähigkeit, Einreichungen zurückzuziehen, und fehlender Abkürzungen für die Genehmigung.
- Benutzer finden die **schwierige Navigation** von V7 Darwin herausfordernd, da sie Zeit benötigen, um sich an die komplexe Benutzeroberfläche anzupassen.

#### What Are Recent G2 Reviews of V7 Darwin?

**["Einfache Videoannotation und prädiktive Kennzeichnung für massive Datensätze"](https://www.g2.com/de/survey_responses/v7-darwin-review-12700843)**

**Rating:** 4.5/5.0 stars

_— Jed D._

[Read full review](https://www.g2.com/de/survey_responses/v7-darwin-review-12700843)

**["Umfassendes HRMS für das End-to-End-Management des Mitarbeiterlebenszyklus"](https://www.g2.com/de/survey_responses/v7-darwin-review-11727041)**

**Rating:** 4.0/5.0 stars

_— Shiv S._

[Read full review](https://www.g2.com/de/survey_responses/v7-darwin-review-11727041)

#### What Are G2 Users Discussing About V7 Darwin?

- [Wofür wird V7 verwendet?](https://www.g2.com/de/discussions/what-is-v7-used-for)

### [Sama](https://www.g2.com/de/products/sama/reviews)

Sama ist ein weltweit anerkannter Marktführer in der Datenannotationslösungen für Unternehmens-Computer Vision und generative KI-Modelle, die höchste Genauigkeit erfordern. Als Branchenpionier mit 15 Jahren Erfahrung werden Samas Fachwissen und Lösungen von führenden Unternehmen wie GM, Ford, Continental, Google und vielen anderen vertraut. Sama ist auf Datenannotationsdienste für generative KI sowie 2D- und 3D-Bild- und Videoverarbeitung (einschließlich LiDAR und Sensorfusion) spezialisiert. Wir validieren auch komplexe maschinelle Lernalgorithmen. Als Vorreiter in ethischer KI und zertifiziertes B-Corp haben wir ein Wirkungsmodell entwickelt, das die Kraft der Märkte für das soziale Wohl nutzt. Wir haben die Beschäftigungs- und Einkommensmöglichkeiten für diejenigen, die die größten Hürden für formelle Arbeit haben, erheblich verbessert (validiert durch eine unabhängige MIT-Studie). Bisher haben wir mehr als 60.000 Menschen geholfen, sich aus der Armut zu befreien.

**Average Rating:** 4.6/5.0

**Total Reviews:** 11

#### How Do G2 Users Rate Sama?

- **Qualität des Etikettierers:** 9.0/10 (Category avg: 8.9/10)
- **Objekt-Erkennung:** 9.6/10 (Category avg: 8.9/10)
- **Datentypen:** 9.6/10 (Category avg: 8.8/10)
- **Einfache Bedienung:** 9.2/10 (Category avg: 8.8/10)

#### Who Is the Company Behind Sama?

- **Verkäufer:** [Sama](https://www.g2.com/de/sellers/sama)
- **Gründungsjahr:** 2008
- **Hauptsitz:** San Francisco, US
- **Twitter:** @SamaAI  
228,871 Twitter-Follower
- **LinkedIn®-Seite:** [www.linkedin.com](https://www.g2.com/de/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=05e2827df365f003c18aadac7b3a03b2ddcf89a9cc0237cf0288b460cca1fcfb&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2F410136&secure%5Burl_type%5D=linkedin_company_website)  
4,349 Mitarbeiter\*innen auf LinkedIn®

#### Who Uses This Product?

- **Company Size:** 55% Small, 36% Large

#### What Do G2 Reviewers Say About Sama?

_AI-generated summary from verified user reviews_

##### Pros

- Benutzer schätzen die **erstaunlichen Datenvorbereitungs- und Anreicherungsfunktionen** von Sama, die ihr Analyseerlebnis verbessern.
- Benutzer schätzen die **24/7-Kundensupport** -Dienste von Sama, die ihr Erlebnis mit Tutorials und Demos verbessern.
- Benutzer schätzen die **Datenkatalogisierungsfunktionen** von Sama, die das Datenmanagement verbessern und schnelle Einblicke bieten.
- Benutzer schätzen die **erstaunlichen Datenvorbereitungs- und Anreicherungsfunktionen** von Sama, die ihre Analysefähigkeiten erheblich verbessern.
- Benutzer schätzen die **fortschrittlichen Datenverarbeitungs- und Anreicherungsfunktionen** von Sama, die ihre Analysefähigkeiten erheblich verbessern.

##### Cons

- Benutzer finden, dass die **Komplexität** von Sama umfangreiches Training und qualifiziertes Personal erfordert, was die Benutzerfreundlichkeit erschwert.
- Benutzer finden, dass die **komplexe Einrichtung** von Sama umfangreiche Schulung oder qualifiziertes Personal für einen effektiven Betrieb erfordert.
- Benutzer finden, dass die Komplexität des Produkts einen **Mangel an Schulung** verursacht, was qualifiziertes Personal für einen effektiven Betrieb erforderlich macht.
- Benutzer finden, dass das Produkt **umfangreiche Schulung** erfordert, was es schwierig macht, ohne geschultes Personal zu bedienen.

#### What Are Recent G2 Reviews of Sama?

**["Beeindruckende Genauigkeit bei ihren Datenanmerkungen"](https://www.g2.com/de/survey_responses/sama-review-9935840)**

**Rating:** 4.5/5.0 stars

_— Nikita D._

[Read full review](https://www.g2.com/de/survey_responses/sama-review-9935840)

**["Übersetzt zu besserer Modellleistung"](https://www.g2.com/de/survey_responses/sama-review-9669978)**

**Rating:** 4.5/5.0 stars

_— Mohammad A._

[Read full review](https://www.g2.com/de/survey_responses/sama-review-9669978)

### [SUPA](https://www.g2.com/de/products/supa/reviews)

SUPA ist hier, um Ihnen zu helfen, Ihre Daten in jeder Phase zu optimieren: Sammlung, Kuratierung, Annotation, Modellvalidierung und menschliches Feedback. SUPA wird von KI-Teams vertraut, um ihre menschlichen Datenanforderungen zu lösen. Unsere blitzschnelle, maschinengesteuerte Kennzeichnungsplattform integriert sich mit unserer vielfältigen Belegschaft, um qualitativ hochwertige Daten in großem Maßstab bereitzustellen, was sie zur kosteneffizientesten Lösung für Ihre KI macht.

**Average Rating:** 4.9/5.0

**Total Reviews:** 11

#### How Do G2 Users Rate SUPA?

- **Qualität des Etikettierers:** 8.8/10 (Category avg: 8.9/10)
- **Objekt-Erkennung:** 9.7/10 (Category avg: 8.9/10)
- **Datentypen:** 9.2/10 (Category avg: 8.8/10)
- **Einfache Bedienung:** 9.3/10 (Category avg: 8.8/10)

#### Who Is the Company Behind SUPA?

- **Verkäufer:** [TDCX](https://www.g2.com/de/sellers/tdcx)
- **Hauptsitz:** Singapore, SG
- **Twitter:** @tdcxgroup  
1,054 Twitter-Follower
- **LinkedIn®-Seite:** [www.linkedin.com](https://www.g2.com/de/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=f51abb55ad18cb507299afe0569be18815a9e2cb3a40380ff7f421113492cbb7&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2Ftdcxgroup%2F&secure%5Burl_type%5D=linkedin_company_website)  
15,664 Mitarbeiter\*innen auf LinkedIn®
- **Eigentum:** NYSE: TDCX

#### Who Uses This Product?

- **Company Size:** 45% Medium, 45% Small

#### What Are Recent G2 Reviews of SUPA?

**["Zuverlässig und eine großartige Wahl für die Datenkennzeichnung"](https://www.g2.com/de/survey_responses/supa-review-9744081)**

**Rating:** 5.0/5.0 stars

_— Verifizierter Benutzer in Design_

[Read full review](https://www.g2.com/de/survey_responses/supa-review-9744081)

**["Leiter der Daten"](https://www.g2.com/de/survey_responses/supa-review-8870147)**

**Rating:** 5.0/5.0 stars

_— Dominic C._

[Read full review](https://www.g2.com/de/survey_responses/supa-review-8870147)

### [Outlier AI](https://www.g2.com/de/products/outlier-ai-outlier-ai/reviews)

Outlier AI ist eine Plattform, die menschliche Expertise mit künstlicher Intelligenz verbindet, um die Genauigkeit, Geschwindigkeit und Zuverlässigkeit von KI-Modellen zu verbessern. Durch die Einbindung eines globalen Netzwerks von über 100.000 Experten in mehr als 50 Ländern hat Outlier AI die Entwicklung von wissensreicheren und wirkungsvolleren KI-Systemen erleichtert und über 500 Millionen Dollar an seine Mitwirkenden verteilt. Hauptmerkmale und Funktionalität: - Expertengetriebene KI-Schulung: Outlier AI nutzt die spezialisierten Fähigkeiten seiner globalen Expertengemeinschaft, um KI-Modelle zu trainieren und zu verfeinern, was eine hochwertige Datenannotation und Modellentwicklung sicherstellt. - Flexible Remote-Arbeitsmöglichkeiten: Die Plattform bietet Einzelpersonen sinnvolle und zugängliche Arbeitsmöglichkeiten, die es Experten ermöglichen, remote und nach ihrem eigenen Zeitplan beizutragen. - Integration mit Scale AI: Angetrieben von Scale AI kombiniert Outlier AI erstklassige Dateninfrastruktur mit fortschrittlichen Anomalieerkennungsmöglichkeiten, um die Skalierbarkeit und Genauigkeit von KI-Lösungen zu verbessern. Primärer Wert und gelöstes Problem: Outlier AI adressiert die Herausforderung, zuverlässige und effektive KI-Modelle zu entwickeln, indem es menschliche Expertise in den KI-Trainingsprozess integriert. Dieser Ansatz verbessert nicht nur die Qualität der KI-Ergebnisse, sondern bietet auch flexible Beschäftigungsmöglichkeiten für eine vielfältige globale Belegschaft. Durch die Überbrückung der Kluft zwischen menschlicher Intelligenz und künstlicher Intelligenz stellt Outlier AI sicher, dass KI-Systeme genauer, effizienter und besser auf reale Anwendungen abgestimmt sind.

**Average Rating:** 4.0/5.0

**Total Reviews:** 14

#### How Do G2 Users Rate Outlier AI?

- **Qualität des Etikettierers:** 9.6/10 (Category avg: 8.9/10)
- **Objekt-Erkennung:** 9.2/10 (Category avg: 8.9/10)
- **Datentypen:** 10.0/10 (Category avg: 8.8/10)
- **Einfache Bedienung:** 8.1/10 (Category avg: 8.8/10)

#### Who Is the Company Behind Outlier AI?

- **Verkäufer:** [Outlier AI](https://www.g2.com/de/sellers/outlier-ai)
- **Gründungsjahr:** 2023
- **Hauptsitz:** San Francisco, US
- **LinkedIn®-Seite:** [www.linkedin.com](https://www.g2.com/de/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=cd7fc401ed04daac65acf0d9fb4d1889c1db791ed38161618afccb1b10a73dfa&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2Ftry-outlier&secure%5Burl_type%5D=linkedin_company_website)  
28,516 Mitarbeiter\*innen auf LinkedIn®

#### Who Uses This Product?

- **Top Industries:** Informationstechnologie und Dienstleistungen
- **Company Size:** 100% Small

#### What Do G2 Reviewers Say About Outlier AI?

_AI-generated summary from verified user reviews_

##### Pros

- Benutzer schätzen den **hilfreichen Kundensupport** von Outlier AI, was ihre Gesamterfahrung und Zufriedenheit verbessert.
- Benutzer schätzen die **Datengenauigkeit** von Outlier AI und würdigen das Engagement für präzise und durchdachte Antworten.
- Benutzer schätzen die **flexiblen und remote Projektmöglichkeiten** , die Outlier AI für kompetenzbasiertes Freelancing bietet.
- Benutzer schätzen das **transparente und schnelle Zahlungssystem** von Outlier AI, was ihre allgemeine Zufriedenheit mit der Plattform erhöht.
- Benutzer loben die **schnelle Reaktionsgeschwindigkeit** von Outlier AI, was ihre allgemeine Benutzererfahrung und Zufriedenheit verbessert.

##### Cons

- Benutzer erleben frustrierende **Arbeitsunterbrechungen** aufgrund inkonsistenter Projektverfügbarkeit und schlechter Kommunikation seitens des Managements.
- Benutzer berichten von **fehlerhafter Leistung** , die unerwartete Projektentfernungen und schwankende Arbeitsverfügbarkeit verursacht und ihre Gesamterfahrung beeinträchtigt.
- Benutzer äußern Frustration über **niedrige Vergütung** und inkonsistente Projektverfügbarkeit, was ihre Gesamterfahrung mit Outlier AI beeinträchtigt.
- Benutzer finden, dass die **Leistungsprobleme** von Outlier AI die allgemeine Benutzererfahrung und Benutzerfreundlichkeit der Plattform beeinträchtigen.
- Benutzer stehen häufig vor **schlechtem Kundensupport** , was zu Verwirrung und Frustration bei abrupten Projektänderungen und der Kommunikation des Managements führt.

#### What Are Recent G2 Reviews of Outlier AI?

**["Ein Traum für Entwickler am Schnittpunkt der KI"](https://www.g2.com/de/survey_responses/outlier-ai-review-12834046)**

**Rating:** 4.5/5.0 stars

_— Verifizierter Benutzer in Computersoftware_

[Read full review](https://www.g2.com/de/survey_responses/outlier-ai-review-12834046)

**["Flexibler, nahtloser Arbeitsablauf für bedeutungsvolle KI-Projekte"](https://www.g2.com/de/survey_responses/outlier-ai-review-12904617)**

**Rating:** 4.0/5.0 stars

_— Verifizierter Benutzer in Informationstechnologie und Dienstleistungen_

[Read full review](https://www.g2.com/de/survey_responses/outlier-ai-review-12904617)

### [Clarifai](https://www.g2.com/de/products/clarifai/reviews)

Clarifai ist ein führendes Unternehmen in der KI-Orchestrierung und -Entwicklung, das Organisationen, Teams und Entwicklern hilft, KI in großem Maßstab zu erstellen, bereitzustellen, zu orchestrieren und zu operationalisieren. Clarifais hochmoderne KI-Workflow-Orchestrierungsplattform nutzt die modernen KI-Technologien von heute wie Large Language Models (LLMs), Large Vision Models (LVMs) und Retrieval Augmented Generation (RAG), Datenkennzeichnung, Inferenz und mehr und ist in Cloud-, On-Premises- oder Hybridumgebungen verfügbar. Gegründet im Jahr 2013, wurde Clarifai verwendet, um mehr als 1,5 Millionen KI-Modelle mit mehr als 400.000 Nutzern in 170 Ländern zu erstellen. Erfahren Sie mehr unter www.clarifai.com.

**Average Rating:** 4.3/5.0

**Total Reviews:** 74

#### How Do G2 Users Rate Clarifai?

- **Qualität des Etikettierers:** 8.3/10 (Category avg: 8.9/10)
- **Objekt-Erkennung:** 8.3/10 (Category avg: 8.9/10)
- **Datentypen:** 8.3/10 (Category avg: 8.8/10)
- **Einfache Bedienung:** 8.3/10 (Category avg: 8.8/10)

#### Who Is the Company Behind Clarifai?

- **Verkäufer:** [Clarifai](https://www.g2.com/de/sellers/clarifai)
- **Gründungsjahr:** 2013
- **Hauptsitz:** Wilmington, Delaware
- **Twitter:** @clarifai  
10,922 Twitter-Follower
- **LinkedIn®-Seite:** [www.linkedin.com](https://www.g2.com/de/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=8e96cd967f3d17ebfc636080f07d5b87c9155366576feb1d18188684d774aa32&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2F10064814%2F&secure%5Burl_type%5D=linkedin_company_website)  
49 Mitarbeiter\*innen auf LinkedIn®

#### Who Uses This Product?

- **Top Industries:** Computersoftware, Informationstechnologie und Dienstleistungen
- **Company Size:** 61% Small, 29% Medium

#### What Do G2 Reviewers Say About Clarifai?

_AI-generated summary from verified user reviews_

##### Pros

- Benutzer schätzen Clarifai für seine **beeindruckenden Modelle und Flexibilität** , die die Bild- und Videoerkennung effektiv verbessern.
- Benutzer schätzen die **einfachen KI-Tools** von Clarifai, die eine schnelle und genaue Bild- und Videoerkennung mühelos ermöglichen.
- Benutzer schätzen die **Modellvielfalt** in Clarifai, die maßgeschneiderte Lösungen mit beeindruckender Flexibilität und einfacher Integration ermöglicht.
- Benutzer schätzen die **fortschrittliche KI-Integration** von Clarifai, die durch anpassbare Modelle eine genaue Bild- und Videoerkennung ermöglicht.
- Benutzer schätzen die **fortschrittlichen KI-Fähigkeiten** von Clarifai, insbesondere für Aufgaben der Bild- und Videoerkennung.

##### Cons

- Benutzer finden die Plattform für **teuer** für Entwickler im kleinen Maßstab, da sich die Kosten bei der Nutzung schnell summieren können.
- Benutzer finden die **Komplexität der Einrichtung und Dokumentation** herausfordernd, insbesondere für Neulinge auf der Plattform.
- Benutzer finden die **Lernkurve steil** für Clarifai, insbesondere für diejenigen, die neu in maschinellen Lernplattformen sind.
- Benutzer stehen vor einem **Mangel an Ressourcen** , was die Zugänglichkeit für kleine Entwickler und gemeinnützige Organisationen, die nach erschwinglichen Optionen suchen, behindert.
- Benutzer finden die **schlechte Dokumentation** von Clarifai oft detailarm, was ihre Fähigkeit behindert, die Funktionen maximal zu nutzen.

#### What Are Recent G2 Reviews of Clarifai?

**["Clarifai's flexible Modell-Workflows und leistungsstarke multimodale Plattform"](https://www.g2.com/de/survey_responses/clarifai-review-13279085)**

**Rating:** 4.5/5.0 stars

_— Subhashree S._

[Read full review](https://www.g2.com/de/survey_responses/clarifai-review-13279085)

**["Leistungsstarke KI-Plattform für Computer Vision und multimodale Workflows"](https://www.g2.com/de/survey_responses/clarifai-review-13188111)**

**Rating:** 4.0/5.0 stars

_— Jeni J._

[Read full review](https://www.g2.com/de/survey_responses/clarifai-review-13188111)

### [Taskmonk](https://www.g2.com/de/products/taskmonk/reviews)

Taskmonk ist die Enterprise-AI-Trainingsdatenplattform für Teams, die Annotationen im Produktionsmaßstab benötigen, nicht im Pilotmaßstab. Es ist eine der wenigen Plattformen, die sechs Datenmodalitäten in einer einzigen Umgebung verarbeitet: Text, Bild, Audio, Video, LiDAR und DICOM, sodass Teams nicht separate Tools zusammenfügen müssen, sobald ein Projekt über die Standard-Computer-Vision hinausgeht. Entwickelt für Annotationsteams, Projektmanager und AI-Leiter gleichermaßen, kombiniert Taskmonk einen No-Code-Workflow-Builder mit AI-unterstütztem Labeling und QA, das im großen Maßstab funktioniert: • No-Code/Low-Code-Workflows, die sich ohne Ingenieurzeit an jedes Projekt anpassen • AI-unterstütztes Labeling und modellunterstütztes Vor-Labeling, das die Annotierungsstunden reduziert und den Durchsatz erhöht • Mehrstufige QA wie Gold-Sets, Konsens, Schlichtung sowie affinitätsbasierte Aufgabenverteilung nach der Leistung der Annotatoren, nicht nur nach Rolle • SOC 2 Typ II und ISO 27001 Konformität, mit HIPAA und Datenresidenz für regulierte Branchen verfügbar Taskmonk hat über 480 Millionen Annotierungsaufgaben und über 6 Millionen Labeling-Stunden für mehr als 10 Fortune-500-Kunden, darunter Flipkart, Myntra und LG, verarbeitet und den Kunden bisher über 10 Millionen Dollar eingespart. Teams können auch eine geprüfte, SLA-gestützte Annotierungsarbeitskraft auf derselben Plattform einbinden, sodass es nur einen Vertrag und einen Eskalationsweg gibt, anstatt das Labeling-Talent separat zu beschaffen.

**Average Rating:** 4.6/5.0

**Total Reviews:** 17

#### How Do G2 Users Rate Taskmonk?

- **Qualität des Etikettierers:** 9.3/10 (Category avg: 8.9/10)
- **Objekt-Erkennung:** 9.2/10 (Category avg: 8.9/10)
- **Datentypen:** 9.7/10 (Category avg: 8.8/10)
- **Einfache Bedienung:** 9.2/10 (Category avg: 8.8/10)

#### Who Is the Company Behind Taskmonk?

- **Verkäufer:** [Taskmonk](https://www.g2.com/de/sellers/taskmonk)
- **Gründungsjahr:** 2018
- **Hauptsitz:** Bengaluru, Karnataka, India
- **Twitter:** @TaskmonkAI  
16 Twitter-Follower
- **LinkedIn®-Seite:** [www.linkedin.com](https://www.g2.com/de/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=1d6fc148b17fe971252bbfff900d04da0cb064fa240b87046de8c9b9bcd442f6&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2Ftaskmonk%2F&secure%5Burl_type%5D=linkedin_company_website)  
28 Mitarbeiter\*innen auf LinkedIn®

#### Who Uses This Product?

- **Top Industries:** Informationstechnologie und Dienstleistungen
- **Company Size:** 72% Small, 22% Large

#### What Do G2 Reviewers Say About Taskmonk?

_AI-generated summary from verified user reviews_

##### Pros

- Benutzer heben die **Benutzerfreundlichkeit** von Taskmonk hervor und loben die intuitive Benutzeroberfläche und das schnelle Onboarding für Anfänger.
- Benutzer loben den **außergewöhnlichen Kundensupport** von Taskmonk und heben ihre Reaktionsfähigkeit und ihr Engagement für den Erfolg der Benutzer hervor.
- Benutzer loben Taskmonk für seine **Effizienz** , die eine schnelle Einarbeitung und nahtlose Bearbeitung von Projekten zur Datenannotation mit hohem Volumen ermöglicht.
- Benutzer loben Taskmonk für seine **intuitive Benutzeroberfläche und robusten Funktionen** , die das Labeln von großen Datenmengen mühelos und effizient machen.
- Benutzer heben das **schnelle Setup und Onboarding** von Taskmonk hervor, was es einfach macht, die Plattform schnell zu nutzen.

##### Cons

- Benutzer finden, dass Taskmonk einen **Mangel an Funktionen** hat, insbesondere in Bezug auf Berichterstattung und Flexibilität der Benutzeroberfläche.
- Benutzer finden die **schwierige Lernkurve** herausfordernd für Anfänger, was auf die Notwendigkeit einer benutzerfreundlicheren Oberfläche hinweist.
- Benutzer finden die **Komplexität der Benutzeroberfläche** abschreckend, insbesondere Neulinge, aufgrund der überwältigenden Anzahl sichtbarer Optionen.
- Benutzer erleben **gelegentliche technische Schwierigkeiten** , obwohl schneller Support und Lösungen die allgemeine Zufriedenheit mit Taskmonk erhöhen.
- Benutzer haben **Upload-Probleme** aufgrund von Verzögerungen während der Stoßzeiten und begrenzten lokalen Optionen für LiDAR-Projekte.

#### What Are Recent G2 Reviews of Taskmonk?

**["Beeindruckendes Produkt!"](https://www.g2.com/de/survey_responses/taskmonk-review-11562637)**

**Rating:** 4.0/5.0 stars

_— Aditya N._

[Read full review](https://www.g2.com/de/survey_responses/taskmonk-review-11562637)

**["Ein anfängerfreundliches, schnelles und hochgradig anpassbares Werkzeug für jede Art von Prozess."](https://www.g2.com/de/survey_responses/taskmonk-review-11752427)**

**Rating:** 5.0/5.0 stars

_— JAYAPRAKASH K._

[Read full review](https://www.g2.com/de/survey_responses/taskmonk-review-11752427)

### [Keymakr](https://www.g2.com/de/products/keymakr/reviews)

Wir sind ein Datenkennzeichnungsunternehmen, das sich auf die Bereitstellung hochwertiger Annotationsdienste und exzellenten Kundensupport konzentriert. Wir sind die beste Wahl für: Bildannotation Videoannotation Datenvalidierung Dokumentenannotation Datenerstellung Datensammlung Unser Unternehmen erstellt erstklassige Trainingsdaten für Computer Vision. Wir bieten ein internes Team in Kombination mit fortschrittlichen, proprietären Annotationswerkzeugen. Skalierbare und sichere All-in-One-Lösung für Ihre KI

**Average Rating:** 4.8/5.0

**Total Reviews:** 45

#### How Do G2 Users Rate Keymakr?

- **Qualität des Etikettierers:** 9.4/10 (Category avg: 8.9/10)
- **Objekt-Erkennung:** 9.7/10 (Category avg: 8.9/10)
- **Datentypen:** 9.7/10 (Category avg: 8.8/10)
- **Einfache Bedienung:** 9.2/10 (Category avg: 8.8/10)

#### Who Is the Company Behind Keymakr?

- **Verkäufer:** [Keymakr](https://www.g2.com/de/sellers/keymakr)
- **Gründungsjahr:** 2015
- **Hauptsitz:** New York, NY
- **Twitter:** @keymakr\_com  
354 Twitter-Follower
- **LinkedIn®-Seite:** [www.linkedin.com](https://www.g2.com/de/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=5fb808098ef63b965158417815d66f2ac2810beadbf8235c0f26f8875a0b72ae&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2Fkeymakr%2F&secure%5Burl_type%5D=linkedin_company_website)  
69 Mitarbeiter\*innen auf LinkedIn®

#### Who Uses This Product?

- **Top Industries:** Computersoftware
- **Company Size:** 52% Small, 22% Medium

#### What Do G2 Reviewers Say About Keymakr?

_AI-generated summary from verified user reviews_

##### Pros

- Benutzer loben den **ausgezeichneten Kundensupport** und die Reaktionsfähigkeit von Keymakr, was ihre Gesamterfahrung mit der Software verbessert.
- Benutzer schätzen die **hochwertigen Anmerkungen** von Keymakr und loben deren Genauigkeit und Detailgenauigkeit bei verschiedenen Bildern.
- Benutzer schätzen die **effiziente Workflow-Integration** von Keymakr, die Prozesse vereinfacht und die Gesamtproduktivität erheblich steigert.
- Benutzer loben Keymakr für seine **Annotationseffizienz** und schätzen die schnellen Dienstleistungen und optimierten Arbeitsabläufe, die die Produktivität steigern.
- Benutzer schätzen den **außergewöhnlichen Kundenservice** von Keymakr und heben ihren proaktiven Ansatz sowie schnelle und präzise Antworten auf Probleme hervor.

##### Cons

- Benutzer berichten von **wiederkehrenden Annotationsfehlern** und Schwierigkeiten beim Exportieren von COCO-Format-Labels, obwohl die Probleme schließlich gelöst wurden.
- Benutzer finden die **schwierige Einrichtung** herausfordernd, wobei anfängliche Anpassungen und verwirrende UI-Navigation Frustration verursachen.
- Benutzer finden die **UI-Navigation verworren** , was die anfängliche Einrichtung und Verbindungen, wie zu S3, herausfordernd macht.
- Benutzer finden die **begrenzten Anpassungsmöglichkeiten** in Keymakr frustrierend, insbesondere in Bezug auf Passwortänderungen.

#### What Are Recent G2 Reviews of Keymakr?

**["Ein wahrer Partner in der Bilderkennung"](https://www.g2.com/de/survey_responses/keymakr-review-11968704)**

**Rating:** 5.0/5.0 stars

_— Yacine B._

[Read full review](https://www.g2.com/de/survey_responses/keymakr-review-11968704)

**["Genaue Annotation, wertvolle Ergebnisse"](https://www.g2.com/de/survey_responses/keymakr-review-11103169)**

**Rating:** 4.5/5.0 stars

_— Rinat L._

[Read full review](https://www.g2.com/de/survey_responses/keymakr-review-11103169)

### [Appen](https://www.g2.com/de/products/appen/reviews)

Appen sammelt und kennzeichnet Bilder, Text, Sprache, Audio, Video und andere Daten, um Trainingsdaten zu erstellen, die zur Entwicklung und kontinuierlichen Verbesserung der innovativsten KI-Systeme der Welt verwendet werden. Wir bieten eine hochmoderne, lizenzierbare Datenannotationsplattform an, um Anwendungsfälle für Trainingsdaten in der Computer Vision und der Verarbeitung natürlicher Sprache zu annotieren. Unsere Plattform verbessert die Genauigkeit und Effizienz durch unsere Smart Labeling- und Pre-Labeling-Funktionen, die maschinelles Lernen nutzen, um menschliche Annotationen zu erleichtern. Sie wählen das gewünschte Niveau an Service und Sicherheit für die Datensammlung und Annotation, von einem umfassenden Managed Service bis hin zu einem flexiblen Self-Service. Unsere Expertise umfasst eine globale Crowd von über 1 Million qualifizierten Auftragnehmern, die über 235 Sprachen und Dialekte sprechen, in über 70.000 Standorten und 170 Ländern, sowie die fortschrittlichste KI-unterstützte Datenannotationsplattform der Branche. Unsere zuverlässigen Trainingsdaten geben Führungskräften in Technologie, Automobilindustrie, Finanzdienstleistungen, Einzelhandel, Gesundheitswesen und Regierungen das Vertrauen, erstklassige KI-Produkte einzusetzen. Gegründet im Jahr 1996, hat Appen Kunden und Büros weltweit.

**Average Rating:** 4.2/5.0

**Total Reviews:** 33

#### How Do G2 Users Rate Appen?

- **Qualität des Etikettierers:** 8.5/10 (Category avg: 8.9/10)
- **Objekt-Erkennung:** 8.7/10 (Category avg: 8.9/10)
- **Datentypen:** 8.8/10 (Category avg: 8.8/10)
- **Einfache Bedienung:** 8.2/10 (Category avg: 8.8/10)

#### Who Is the Company Behind Appen?

- **Verkäufer:** [Appen](https://www.g2.com/de/sellers/appen)
- **Gründungsjahr:** 1996
- **Hauptsitz:** Kirkland, Washington, United States
- **LinkedIn®-Seite:** [www.linkedin.com](https://www.g2.com/de/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=b0931c0a7f2c53197a2b59e80de4a7fdfa27c61180c1a3b6c615dca728909c73&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2Fappen&secure%5Burl_type%5D=linkedin_company_website)  
20,647 Mitarbeiter\*innen auf LinkedIn®
- **Eigentum:** ASX:APX
- **Gesamterlös (USD Mio):** $244,900

#### Who Uses This Product?

- **Top Industries:** Informationstechnologie und Dienstleistungen
- **Company Size:** 54% Small, 26% Medium

#### What Do G2 Reviewers Say About Appen?

_AI-generated summary from verified user reviews_

##### Pros

- Benutzer schätzen die **Flexibilität und ansprechende Vielfalt** der Aufgaben auf Appen, was ihre Arbeitserfahrung und Freude verbessert.
- Benutzer schätzen die **Benutzerfreundlichkeit** von Appen, die es ermöglicht, Aufgaben bequem über ihre Handys zu erledigen.
- Benutzer schätzen die **Flexibilität** von Appen, die es ihnen ermöglicht, sich an vielfältigen und interessanten Projekten zu beteiligen.

##### Cons

- Benutzer erleben häufige **Arbeitsunterbrechungen** aufgrund inkonsistenter Projektverfügbarkeit und Navigationsprobleme, was die Einkommenszuverlässigkeit beeinträchtigt.
- Benutzer finden, dass die **niedrige Vergütung** und die inkonsistente Verfügbarkeit von Arbeit Appen für ein stabiles Einkommen unzuverlässig machen.
- Benutzer finden die **Navigation verwirrend** und erleben häufige Abmeldungen, was ihre Nutzbarkeit von Appen beeinträchtigt.
- Benutzer erleben oft **Verbindungsprobleme** mit Appen, was zu Verwirrung und häufigen Verbindungsabbrüchen führt.
- Benutzer finden die **Navigations verwirrend** und melden häufige Verbindungsabbrüche, die ihre Gesamterfahrung mit Appen beeinträchtigen.

#### What Are Recent G2 Reviews of Appen?

**["Ideal für Freiberufler, Einfachheit mit Raum für Verbesserungen im Support"](https://www.g2.com/de/survey_responses/appen-review-12550258)**

**Rating:** 5.0/5.0 stars

_— Ashish S._

[Read full review](https://www.g2.com/de/survey_responses/appen-review-12550258)

**["Robuste Crowdsourcing-Plattform für KI- und Sprachaufgaben"](https://www.g2.com/de/survey_responses/appen-review-12769449)**

**Rating:** 4.0/5.0 stars

_— Sina A._

[Read full review](https://www.g2.com/de/survey_responses/appen-review-12769449)

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 ![Bijou Barry](/assets/transparent-ad5be28fbcd25b7b08d2cebe1d957125437fb5407d75ee717965ad22c8808791.gif "Bijou Barry")
BB

Researched and written by [Bijou Barry](https://research.g2.com/insights/author/bijou-barry)

Updated April 9, 2026

Data labeling software helps data science and machine learning teams source, manage, annotate, and classify unstructured data, including text, images, videos, audio, and PDFs, into labeled datasets that create efficient training data pipelines for building and improving AI and ML models.

### Core Capabilities of Data Labeling Software

To qualify for inclusion in the Data Labeling category, a product must:

- Integrate a managed workforce and/or data labeling service
- Ensure labels are accurate and consistent
- Give the user the ability to view analytics that monitor the accuracy and speed of labeling
- Allow annotated data to be integrated into data science and machine learning platforms to build machine learning models

### Common Use Cases for Data Labeling Software

ML engineers, data scientists, and AI teams use data labeling tools to build high-quality training datasets across a wide range of application types. Common use cases include:

- Annotating images, video, and text for computer vision, NLP, and speech recognition model training
- Fine-tuning and evaluating large language models (LLMs) with human-labeled feedback data
- Building training pipelines for object detection, named entity recognition, and sentiment analysis applications

### How Data Labeling Software Differs from Other Tools

Data labeling is a foundational building block of the AI development lifecycle, distinct from the downstream tools it feeds. It integrates with [generative AI software](https://www.g2.com/categories/generative-ai), [MLOps platforms](https://www.g2.com/categories/mlops-platforms), [data science and machine learning platforms](https://www.g2.com/categories/data-science-and-machine-learning-platforms), [LLM software](https://www.g2.com/categories/large-language-models-llms), and [active learning tools](https://www.g2.com/categories/active-learning-tools) to support the full model development pipeline.

### Insights from G2 on Data Labeling Software

Based on category trends on G2, labeling accuracy controls and workforce management features stand out as standout capabilities. Faster training data pipeline construction and improved model accuracy stand out as primary outcomes of adoption.

Show More

### Data Labeling Topics

- [What is Data Labeling Software?](#what-is-data-labeling-software)
- [What types of data labeling software exist?](#what-types-of-data-labeling-software-exist)
- [What are the Common Features of Data Labeling Software?](#what-are-the-common-features-of-data-labeling-software)
- [Benefits of Data Labeling Software](#benefits-of-data-labeling-software)
- [Who Uses Data Labeling Software?](#who-uses-data-labeling-software)
- [Alternatives to data labeling software](#alternatives-to-data-labeling-software)
- [Challenges with Data Labeling Software](#challenges-with-data-labeling-software)
- [What companies should buy data labeling software?](#what-companies-should-buy-data-labeling-software)
- [How to Buy Data Labeling Software](#how-to-buy-data-labeling-software)
- [What does data labeling software cost?](#what-does-data-labeling-software-cost)
- [Implementation of data labeling software](#implementation-of-data-labeling-software)
- [Data Labeling Software Trends](#data-labeling-software-trends)
- [Data Labeling FAQs](#data-labeling-faqs)
- [Most Popular FAQs](#most-popular-faqs)
- [Small Business FAQs](#small-business-faqs)
- [Enterprise FAQs](#enterprise-faqs)

[
### Data Labeling Topics
 Expand/Collapse ](#)
- [What is Data Labeling Software?](#what-is-data-labeling-software)
- [What types of data labeling software exist?](#what-types-of-data-labeling-software-exist)
- [What are the Common Features of Data Labeling Software?](#what-are-the-common-features-of-data-labeling-software)
- [Benefits of Data Labeling Software](#benefits-of-data-labeling-software)
- [Who Uses Data Labeling Software?](#who-uses-data-labeling-software)
- [Alternatives to data labeling software](#alternatives-to-data-labeling-software)
- [Challenges with Data Labeling Software](#challenges-with-data-labeling-software)
- [What companies should buy data labeling software?](#what-companies-should-buy-data-labeling-software)
- [How to Buy Data Labeling Software](#how-to-buy-data-labeling-software)
- [What does data labeling software cost?](#what-does-data-labeling-software-cost)
- [Implementation of data labeling software](#implementation-of-data-labeling-software)
- [Data Labeling Software Trends](#data-labeling-software-trends)
- [Data Labeling FAQs](#data-labeling-faqs)
- [Most Popular FAQs](#most-popular-faqs)
- [Small Business FAQs](#small-business-faqs)
- [Enterprise FAQs](#enterprise-faqs)

## Learn More About Data Labeling Software

### What is Data Labeling Software?

Data labeling software labels or annotates data for training machine learning models. Machine learning algorithms rely on large amounts of labeled data to learn patterns and make predictions. Data labeling solutions help humans identify and label the relevant features and characteristics of the data that will be used to train the machine learning model.

Many types of data labeling solutions are available, ranging from simple tools that allow users to label data manually to more advanced tools that use machine learning algorithms to automate the labeling process. Some data labeling software also includes features such as image annotation tools, which allow users to label and annotate images and other visual data, and many data labeling platforms now extend that same tooling to computer vision tasks like bounding boxes and point cloud annotation for 3D and LiDAR data.

Data labeling software is used in various applications, including[](https://www.g2.com/articles/natural-language-processing)[natural language processing,](https://www.g2.com/articles/natural-language-processing) image and video classification, and[](https://www.g2.com/articles/object-detection)[object detection](https://www.g2.com/articles/object-detection). It is an important tool in the development and training of machine learning models and plays a critical role in their accuracy and effectiveness.

### What types of data labeling software exist?

Selecting a data labeling software requires a prior evaluation and understanding of data-driven workflows in your business. Below are the types of software you can consider.

- **Manual labeling software:** These data labeling platforms segment, label, and classify data with the help of a "[human in the loop"](https://www.g2.com/glossary/human-in-the-loop-definition) service. Human annotators label the training data based on businesses' geographic locations. The data annotation service is extended to the[ML model](https://www.g2.com/articles/machine-learning-models) development workflow, and labeling data becomes more effective.
- **Automated labeling software:** The automated data labeling software preprocesses raw datasets consisting of text, images, liDAR data, DICOM, PDF, or audio using an unsupervised learning approach. The algorithm assigns labels and categories to data without referring to external annotators.
- **Active learning labeling software:** Also known as active learning tools, these are semi-supervised tools that follow a "query-based" approach to labeling data. Based on the uncertainty score, they query data using manual or annotator labeling. For more challenging labels, they prompt the human annotator with queries.
- **Crowdsource labeling software:** These data labeling platforms crowd data labeling services to a crowd of developers to[train high-quality data pipelines](https://learn.g2.com/training-data). Custom data labeling can be ideal for large or enterprise-sized teams.
- **Integrated labeling and model training software:** These tools provide combined services for data labeling and predictive modeling. Using advanced data analysis, users can label, train, and build machine learning models to optimize their production cycles.

### What are the Common Features of Data Labeling Software?

Based on G2 reviews, data scientists and machine learning engineers evaluate data labeling software by comparing annotation tool variety, model-assisted labeling speed, and collaboration features. There are several features that are often included in data labeling software, including:

- **Label assignment:** Data labeling software allows users to assign labels or tags to specific data points, such as text, images, or videos.
- **Annotation tools:** Some data labeling software includes tools for annotating data, such as bounding boxes, polygon drawing tools, cloud points, keymakers, and point annotation tools. These tools can be used to highlight specific features or characteristics of the data.
- **Machine learning algorithms:** Some data labeling software uses machine learning algorithms to automate the labeling process or generate initial labels for data, which humans can then review and correct as needed.
- **Data management and organization** : Data labeling software often includes features for organizing and managing large datasets, such as the ability to filter and search for specific data points, track progress and completion, and generate reports.
- **Collaboration tools:** Some data labeling software includes collaboration tools, such as the ability to assign tasks to multiple users, track changes and revisions, and review and discuss data labeling decisions.
- **Integration with data science and machine learning platforms** : Some data labeling software is designed to integrate with popular[](https://www.g2.com/categories/data-science-and-machine-learning-platforms)[data science and machine learning platforms](https://www.g2.com/categories/data-science-and-machine-learning-platforms), such as TensorFlow or PyTorch, making it easier to use the labeled data to train machine learning models.
- **Image, text, audio, or video annotation:** These tools comply with multiple unstructured data formats to train and validate models designed to generate output in images, text, video, audio, PDF, and so on.

### Benefits of Data Labeling Software

Choosing a data labeling platform empowers businesses to either pre-train existing machine learning models to save time or build new models to upgrade their workflows and train teams. 

While data labeling platforms can help do both, it also has some significant benefits listed as under:

- **Improved accuracy and quality of labeled data** : Data labeling software can help ensure that data is accurately and consistently labeled, which is critical for the accuracy and effectiveness of machine learning models.
- **Increased efficiency and productivity** : Data labeling software can help streamline the data labeling process, allowing users to label more data in less time. This can be particularly useful for large datasets or repetitive or routine tasks.
- **Enhanced collaboration and team communication:** Some data labeling software includes collaboration tools, such as the ability to assign tasks to multiple users and track changes and revisions. These tools can help improve communication and coordination within teams working on data labeling projects.
- **Reduced cost** : Using data labeling software can help reduce the cost of data labeling projects by automating routine tasks and reducing the need for manual labor.
- **Increased flexibility and scalability** : Data labeling software can be used to label a wide variety of data types and can be easily scaled up or down as needed to meet project demands.
- **Respite for data operations, ML, and data science teams:** These solutions offer agile service marketplaces with high-quality labelers and annotators that solve the problems of data cleaning, preprocessing, and classification for these teams.
- **Superpixel segmentation and brushes:** These tools are also widely used for image recognition, natural language processing (NLP), and computer vision algorithms. It creates region pools using brushing and superpixel segmentation to classify images.

### Who Uses Data Labeling Software?

The data labeling tools are a must-have for businesses that want to foray into AI automation and build robust and efficient product applications and SDK with pre-installed machine learning capabilities.

Below are the individuals and organizations that use data labeling platforms:

- **Data scientists and machine learning engineers** : Data scientists and machine learning engineers use data labeling software to label and annotate data that will be used to train machine learning models. This helps the models learn to recognize patterns and make predictions based on the labeled data.
- **Business analysts and data analysts** : Business analysts and data analysts may use data labeling software to label and annotate data to create reports and visualizations or for use in machine learning models.
- **Quality assurance professionals** : Quality assurance professionals may use data labeling software to label and annotate data to test and debug machine learning models or other software applications.
- **Researchers** : Researchers in various fields, such as computer science, linguistics, and biology, may use data labeling software to label and annotate data to conduct research or develop machine learning models.

### Alternatives to data labeling software

Some alternatives to data labeling software provide annotation and labeling services along with other machine learning features.

- [Natural language processing (NLP) software](https://www.g2.com/categories/natural-language-processing-nlp) **:** The NLP software derives semantic relationships between words of an input sentence and generates relevant and personalized content. These tools replicate the functioning of a human brain to register prompt intent and derive coherent content blocks.
- [Machine learning operationalization (MLOps software):](https://www.g2.com/categories/mlops-platforms) The MLOPs software facilitates the entire machine learning model journey, from data preprocessing to ML integration and delivery. It applies various DevOps automation concepts and runs ML-based workflows without human supervision.
- [Image recognition software:](https://www.g2.com/categories/image-recognition) Image recognition software detects, categorizes, and localizes digital images or photographs. It is based on specialized deep-learning models that group data into grids and identify relevant categories of all objects.

### Challenges with Data Labeling Software

Even though data labeling software reduces costs, provides security and privacy to data, and moderates data quality control, some evident challenges can occur at any stage of working with this platform.

Below are some of the challenges of data labeling software

- **Data quality and consistency:** It is not certain that data labeling tools would predict accurate labels for ML models. Sometimes, the platform can incorrectly categorize text as video or process incorrect calculations, which can lower the data quality.
- **Scalability:** As a business receives large influxes of data, repurposing raw data to train models, make model versions, calculate risks, and be consistent with quality control becomes a challenge and results in scalability problems for different teams across the company.
- **Cost: ** Though data labeling platforms tend to be cheaper than other expensive human annotation services, submitting a large cluster of datasets for categorization can become costly. It would exhaust your credits and leave you with no alternative but to upgrade to a more expensive plan.
- **Complexity of tasks:** Not all data labeling tasks are simple. Some require deep domain exercises and more specialized algorithm training, such as reinforcement learning, query sampling, or entropy, to build ML models accurately without investing in external annotation services.
- **Data privacy and security:** These platforms are open source or paid. However, they retrieve and store data on[](https://www.g2.com/categories/hybrid-cloud-storage-solutions)[hybrid](https://www.g2.com/categories/hybrid-cloud-storage-solutions) or[](https://www.g2.com/articles/public-cloud)[public cloud storage platforms](https://www.g2.com/articles/public-cloud), which can infect your dataset and give hackers and fishers leeway to infect the data. 

### What companies should buy data labeling software?

Companies that want to optimize the quality of their datasets and build powerful algorithms should consider data labeling software. Not just because it helps label data but because it can build accurate predictions and forecasts. Here are some companies that can benefit from these tools:

- **Machine learning startups or research labs:** These companies conduct the majority of machine learning experiments and constantly work with data tools. Investing in a data labeling tool can benefit their AI research and ML model development processes.
- **Data companies:** Companies that provide data management services like search engines, e-commerce platforms, or social media management tools also need data labeling software to generate effective algorithms that generate accurate responses and deal with large data volumes.
- **Market research companies:** Companies that conduct market research or gather customer insights and trends can also benefit from data labeling platforms. These platforms allow them to gather real-time market trends and track consumer behaviors.
- **Healthcare organizations:** These companies utilize data labeling platforms for early detection of diseases, medical imaging, patient recordkeeping, consultation, and treatments. With this software, they accurately study patient data and forecast treatment cycles.

### How to Buy Data Labeling Software

Investing in data labeling software is a step-by-step process that requires the input of all related teams and stakeholders. Below are the steps buyers need to follow chronologically to purchase the best data labeling platform for their business. 

#### Requirements Gathering (RFI/RFP) for Data Labeling Software

Before purchasing, buyers should consider their needs and determine what they hope to achieve with this software. Evaluate the type of database system, products, AI maturity, and budget data from revenue teams. Also, make a list of the data-related and language services you expect from the product. Enlist all these points in the form of a structured request for proposal (RFP) and get the approval of your teams and stakeholders who are involved in the decision-making process.

#### Compare Data Labeling Software Products

Evaluate the shortlisted products' features, security and privacy guidelines, pros and cons, pricing, and AI functionalities. Compare the features and benefits with the requirements your team has listed in the request for proposal. Analyze the budget, contract metrics, and return on investment for each software feature and compare them with those of other contenders in the market. 

At this stage, buyers can also request demos or free trials to see how the software works and ensure it meets their needs. While shortlisting vendors, it is also crucial to consider their credibility. Look for vendors with a strong track record and a good reputation.

#### Selection of Data Labeling Software

Discuss all shortlisted software's technical and configuration workflows with your IT and software development teams. Sit with them to analyze current software consumption, active subscription plans, system of records, and IT audit reports, and then check where this software fits in your tech stack. Discuss the compatibility of the software with related account executives and sales teams to ensure that the software doesn't cause more overheads and storage expenses for your teams.

#### Negotiation

After finalizing the software, get your legal teams to draft a legitimate contract outlining RFP terms, renewal policies, data retention and privacy policies, and the vendor's non-compete and discuss it with the vendor. At this stage, it is also feasible to negotiate for a better subscription rate, more features, or add-ons that buyers are interested in at the vendor's discretion. 

#### Final decision

The final decision to purchase data labeling software lies with the buyer's decision-making teams. These could be the chief information officer (CIO), head of the data science team, or procurement team. While making this decision, it is also important to consider budget constraints, team queries, or business objectives. It will be helpful to consult with stakeholders and experts, like data scientists and ML engineers, to get their input on the best data labeling solution for the institution.

### What does data labeling software cost?

The cost of data labeling software can vary widely depending on its specific features and capabilities, as well as the size and scope of the deployment. Some software is free or open-source, while others are commercial products sold on a subscription or per-use basis.

Data labeling software designed for enterprise-level use with a wide range of advanced features will be more expensive than straightforward solutions. Prices can range from a few hundred dollars per year for an introductory subscription to several thousand dollars for a more comprehensive solution.

It is essential to evaluate subscription, license, pay-per-seat, and pay-per-token usage costs to check whether the product is suitable for your business and has scope for a decent return on investment (ROI). While you are engaged in the monetary calculations, factor in software upgrade cost, business size, version, software maintenance, and upsell costs to indicate the budget clearly. These tools can help improve productivity and efficiency, contributing to ROI calculation.

To calculate the ROI of data labeling software, the following formula can be used:

ROI = (Benefits - Costs) / Costs

"Benefits" is the value of the time saved and increased productivity resulting from using the software, and "Costs" is the total cost of the software license and any additional costs associated with implementation and use.

### Implementation of data labeling software

When considering purchasing data labeling software, companies should have a rough vision of how to implement it for data science and machine learning teams.

Other factors, such as alignment with notebook editors, statistical tools, data analysis limitations, training, and testing ML cycles, will be altered and modified per the implementation timeline of data labeling software. Below are some tips to ensure a smooth implementation.

- **Integration with existing data and ML workflows:** Consult your software development teams on setting up user permissions and integrating this platform with your existing code development platform, such as R or Python editors. The first step is to ensure it is compatible with various data formats, data types, data analysis tools, and other collaborative ML tools.
- **Customization and flexibility in labeling tasks:** These platforms must be agile and compatible with datasets of multiple formats and languages. It should provide customization for various tasks such as image recognition, computer vision, audio generation, video generation, and[speech recognition](https://www.g2.com/glossary/speech-recognition-definition). Labeling unstructured data should be open to anyone who authenticates their identity through multi-factor authentication and is an authorized user.
- **Collaboration and workforce management features:** The data labeling platform needs to be activated for model prototype and version control. It should have features like role-based access control, data privacy and security guidelines, user authentication, model collaboration, and ML code supervision. The platform should be accessible to respective team members so they can double-check the labeled tasks and stop the model from hallucinating at any stage of the training data pipeline.
- **Quality assurance and review mechanisms:** When a model's output accuracy depends on the quality of training data, it is evident that data labeling platforms need to be set of modulation accuracy, quality control, and labeling review mechanisms. Given the models might inaccurately label datasets or predict wrong values, the labels need to be further supervised by a human in the loop service or external human oracle.
- **Scalability, automation, and cost efficiency:** As labeling needs grow, ML engineers and developers need to invest in a scalable and cost-efficient data labeling solution that doesn't obstruct their network infrastructure and database architecture. The final implementation step is to ensure that the controls are set, the license is active, and the platform is retrieving and labeling data typically.

### Data Labeling Software Trends

Overall, these trends reflect the growing importance of data labeling in the machine learning and AI ecosystem and the need for tools and technologies to help organizations create and manage large datasets of labeled data efficiently and effectively. There are several trends surrounding data labeling software that are worth noting:

- **Increased adoption of artificial intelligence (AI) and machine learning (ML)**: One key trend in data labeling software is the increasing adoption of AI and ML technologies. Many software solutions now incorporate AI and machine learning algorithms to automate and streamline the data labeling process, improving efficiency and accuracy. As with general AI software,[](https://www.g2.com/articles/ai-trends-2023)[G2 expects this software to get cheaper](https://www.g2.com/articles/ai-trends-2023).
- **Growing demand for high-quality labeled data** : Another trend is the growing demand for high-quality labeled data to train and test machine learning models. Data labeling software can help organizations create and manage large datasets of labeled data, improving the quality and reliability of machine learning models.
- **Focus on user experience and collaboration** : Another trend in data labeling software is a focus on user experience and collaboration. Many data labeling software solutions now offer intuitive and user-friendly interfaces, tools, and features that facilitate collaboration and teamwork.

### Data Labeling FAQs

### Most Popular FAQs

#### Which data labeling software has the best reviews?

Across the Data Labeling category, where ML engineers make up a notable share of the most trusted reviews, the strongest ratings tend to go to platforms that make the repetitive work of annotation feel fast and organized rather than tedious.

- [Roboflow](https://www.g2.com/products/roboflow/reviews): Carries a large review base at a high rating, with users pointing to frequent updates and a genuinely friendly interface for building computer vision datasets.
- [SuperAnnotate](https://www.g2.com/products/superannotate/reviews): Supports essentially every data type, with strong tools for splitting images and organizing projects across a team.
- [Encord](https://www.g2.com/products/encord/reviews): A newer name in this data set, though its S3 integration and customer success team come up repeatedly as reasons teams building serious infrastructure choose it.

#### What are the best automated data labeling tools?

Automation here usually means pre-labeling data with a model first, so a human is verifying and correcting rather than tagging everything from a blank slate.

- [Roboflow](https://www.g2.com/products/roboflow/reviews): Its data augmentation and AI labeling features come up often as a reason it saves real time over manual tagging.
- [SuperAnnotate](https://www.g2.com/products/superannotate/reviews): Handles auto-labeling across a wide range of data types, which keeps a project's setup and dataset preparation in one place.
- [CVAT.ai](https://www.g2.com/products/cvat-ai/reviews): A smaller sample on this specific theme, but its model-assisted annotation loop runs YOLO and SAM2 pre-annotation directly, turning most of the work into verifying rather than labeling from scratch.

#### Which data labeling software enables consistent, high-quality annotations across multiple image formats and datasets?

Consistency at this level usually comes down to how well a platform organizes projects and enforces the same workflow across every dataset a team touches.

- [Roboflow](https://www.g2.com/products/roboflow/reviews): Lets teams export and import datasets with annotations intact, keeping formats consistent as data moves between training runs.
- [SuperAnnotate](https://www.g2.com/products/superannotate/reviews): Its ability to organize projects across data types is what keeps annotation quality consistent at scale.
- [Taskmonk](https://www.g2.com/products/taskmonk/reviews): A smaller sample so far, but its no-code Task Builder and pre-built quality checks are designed specifically to keep labeling consistent across any data type.

#### Which data labeling software offers user-friendly interfaces and shortcut tools for efficient image annotation and tagging?

The platforms reviewers single out here are the ones where annotating feels fast because of the interface itself, not just the underlying model.

- [SuperAnnotate](https://www.g2.com/products/superannotate/reviews): This is the single most-mentioned strength in its reviews — a clean, intuitive interface that keeps annotation moving quickly.
- [Roboflow](https://www.g2.com/products/roboflow/reviews): Described as intuitive and easy to use, with frequent updates that keep the tagging workflow current.
- [FiftyOne](https://www.g2.com/products/voxel51-fiftyone/reviews): A smaller sample on this specific theme, but its visualization module lets a team spot labeling discrepancies and edge cases at a glance rather than digging through data manually.

#### What is the best data labeling software?

The right answer depends on the type of data and team size, but a few platforms come up across a wide range of annotation projects.

- [Kili](https://www.g2.com/products/kili/reviews): A smaller footprint in this data set, but simple cloud storage integration and scalability make it work well for growing teams.
- [CVAT.ai](https://www.g2.com/products/cvat-ai/reviews): Intuitive enough to find what's needed quickly, especially compared to a self-built annotation tool.
- [Labelbox](https://www.g2.com/products/labelbox/reviews): A newer name here, though its professional, uncluttered interface comes up as a reason it's simple and effective to work in day to day.

#### Which data labeling tools maintain stable performance and annotation quality for high-volume image batches?

Volume tends to be where a platform's design choices show up the most, the same interface that feels snappy on a small project can slow down once a team is running thousands of high-resolution images through it, so reviewers' experience at scale is the most useful signal here.

- [Roboflow](https://www.g2.com/products/roboflow/reviews): Reviewers describe performance as "consistently fast, very intuitive, and reliable, even when handling large datasets or heavier annotation workloads," though a few note that pagination can interrupt the review flow once a dataset gets very large.
- [SuperAnnotate](https://www.g2.com/products/superannotate/reviews): Reviewers specifically call out its keyboard shortcuts as "an absolute lifesaver when working on large batches," while also noting the platform can slow down when loading very large or high-resolution datasets — a tradeoff worth weighing against its annotation depth.
- [CVAT.ai](https://www.g2.com/products/cvat-ai/reviews): Its model-assisted pre-annotation loop reduces how much of a large batch needs manual tagging in the first place, which is one way to keep quality consistent as volume grows rather than relying on raw interface speed alone.

#### Which data labeling platforms do ML engineers trust most for scalable annotation with intuitive design and responsive support?

Engineers evaluating this category tend to weigh three things together rather than separately: does the interface stay out of the way, does support actually respond, and does the workflow speed up how fast a model can go through another training iteration.

- [SuperAnnotate](https://www.g2.com/products/superannotate/reviews): One reviewer, writing from an engineering perspective, described it as reducing "coordination overhead instead of adding more complexity" and called it a "scalable, well-structured annotation solution" for machine learning projects.
- [Roboflow](https://www.g2.com/products/roboflow/reviews): A research team reported cutting the time from raw imagery to a trained neural network by roughly fivefold, with reviewers separately noting that support has "always been quick and helpful" when needed.
- [Labelbox](https://www.g2.com/products/labelbox/reviews): Reviewers describe a professional, uncluttered interface with "all the necessary tools" to work smoothly, without extra details getting in the way of day-to-day annotation work.

### Small Business FAQs

#### What is the most affordable data labeling software for SMBs?

Within the[](https://www.g2.com/categories/data-labeling/small-business)[small business segment of Data Labeling](https://www.g2.com/categories/data-labeling/small-business), the platforms that come up most often are the ones with a genuine free tier rather than just a limited trial.

- [Roboflow](https://www.g2.com/products/roboflow/reviews): Offers a free entry tier, which fits its large base of solo founders and researchers using it for computer vision projects.
- [CVAT.ai](https://www.g2.com/products/cvat-ai/reviews): A team running a small operation can move straight to verifying and correcting pre-annotated data rather than paying for labeling from scratch.
- [SuperAnnotate](https://www.g2.com/products/superannotate/reviews): Reasonable task rates and convenient payment methods come up in reviews as reasons it works for teams watching costs closely.

#### What is the best data labeling software for startups?

Startups evaluating[](https://www.g2.com/categories/data-labeling/small-business)[small business data labeling](https://www.g2.com/categories/data-labeling/small-business) tools tend to prioritize a platform a lean team can run without a dedicated annotation specialist.

- [Roboflow](https://www.g2.com/products/roboflow/reviews): A large share of its reviewers are founders and researchers, and its friendly, frequently updated interface fits a small team without a dedicated ML infrastructure person.
- [SuperAnnotate](https://www.g2.com/products/superannotate/reviews): Handles every data type in one place, which cuts down on stitching together separate tools as a startup's dataset needs grow.
- [Taskmonk](https://www.g2.com/products/taskmonk/reviews): A smaller sample so far, but its no-code Task Builder lets a startup manage labeling projects at scale without writing custom tooling.

#### Which data labeling platform is the most user-friendly for startups?

Ease of use matters most at this stage, since the person labeling data is often the same person building and training the model.

- [SuperAnnotate](https://www.g2.com/products/superannotate/reviews): Its clean, intuitive interface is the most consistently praised feature across reviews from smaller teams.
- [Roboflow](https://www.g2.com/products/roboflow/reviews): Easy to understand with a friendly UI, which shortens the ramp-up for a small team's first project.
- [Datature](https://www.g2.com/products/datature/reviews): A newer name in this data set, though it's described as extremely user-friendly for both annotation and model training.

#### Which data labeling tool is easiest to set up for small teams?

Setup speed is one of the more differentiated ratings in this category, and small teams generally do best with a platform that's usable the same day.

- [Labelbox](https://www.g2.com/products/labelbox/reviews): Setup is described as super simple with no issues, with everything accessible right after signing in.
- [Segments.ai](https://www.g2.com/products/segments-ai/reviews): A smaller sample so far, but its all-in-one workflow lets a team label and review data in the same place without stitching tools together.
- [Roboflow](https://www.g2.com/products/roboflow/reviews): Frequent updates and a friendly interface keep the path from signup to a first labeled dataset short.

#### Which data labeling tool is best for solo researchers and independent ML practitioners?

A meaningful share of this category's reviewers work alone or on very small teams, and the platforms that fit best tend to double as a full workspace rather than just a labeling tool.

- [Roboflow](https://www.g2.com/products/roboflow/reviews): Its user base skews heavily toward founders and researchers working solo, with a free entry tier that matches that reality.
- [FiftyOne](https://www.g2.com/products/voxel51-fiftyone/reviews): Works as a central orchestration layer for an entire computer vision pipeline, useful when one person is running evaluation, visualization, and labeling together.
- [CVAT.ai](https://www.g2.com/products/cvat-ai/reviews): Built for exactly this scenario — a small operation running model-assisted pre-annotation so one person can focus on verifying rather than labeling from scratch.

### Enterprise FAQs

#### What is best-rated data labeling software for large enterprises?

Within the[](https://www.g2.com/categories/data-labeling/enterprise)[Enterprise segment of Data Labeling](https://www.g2.com/categories/data-labeling/enterprise), a smaller set of platforms have the review volume from large organizations to back up a strong rating.

- [SuperAnnotate](https://www.g2.com/products/superannotate/reviews): Holds up well at the enterprise level, with the same broad data-type support and project organization that smaller teams rely on scaling cleanly.
- [Roboflow](https://www.g2.com/products/roboflow/reviews): The same frequent updates and friendly interface that made it popular with smaller teams carry over at enterprise scale.
- [Taskmonk](https://www.g2.com/products/taskmonk/reviews): A smaller footprint at this scale so far, but its custom workflows and pre-built quality checks are built to optimize labeling budgets as volume grows.

#### What is the most reliable data labeling tool for enterprises?

Reliability at this scale tends to come down to support responsiveness, since large teams need fast answers when an annotation pipeline stalls.

- [Roboflow](https://www.g2.com/products/roboflow/reviews): Consistent support ratings back up its reputation for frequent updates and a stable, well-maintained platform.
- [SuperAnnotate](https://www.g2.com/products/superannotate/reviews): Support quality holds up even as reviewers' organizations get larger and more projects get added.
- [Encord](https://www.g2.com/products/encord/reviews): A smaller sample so far, but its customer success team is described as coming to every meeting prepared and actively building features based on customer feedback.

#### What is best-reviewed data labeling software for enterprise-scale annotation consistency?

Enterprise deployments need annotation quality to hold steady across many projects and contributors, not just within a single dataset.

- [SuperAnnotate](https://www.g2.com/products/superannotate/reviews): Its project organization tools are what let large teams keep annotation standards consistent across many datasets at once.
- [Roboflow](https://www.g2.com/products/roboflow/reviews): Consistent dataset export and import keeps annotation formats stable as a large organization scales up its computer vision work.
- [Kili](https://www.g2.com/products/kili/reviews): A smaller footprint at enterprise scale so far, but its cloud storage integrations and scalability are what growing organizations lean on most.

#### Which data labeling platform offers the strongest quality assurance and model-assisted annotation for enterprise teams?

At enterprise volume, quality assurance needs to be built into the workflow itself rather than handled through manual spot-checks after the fact.

- [CVAT.ai](https://www.g2.com/products/cvat-ai/reviews): Its model-assisted annotation loop runs pre-annotation directly through the platform, turning most enterprise labeling work into verification rather than labeling from scratch.
- [Taskmonk](https://www.g2.com/products/taskmonk/reviews): Pre-built quality checks and custom workflows are designed specifically to enhance data quality at the volume large teams operate at.
- [SuperAnnotate](https://www.g2.com/products/superannotate/reviews): Its ability to organize projects and split data across large teams supports the kind of structured QA process enterprise labeling needs.

#### Which data labeling platform is best for large-scale crowdsourced annotation workforces?

Some enterprise labeling needs go beyond software alone and require an actual workforce of annotators, which changes what "best" means for this question.

- [Appen](https://www.g2.com/products/appen/reviews): A robust data collection and crowdsourcing platform built specifically for AI, language, and similar large-scale annotation work.
- [Outlier AI](https://www.g2.com/products/outlier-ai-outlier-ai/reviews): Offers a flexible range of real AI project work, from writing prompts to reviewing responses, which enterprise teams tap into for large annotation volumes.
- [Keymakr](https://www.g2.com/products/keymakr/reviews): A smaller sample so far, but its team is described as fast, responsive, and proactive about finding solutions when annotation issues come up.

_Researched and written by_ [_Matthew Miller_](https://learn.g2.com/author/matthew-miller)