# Best Low-Code Machine Learning Platforms Software

## How Many Low-Code Machine Learning Platforms Software Products Does G2 Track?

**Total Products under this Category:** 25

### Category Stats (Aug 2026)

- **Average Rating:** 4.41/5 (↑0.02 vs Jul 2026) The average rating of products in this category, based on all submitted ratings
- **Top Trending Product:** Google Cloud AutoML (+4.08%) - Among all products in this category, Google Cloud AutoML recorded the largest rating increase compared to last month

_Last updated: August 28, 2026_

## How Does G2 Rank Low-Code Machine Learning Platforms Software Products?

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

- 30 Analysts and Data Experts
- 3,900+ Authentic Reviews
- 25+ 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 Low-Code Machine Learning Platforms Software
 ![G2 Grid® for Low-Code Machine Learning Platforms Software plotting products by satisfaction and market presence](https://www.g2.com/categories/low-code-machine-learning-platforms/grids.png?focus%5B%5D=1327283&focus%5B%5D=989&focus%5B%5D=7150&focus%5B%5D=21469&focus%5B%5D=67045&focus%5B%5D=16291&focus%5B%5D=16295&focus%5B%5D=38300)

Highlighted products: SAS Viya, Alteryx, Dataiku, Gemini Enterprise Agent Platform, Google Cloud AutoML, KNIME, Altair AI Studio, and Qlik Predict.

Underlying data: [Grid® JSON](https://www.g2.com/categories/low-code-machine-learning-platforms/grids.json?focus%5B%5D=sas-sas-viya&focus%5B%5D=alteryx&focus%5B%5D=dataiku&focus%5B%5D=gemini-enterprise-agent-platform&focus%5B%5D=google-cloud-automl&focus%5B%5D=knime-analytics-platform&focus%5B%5D=rapidminer-studio&focus%5B%5D=qlik-predict)

**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=1011941&secure%5Bchosen_at%5D=2026-08-28T23%3A06%3A03Z&secure%5Bdisplayable_resource_id%5D=1011941&secure%5Bdisplayable_resource_type%5D=Category&secure%5Bmedium%5D=sponsored&secure%5Bplacement_reason%5D=page_category&secure%5Bplacement_resource_ids%5D%5B%5D=1011941&secure%5Bprioritized%5D=false&secure%5Bproduct_id%5D=52115&secure%5Bresource_id%5D=1011941&secure%5Bresource_type%5D=Category&secure%5Bsource_type%5D=category_page&secure%5Bsource_url%5D=https%3A%2F%2Fwww.g2.com%2Fcategories%2Flow-code-machine-learning-platforms%3Fsource%3Dsearch&secure%5Btoken%5D=f802d33d05f88ab26526f0393a9f75cdf81a4b8718eeece32de48ce349863097&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)

### [SAS Viya](https://www.g2.com/products/sas-sas-viya/reviews)

SAS Viya is a cloud-native data and AI platform that enables teams to build, deploy and scale explainable AI that drives trusted, confident decisions. It unites the entire data and AI life cycle and empowers teams to innovate quickly while balancing speed, automation and governance by design. Viya unifies data management, advanced analytics and decisioning in a single platform, so organizations can move from experimentation to production with confidence, delivering measurable business impact that is secure, explainable and scalable across any environment. Key capabilities required to deliver trusted decisions include: • End-to-end clarity across the data and AI life cycle, with built-in lineage, auditability and continuous monitoring to support defensible decisions. • Governance by design, enabling consistent oversight across data, models and decisions to reduce risk and accelerate adoption. • Explainable AI at scale, so insights and outcomes can be understood, validated and trusted by business and regulators alike. • Operationalized analytics, ensuring value continues beyond deployment through monitoring, retraining and life cycle management. • Flexible, cloud-native deployment, allowing organizations to start anywhere and scale everywhere while maintaining control.

**Average Rating:** 4.3/5.0

**Total Reviews:** 774

#### Who Is the Company Behind SAS Viya?

- **Seller:** [SAS Institute Inc.](https://www.g2.com/sellers/sas-institute-inc-df6dde22-a5e5-4913-8b21-4fa0c6c5c7c2)
- **Company Website:** www.sas.com
- **Year Founded:** 1976
- **HQ Location:** Cary, NC
- **Twitter:** @SASsoftware  
60,863 Twitter followers
- **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=64db42c044af5bbad79bd9677a620a6c31a8ff1abf4e7b2a6f1d6ed9561d105d&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2F1491%2F&secure%5Burl_type%5D=linkedin_company_website)  
18,638 employees on LinkedIn®

#### Who Uses This Product?

- **Who Uses This:** Student, Biostatistician
- **Top Industries:** Pharmaceuticals, Banking
- **Company Size:** 33% Small, 33% Large

#### What Do G2 Reviewers Say About SAS Viya?

_AI-generated summary from verified user reviews_

##### Pros

- Users value the **ease of use** in SAS Viya, enhancing data visualization and decision-making for businesses.
- Users appreciate the **advanced analytical capabilities** of SAS Viya, making data analysis and decision-making more efficient.
- Users value the **sophisticated analytical capabilities** of SAS Viya, enhancing decision-making and insights from diverse data sources.
- Users value the **end-to-end data lifecycle tooling** in SAS Viya, enhancing insights and strategic decision-making capabilities.
- Users commend SAS Viya for its **user-friendly interface** , making complex analytics accessible to individuals of all skill levels.

##### Cons

- Users find SAS Viya **difficult for non-technical users** to navigate, impacting ease of access to reports and dashboards.
- Users find the **learning curve challenging** , especially for non-technical individuals navigating reports and dashboards.
- Users find the **visualization complexity** of SAS Viya challenging, especially for those without technical expertise.
- Users find the **difficult learning curve** for SAS Viya challenging, especially for non-technical users attempting to access features.
- Users find the **expensive pricing** of SAS Viya a potential barrier, complicating their decision-making process.

#### What Are Recent G2 Reviews of SAS Viya?

**["SAS Viya: Powerful AI & Data Analysis with Seamless Integrations"](https://www.g2.com/survey_responses/sas-viya-review-11855145)**

**Rating:** 5.0/5.0 stars

_— Verified User in Hospital & Health Care_

[Read full review](https://www.g2.com/survey_responses/sas-viya-review-11855145)

**["Effective Data Analysis with SAS Viya"](https://www.g2.com/survey_responses/sas-viya-review-11872818)**

**Rating:** 4.5/5.0 stars

_— Fungai J._

[Read full review](https://www.g2.com/survey_responses/sas-viya-review-11872818)

#### What Are G2 Users Discussing About SAS Viya?

- [What is SAS Visual Data Mining and Machine Learning used for?](https://www.g2.com/discussions/what-is-sas-visual-data-mining-and-machine-learning-used-for) - 2 comments

### [Alteryx](https://www.g2.com/products/alteryx/reviews)

Alteryx, through it's Alteryx One platform, helps enterprises transform complex, disconnected data into a clean, AI-ready state. Whether you’re creating financial forecasts, analyzing supplier performance, segmenting customer data, analyzing employee retention, or building competitive AI applications from your proprietary data, Alteryx One makes it easy to cleanse, blend, and analyze data to unlock the unique insights that drive impactful decisions. AI-Guided Analytics Alteryx automates and simplifies every stage of data preparation and analysis, from validation and enrichment to predictive analytics and automated insights. Incorporate generative AI directly into your workflows to streamline complex data tasks and generate insights faster. Unmatched flexibility, whether you prefer code-free workflows, natural language commands, or low-code options, Alteryx adapts to your needs. Trusted. Secure. Enterprise-Ready. Alteryx is trusted by over half of the Global 2000 and 19 of the top 20 global banks. With built-in automation, governance, and security, your workflows can scale and maintain compliance while delivering consistent results. And it doesn’t matter if your systems are on-premises, hybrid, or in the cloud; Alteryx fits effortlessly into your infrastructure. Easy to Use. Deeply Connected. What truly sets Alteryx apart is our focus on efficiency and ease of use for analysts and our active community of 700,000 Alteryx users to support you at every step of your journey. With seamless integration to data everywhere including platforms like Databricks, Snowflake, AWS, Google, SAP, and Salesforce, our platform helps unify siloed data and accelerate getting to insights. Visit Alteryx.com for more information, and to start your free trial.

**Average Rating:** 4.6/5.0

**Total Reviews:** 864

#### Who Is the Company Behind Alteryx?

- **Seller:** [Alteryx](https://www.g2.com/sellers/alteryx)
- **Company Website:** www.alteryx.com
- **Year Founded:** 1997
- **HQ Location:** Irvine, CA
- **Twitter:** @alteryx  
26,149 Twitter followers
- **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=ae8a7629c5a6d593caff29361a6ee3fb670df11992dd94a9656c66461078b340&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2F903031%2F&secure%5Burl_type%5D=linkedin_company_website)  
2,304 employees on LinkedIn®

#### Who Uses This Product?

- **Who Uses This:** Data Analyst, Analyst
- **Top Industries:** Financial Services, Accounting
- **Company Size:** 63% Large, 21% Medium

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

_AI-generated summary from verified user reviews_

##### Pros

- Users appreciate the **ease of use** in Alteryx, finding it simple to automate tasks with drag and drop functionality.
- Users value the **automation capabilities** of Alteryx, streamlining data processes and enhancing analytical efficiency.
- Users find Alteryx to be **very intuitive** , making it easy for non-technical users to learn and utilize.
- Users find that Alteryx's interface makes **learning technology easy** for everyone, even those without a tech background.
- Users value Alteryx for its **efficiency** in managing data, streamlining workflows, and enhancing overall productivity.

##### Cons

- Users highlight the **expensive pricing** of Alteryx, making it difficult for small teams or startups to afford licenses.
- Users face a **steep learning curve** with Alteryx, requiring time to master its complex features.
- Users find that Alteryx suffers from **missing features** , such as lack of direct database access and limited reporting tools.
- Users find the **learning difficulty** of Alteryx steep, especially for those unfamiliar with RegEx and SQL.
- Users experience **slow performance** with Alteryx, particularly when handling large workflows and during data wrangling tasks.

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

**["Scales Operations and Saves Time with Automated Data Workflows"](https://www.g2.com/survey_responses/alteryx-review-13047637)**

**Rating:** 4.5/5.0 stars

_— Ihor B._

[Read full review](https://www.g2.com/survey_responses/alteryx-review-13047637)

**["Makes Data Prep Faster with an Intuitive Drag-and-Drop Workflow"](https://www.g2.com/survey_responses/alteryx-review-13190742)**

**Rating:** 4.5/5.0 stars

_— Anushka S._

[Read full review](https://www.g2.com/survey_responses/alteryx-review-13190742)

### [Dataiku](https://www.g2.com/products/dataiku/reviews)

Dataiku is the Platform for AI Success: the AI orchestration layer where enterprises build, deploy, and govern analytics, models, and agents at scale. It sits on top of the data platforms, clouds, and AI services you already use, working across all of them without locking you into any one. Dataiku expands who can build production AI, putting the right tools in the hands of data scientists and domain experts alike, from fraud analysts to demand planners. It orchestrates machine learning, rules, LLMs, and agents as one governed system, built on more than a decade of running production AI. Governance is part of the build rather than something bolted on afterward, so teams ship faster while keeping performance, cost, and risk under control. The result: AI that moves from experimentation to trusted, measurable execution now, not in 18 months.

**Average Rating:** 4.4/5.0

**Total Reviews:** 215

#### Who Is the Company Behind Dataiku?

- **Seller:** [Dataiku](https://www.g2.com/sellers/dataiku)
- **Company Website:** Dataiku.com
- **Year Founded:** 2013
- **HQ Location:** New York, NY
- **Twitter:** @dataiku  
22,917 Twitter followers
- **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=e59ec8fccc02ecc4f883419e54da56d3f6fc8b1e556153f0cc01cd05e3b77faa&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2Fdataiku%2F&secure%5Burl_type%5D=linkedin_company_website)  
1,619 employees on LinkedIn®

#### Who Uses This Product?

- **Who Uses This:** Data Scientist, Data Analyst
- **Top Industries:** Financial Services, Pharmaceuticals
- **Company Size:** 60% Large, 23% Medium

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

_AI-generated summary from verified user reviews_

##### Pros

- Users appreciate how **Dataiku simplifies ML development** , enabling quick training, evaluation, and understanding of data easily.
- Users find Dataiku **easy to use** , simplifying ML development and helping detect opportunities and risks effortlessly.
- Users value the **ease of use** in Dataiku, enabling collaboration and simplifying complex data processes for all skill levels.
- Users appreciate the **easy integrations** of Dataiku, facilitating collaboration across diverse analytics tools and skill sets.
- Users commend the **productivity improvement** brought by Dataiku’s visual recipes and robust tools for analytics projects.

##### Cons

- Users find the **learning curve steep** , making it challenging for beginners to fully utilize Dataiku's advanced features.
- Users find the **steep learning curve** challenging, especially for beginners navigating Dataiku's advanced features.
- Users find the **difficult learning** curve challenging for beginners, impacting their ability to maximize the platform's potential.
- Users face **slow performance** with Dataiku when managing large datasets, impacting efficiency and productivity.
- Users find the **pricing high** for small companies and students, impacting accessibility for basic projects.

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

**["Build Faster Workflows with Connected Data from many providers or distinct data sources"](https://www.g2.com/survey_responses/dataiku-review-13120436)**

**Rating:** 4.5/5.0 stars

_— Adalberto G._

[Read full review](https://www.g2.com/survey_responses/dataiku-review-13120436)

**["Unified, Low-Code Platform That Boosts End-to-End Data & AI Productivity"](https://www.g2.com/survey_responses/dataiku-review-13125252)**

**Rating:** 4.5/5.0 stars

_— Ravindra N._

[Read full review](https://www.g2.com/survey_responses/dataiku-review-13125252)

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

- [Is Dataiku an ETL tool?](https://www.g2.com/discussions/is-dataiku-an-etl-tool)
- [Is Dataiku web based?](https://www.g2.com/discussions/is-dataiku-web-based)
- [What is DSS Dataiku?](https://www.g2.com/discussions/what-is-dss-dataiku)
- [What is Dataiku DSS used for?](https://www.g2.com/discussions/what-is-dataiku-dss-used-for)

### [Google Cloud AutoML](https://www.g2.com/products/google-cloud-automl/reviews)

Google Cloud AutoML is a suite of machine learning products designed to enable developers with limited expertise to train high-quality custom models tailored to their specific business needs. By leveraging Google's advanced transfer learning and neural architecture search technologies, AutoML simplifies the process of building, deploying, and scaling machine learning models, making AI more accessible to a broader audience. Key Features and Functionality: - Automated Model Training: AutoML automates the selection of model architecture and hyperparameter tuning, reducing the need for manual intervention and specialized knowledge. - User-Friendly Interface: The platform offers an intuitive graphical interface that allows users to upload data, train models, and manage deployments with ease. - Versatile Model Types: AutoML supports various data types and tasks through specialized services: - AutoML Vision: For image classification and object detection. - AutoML Natural Language: For text classification, sentiment analysis, and entity recognition. - AutoML Translation: For creating custom translation models between language pairs. - AutoML Video Intelligence: For video classification and object tracking. - AutoML Tables: For structured data tasks like regression and classification. - Seamless Integration: AutoML integrates with other Google Cloud services, facilitating efficient data management, model deployment, and scalability. Primary Value and Problem Solving: Google Cloud AutoML democratizes machine learning by enabling users without deep technical expertise to develop and deploy custom models. This accessibility allows businesses to harness the power of AI to solve complex problems, such as improving customer experiences through personalized recommendations, automating content moderation, enhancing language translation services, and gaining insights from large datasets. By reducing the barriers to entry, AutoML empowers organizations to innovate and stay competitive in their respective industries.

**Average Rating:** 4.3/5.0

**Total Reviews:** 41

#### Who Is the Company Behind Google Cloud AutoML?

- **Seller:** [Google](https://www.g2.com/sellers/google)
- **Year Founded:** 1998
- **HQ Location:** Mountain View, CA
- **Twitter:** @google  
31,899,995 Twitter followers
- **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=fe4a5936665c9702418dd53c477fef5a7baea08078bb117ed67e966fc581b9ec&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2F1441%2F&secure%5Burl_type%5D=linkedin_company_website)  
341,888 employees on LinkedIn®
- **Ownership:** NASDAQ:GOOG

#### Who Uses This Product?

- **Top Industries:** Information Technology and Services
- **Company Size:** 43% Small, 36% Medium

#### What Do G2 Reviewers Say About Google Cloud AutoML?

_AI-generated summary from verified user reviews_

##### Pros

- Users appreciate the **seamless AI integration** of Google Cloud AutoML, enhancing productivity without needing deep ML knowledge.
- Users appreciate the **ease of use** of Google Cloud AutoML, enabling quick training of models without deep expertise.
- Users appreciate the **easy integrations** of Google Cloud AutoML, enhancing their machine learning experience effortlessly.
- Users appreciate the **seamless integration** of Google Cloud AutoML, enhancing usability and collaboration with other Google services.
- Users appreciate the **intuitive interface** of Google Cloud AutoML, making machine learning accessible without deep expertise.

##### Cons

- Users find the **cost prohibitive** for smaller projects or students, making it less accessible for them.
- The **pricing can be expensive** for small projects or students, limiting accessibility and usage for some users.

#### What Are Recent G2 Reviews of Google Cloud AutoML?

**["Google Cloud AutoML Simplifies Model Building and Deployment"](https://www.g2.com/survey_responses/google-cloud-automl-review-13278558)**

**Rating:** 4.5/5.0 stars

_— LOKESH G._

[Read full review](https://www.g2.com/survey_responses/google-cloud-automl-review-13278558)

**["Google Cloud AutoML Helps Us Train ML Models Faster and More Efficiently"](https://www.g2.com/survey_responses/google-cloud-automl-review-13334379)**

**Rating:** 4.0/5.0 stars

_— Kennedy J._

[Read full review](https://www.g2.com/survey_responses/google-cloud-automl-review-13334379)

#### What Are G2 Users Discussing About Google Cloud AutoML?

- [What is Google Cloud AutoML used for?](https://www.g2.com/discussions/what-is-google-cloud-automl-used-for)

## FAQs About Low-Code Machine Learning Platforms Software

Generated using AI

Last updated: June 3, 2026

### Which low code ml platform gives the best balance of price and automation features

Based on G2 reviews, these products are the most consistently mentioned for low-code automation and model-building workflows.

- [Alteryx](https://www.g2.com/products/alteryx) — automated data prep and reporting.
- [SAS Viya](https://www.g2.com/products/sas-sas-viya) — automated ML pipelines and dashboards.
- [Dataiku](https://www.g2.com/products/dataiku) — visual workflows with coding flexibility.
- [Altair AI Studio](https://www.g2.com/products/rapidminer-studio) — AutoML with visual workflow design.

### Low code machine learning platforms that integrate well with existing web apps and APIs

According to verified users, integration strength shows up in a few recurring patterns: API access, compatibility with existing cloud or business systems, and the ability to move data between tools without heavy custom work. Recent reviewers highlight platforms that connect to multiple data sources, support open languages or APIs, and fit into broader enterprise workflows. They also note that integration quality can vary by connector and environment, especially when teams need external systems, custom pipelines, or cross-cloud support. Buyers evaluating this area should look closely at how easily a platform handles ingestion, deployment, and workflow handoffs using the systems their team already depends on.

### What���s the easiest low code ml platform for a non data scientist to build models

According to verified users, ease for non-data scientists usually comes from drag-and-drop design, guided workflows, and the option to build models without writing code. Recent G2 reviews frequently mention beginner-friendly interfaces, visual pipelines, AutoML support, and smoother onboarding when platforms balance simplicity with room to grow. Reviewers also point out that many products are approachable at first but still have learning curves once projects become more complex or datasets get larger. For buyers, the most practical signal is whether non-technical users can prepare data, test models, and share outputs without relying on specialists for every step of the workflow.

### What are the best low code machine learning platforms

Based on G2 reviews, these products appear most often in recent feedback for low-code machine learning use cases.

- [Alteryx](https://www.g2.com/products/alteryx) — drag-and-drop data prep and automation.
- [SAS Viya](https://www.g2.com/products/sas-sas-viya) — visual modeling and governed analytics.
- [Dataiku](https://www.g2.com/products/dataiku) — collaborative end-to-end ML workflows.
- [Altair AI Studio](https://www.g2.com/products/rapidminer-studio) — no-code modeling for engineering data.

### What features define modern low-code machine learning platforms

According to verified users, modern low-code machine learning platforms are defined by visual workflow building, automated model selection, data preparation tools, and the flexibility to mix no-code steps with code when needed. Recent reviews also repeatedly mention dashboarding, reporting, deployment support, model monitoring, collaboration, and integration with common data sources or APIs. Buyers should also weigh practical usability signals that come up often in reviews, such as onboarding experience, interface clarity, workflow speed, and how well the platform supports both technical and non-technical contributors. The strongest products tend to reduce manual work while keeping model building, data movement, and operational handoffs in one environment.

### [Gemini Enterprise Agent Platform](https://www.g2.com/products/gemini-enterprise-agent-platform/reviews)

Google Cloud's comprehensive platform for developers to build, scale, govern and optimize agents and models. It's a single destination for technical teams to build agents that can transform enterprise applications and workflows into powerful agentic systems.

**Average Rating:** 4.3/5.0

**Total Reviews:** 729

#### Who Is the Company Behind Gemini Enterprise Agent Platform?

- **Seller:** [Google](https://www.g2.com/sellers/google)
- **Year Founded:** 1998
- **HQ Location:** Mountain View, CA
- **Twitter:** @google  
31,899,995 Twitter followers
- **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=fe4a5936665c9702418dd53c477fef5a7baea08078bb117ed67e966fc581b9ec&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2F1441%2F&secure%5Burl_type%5D=linkedin_company_website)  
341,888 employees on LinkedIn®
- **Ownership:** NASDAQ:GOOG

#### Who Uses This Product?

- **Who Uses This:** Software Engineer, Data Scientist
- **Top Industries:** Computer Software, Information Technology and Services
- **Company Size:** 42% Small, 29% Large

#### What Do G2 Reviewers Say About Gemini Enterprise Agent Platform?

_AI-generated summary from verified user reviews_

##### Pros

- Users appreciate the **ease of use** of the Gemini Enterprise Agent Platform, highlighting its beginner-friendly interface and intuitive design.
- Users value the **multimodal capabilities** of Gemini, enhancing productivity in software development and automation projects.
- Users appreciate the **multimodal capabilities** of Gemini, enhancing productivity by understanding text, images, code, and documents together.
- Users value the **multimodal capabilities** of Gemini, enhancing productivity in software development and automation projects.
- Users appreciate the **easy integrations** in Gemini Enterprise Agent, which streamline workflows and enhance productivity.

##### Cons

- Users find the platform **expensive** , especially when considering resource usage and challenging documentation.
- Users find the **learning curve steep** with Gemini Enterprise Agent Platform, due to its numerous complex components and configurations.
- Users find the **complex pricing structure** of Gemini Enterprise Agent Platform confusing and difficult to navigate.
- Users find the **complex pricing structure** of Gemini Enterprise Agent challenging and suggest simplifying it for clarity.
- Users find the **difficult learning curve** of Gemini Enterprise Agent Platform overwhelming, especially with advanced features and integrations.

#### What Are Recent G2 Reviews of Gemini Enterprise Agent Platform?

**["Easy AI Agent Creation"](https://www.g2.com/survey_responses/gemini-enterprise-agent-platform-review-13193916)**

**Rating:** 4.5/5.0 stars

_— Belhaje A._

[Read full review](https://www.g2.com/survey_responses/gemini-enterprise-agent-platform-review-13193916)

**["Helped us automate routine work and save hours every week"](https://www.g2.com/survey_responses/gemini-enterprise-agent-platform-review-13212825)**

**Rating:** 4.5/5.0 stars

_— Pavan Simhadri D._

[Read full review](https://www.g2.com/survey_responses/gemini-enterprise-agent-platform-review-13212825)

#### What Are G2 Users Discussing About Gemini Enterprise Agent Platform?

- [What is Google Cloud AI Platform used for?](https://www.g2.com/discussions/what-is-google-cloud-ai-platform-used-for) - 5 comments, 5 upvotes
- [What software libraries does cloud ML engine support?](https://www.g2.com/discussions/what-software-libraries-does-cloud-ml-engine-support) - 4 comments, 5 upvotes
- [How do I use Google cloud platform for machine learning?](https://www.g2.com/discussions/how-do-i-use-google-cloud-platform-for-machine-learning)
- [Is Google Cloud AI free?](https://www.g2.com/discussions/is-google-cloud-ai-free)
- [What is Google AI platform?](https://www.g2.com/discussions/what-is-google-ai-platform) - 3 comments, 3 upvotes

### [KNIME](https://www.g2.com/products/knime-analytics-platform/reviews)

KNIME helps everybody make sense of data. Its free and open source KNIME Analytics Platform enables anyone — whether they come from a business, technical or data background — to intuitively work with data, every day. KNIME Business Hub is the commercial complement to KNIME Analytics Platform and enables users to collaborate on data science and share insights across the organization. Together, the products support the complete data science lifecycle, allowing teams at all levels of analytics readiness to support the operationalization of data and to build a scalable data science practice.

**Average Rating:** 4.5/5.0

**Total Reviews:** 117

#### Who Is the Company Behind KNIME?

- **Seller:** [KNIME](https://www.g2.com/sellers/knime)
- **Company Website:** knime.com
- **Year Founded:** 2008
- **HQ Location:** Zurich, Switzerland
- **Twitter:** @knime  
7,998 Twitter followers
- **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=f7a516bbe2dc4d0e292ed78e5e957b79bbec1dfe489fa8be92a344d651ca0810&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2F692207%3Ftrk%3Dtyah%26trkInfo%3DclickedVertical%253Acompany%252CclickedEntityId%253A692207%252Cidx%253A2-1-4%252CtarId%253A1454002156993%252Ctas%253Aknime&secure%5Burl_type%5D=linkedin_company_website)  
245 employees on LinkedIn®

#### Who Uses This Product?

- **Top Industries:** Information Technology and Services, Higher Education
- **Company Size:** 39% Large, 34% Medium

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

_AI-generated summary from verified user reviews_

##### Pros

- Users find KNIME's **ease of use** enhances productivity, enabling even non-technical individuals to build workflows effortlessly.
- Users find KNIME's **coding ease** remarkable, enabling effortless workflows without requiring extensive technical skills.
- Users find KNIME to be an **easy-to-learn platform** , enabling quick workflow creation without coding skills.
- Users find KNIME easy to learn and start delivering results, thanks to its **intuitive visual workflow interface**.
- Users love the **easy and intuitive data visualization** capabilities of KNIME, enhancing understanding and communication of data insights.

##### Cons

- Users find the **initial learning curve challenging** , especially for those unfamiliar with data science concepts and visual programming.
- Users face **memory usage issues** with KNIME, leading to slow performance, especially with larger files and operations.
- Users report **storage limitations** with KNIME, leading to performance issues and difficulties with large datasets.
- Users note that **data management issues** hinder their experience, especially with file handling and certain databases.
- Users find the **lack of learning resources** for KNIME to be a significant barrier to effective usage.

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

**["KNIME Makes Data Analysis Simple, Fast, and Efficient"](https://www.g2.com/survey_responses/knime-review-13349073)**

**Rating:** 4.0/5.0 stars

_— Md Sameer R._

[Read full review](https://www.g2.com/survey_responses/knime-review-13349073)

**["Powerful, Free Data ETL with an Intuitive Drag-and-Drop Interface"](https://www.g2.com/survey_responses/knime-review-13241518)**

**Rating:** 4.5/5.0 stars

_— Jared W._

[Read full review](https://www.g2.com/survey_responses/knime-review-13241518)

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

- [What is KNIME Analytics Platform used for?](https://www.g2.com/discussions/what-is-knime-analytics-platform-used-for)
- [Is Knime easy to use?](https://www.g2.com/discussions/is-knime-easy-to-use) - 1 comment
- [How do I use Knime Analytics?](https://www.g2.com/discussions/how-do-i-use-knime-analytics)
- [Is Knime any good?](https://www.g2.com/discussions/is-knime-any-good)
- [What is Knime analytics platform?](https://www.g2.com/discussions/what-is-knime-analytics-platform)

### [Altair AI Studio](https://www.g2.com/products/rapidminer-studio/reviews)

Altair AI Studio (formerly RapidMiner Studio) is a data science tool that anyone can use to design and prototype highly explainable AI and machine learning models that help build trust throughout an organization. Altair AI Studio includes: - Full generative AI functionality with access to hundreds of large language models (LLMs). - Intuitive and powerful drag-and-drop canvases that give users code-like control without complexity. - Award-winning auto ML with automated clustering, predictive modeling, feature engineering, and time series forecasting. - Data connectivity, exploration, and preparation. - Deploy and manage AI projects and models at enterprise scale. - Collaborate with team members in the same environment without having to worry about overwriting each other's work. - Unify the entire data science lifecycle from data exploration and machine learning to model operations and visualization and deploy in the cloud. Altair AI Studio helps users make powerful insights accessible to the entire organization and can scale seamlessly for users and enterprises. Altair AI studio enables organizations to derive significant value from AI with minimal cost and operational impact.

**Average Rating:** 4.6/5.0

**Total Reviews:** 507

#### Who Is the Company Behind Altair AI Studio?

- **Seller:** [Altair](https://www.g2.com/sellers/altair-186799f5-3238-493f-b3ad-b8cac484afd7)
- **Company Website:** www.altair.com
- **Year Founded:** 1985
- **HQ Location:** Troy, MI
- **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=bc40bf531df2ca2bbac9a40324b3bf94e73c14f7ec2b90b9b64c29defbdba7b7&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2F8323%2F&secure%5Burl_type%5D=linkedin_company_website)  
2,774 employees on LinkedIn®
- **Ownership:** NASDAQ:ALTR

#### Who Uses This Product?

- **Who Uses This:** Student, Professor
- **Top Industries:** Higher Education, Education Management
- **Company Size:** 42% Small, 31% Large

#### What Do G2 Reviewers Say About Altair AI Studio?

_AI-generated summary from verified user reviews_

##### Pros

- Users praise the **ease of use** of Altair AI Studio, finding its interface intuitive and beneficial for data tasks.
- Users value the **no-code machine learning capabilities** of Altair AI Studio, facilitating easy model creation and analysis.
- Users value the **seamless AI integration** of Altair AI Studio, enhancing decision-making and improving efficiency across organizations.
- Users appreciate the **advanced machine learning and data analytics** in Altair AI Studio for smarter decision-making and efficiency.
- Users value the **automation capabilities** of Altair AI Studio, enhancing efficiency in data processing and decision making.

##### Cons

- Users find the **complexity** of Altair AI Studio challenging, particularly with language support and integrating legacy systems.
- Users experience **slower performance** when handling large datasets in Altair AI Studio, affecting their efficiency and experience.
- Users experience **slow performance** when handling large datasets, leading to slowdowns or freezing during usage.
- Users find the **complexity issues** of Altair AI Studio frustrating, especially due to limited support resources.
- Users find the **complex usage** of Altair AI Studio challenging, especially due to limited documentation and steep learning curve.

#### What Are Recent G2 Reviews of Altair AI Studio?

**["Empowers AI Development with Ease"](https://www.g2.com/survey_responses/altair-ai-studio-review-13205589)**

**Rating:** 4.0/5.0 stars

_— Vivek C._

[Read full review](https://www.g2.com/survey_responses/altair-ai-studio-review-13205589)

**["Great tool for easy data analysis and testing of AI models"](https://www.g2.com/survey_responses/altair-ai-studio-review-12942088)**

**Rating:** 4.5/5.0 stars

_— Sanjeet S._

[Read full review](https://www.g2.com/survey_responses/altair-ai-studio-review-12942088)

#### What Are G2 Users Discussing About Altair AI Studio?

- [What is RapidMiner used for?](https://www.g2.com/discussions/what-is-rapidminer-used-for) - 1 comment
- [What are the data mining tools?](https://www.g2.com/discussions/what-are-the-data-mining-tools)
- [Is RapidMiner open source?](https://www.g2.com/discussions/is-rapidminer-open-source)
- [How do you use the Rapid Miner?](https://www.g2.com/discussions/how-do-you-use-the-rapid-miner)
- [Is Rapid Miner legit?](https://www.g2.com/discussions/is-rapid-miner-legit)

### [Qlik Predict](https://www.g2.com/products/qlik-predict/reviews)

Qlik AutoML (automated machine learning) brings AI-generated machine learning models and predictive analytics directly to your organization’s larger community of analytics users and teams, in a simple user experience focused on augmenting their intuition through machine intelligence. With AutoML, you can easily generate machine learning models, make predictions, and plan decisions – all within an intuitive, code-free user interface. Machine learning (ML) is a branch of artificial intelligence (AI) focused on the process of recognizing patterns in historical data to predict outcomes in the future. ML uses historically observed data as an input, applies a mathematical process against that data, and creates an output called a machine learning model based on patterns in historical data. This model can then be used to make future predictions and test scenarios.

**Average Rating:** 4.4/5.0

**Total Reviews:** 78

#### Who Is the Company Behind Qlik Predict?

- **Seller:** [Qlik](https://www.g2.com/sellers/qlik)
- **Year Founded:** 1993
- **HQ Location:** Radnor, PA
- **Twitter:** @qlik  
64,130 Twitter followers
- **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=cd147c14c7daf80e08bf113d2cae24ca3693e49a87c147350c31e8b0961c7297&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2F10162%2F&secure%5Burl_type%5D=linkedin_company_website)  
4,551 employees on LinkedIn®
- **Phone:** 1 (888) 994-9854

#### Who Uses This Product?

- **Who Uses This:** Data Analyst
- **Top Industries:** Information Technology and Services, Computer Software
- **Company Size:** 38% Large, 31% Small

#### What Do G2 Reviewers Say About Qlik Predict?

_AI-generated summary from verified user reviews_

##### Pros

- Users value the **automation features** of Qlik Predict, enabling seamless machine learning model creation and integration without coding.
- Users enjoy the **ease of use** of Qlik Predict, benefiting from a friendly no-code interface and intuitive automation.
- Users value the **intuitive AI integration** of Qlik Predict, enabling quick, no-code machine learning model deployment.
- Users appreciate the **user-friendly, no-code approach** of Qlik Predict, enabling accessible machine learning for everyone.
- Users value the **user-friendly AI capabilities** of Qlik Predict, enabling easy deployment of predictive models without coding.

##### Cons

- Users note the **limited customization** of Qlik Predict, which may hinder advanced users seeking more control over their models.
- Users experience **deployment issues** due to limited model flexibility and integration options within Qlik Predict.
- Users note the **lack of features** in Qlik Predict, particularly regarding customization and deployment flexibility.
- Users find the **required knowledge of data science** a barrier, limiting optimizations and effective model interpretation.
- Users note the **limited flexibility** of Qlik Predict, particularly due to its no-code approach and ecosystem restrictions.

#### What Are Recent G2 Reviews of Qlik Predict?

**["Qlik AutoML"](https://www.g2.com/survey_responses/qlik-predict-review-11001365)**

**Rating:** 4.0/5.0 stars

_— Anju P._

[Read full review](https://www.g2.com/survey_responses/qlik-predict-review-11001365)

**["Describe your experience in one short sentence."](https://www.g2.com/survey_responses/qlik-predict-review-11007278)**

**Rating:** 5.0/5.0 stars

_— Cristian C._

[Read full review](https://www.g2.com/survey_responses/qlik-predict-review-11007278)

#### What Are G2 Users Discussing About Qlik Predict?

- [How does the NZXT Kraken work?](https://www.g2.com/discussions/how-does-the-nzxt-kraken-work)
- [Is the NZXT Kraken water cooling?](https://www.g2.com/discussions/is-the-nzxt-kraken-water-cooling)
- [Which NZXT Kraken is the best?](https://www.g2.com/discussions/which-nzxt-kraken-is-the-best)
- [Does the NZXT Kraken come with fans?](https://www.g2.com/discussions/does-the-nzxt-kraken-come-with-fans)

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

Clarifai is a leader in AI orchestration and development, helping organizations, teams, and developers build, deploy, orchestrate, and operationalize AI at scale. Clarifai’s cutting-edge AI workflow orchestration platform leverages today's modern AI technologies like Large Language Models (LLMs), Large Vision Models (LVMs), and Retrieval Augmented Generation (RAG), data labeling, inference, and more, and is available in cloud, on-premises, or hybrid environments. Founded in 2013, Clarifai has been used to build more than 1.5 million AI models with more than 400,000 users in 170 countries. Learn more at www.clarifai.com.

**Average Rating:** 4.3/5.0

**Total Reviews:** 74

#### Who Is the Company Behind Clarifai?

- **Seller:** [Clarifai](https://www.g2.com/sellers/clarifai)
- **Year Founded:** 2013
- **HQ Location:** Wilmington, Delaware
- **Twitter:** @clarifai  
10,922 Twitter followers
- **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/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 employees on LinkedIn®

#### Who Uses This Product?

- **Top Industries:** Computer Software, Information Technology and Services
- **Company Size:** 61% Small, 29% Medium

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

_AI-generated summary from verified user reviews_

##### Pros

- Users appreciate Clarifai for its **impressive models and flexibility** , enhancing image and video recognition effectively.
- Users value the **easy AI tools** of Clarifai, enabling fast and accurate image and video recognition effortlessly.
- Users value the **model variety** in Clarifai, enabling tailored solutions with impressive flexibility and ease of integration.
- Users appreciate the **cutting-edge AI integration** of Clarifai, enabling accurate image and video recognition through customizable models.
- Users appreciate the **cutting-edge AI capabilities** of Clarifai, especially for image and video recognition tasks.

##### Cons

- Users find the platform **expensive** for small-scale developers, as costs can accumulate quickly with usage.
- Users find the **complexity of setup and documentation** challenging, especially for newcomers to the platform.
- Users find the **learning curve steep** for Clarifai, particularly for those new to machine learning platforms.
- Users face a **lack of resources** , hindering accessibility for small-scale developers and non-profits seeking affordable options.
- Users often find the **poor documentation** of Clarifai lacking detail, hindering their ability to maximize its features.

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

**["Clarifai’s Flexible Model Workflows and Powerful Multimodal Platform"](https://www.g2.com/survey_responses/clarifai-review-13279085)**

**Rating:** 4.5/5.0 stars

_— Subhashree S._

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

**["Powerful AI Platform for Computer Vision and Multimodal Workflows"](https://www.g2.com/survey_responses/clarifai-review-13188111)**

**Rating:** 4.0/5.0 stars

_— Jeni J._

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

### [IBM watsonx.ai](https://www.g2.com/products/ibm-watsonx-ai/reviews)

Watsonx.ai is part of the IBM watsonx platform that brings together new generative AI capabilities, powered by foundation models and traditional machine learning into a powerful studio spanning the AI lifecycle. With watsonx.ai, you can build, train, validate, tune and deploy generative AI, foundation models and machine learning capabilities with ease and build AI applications in a fraction of the time with a fraction of the data.

**Average Rating:** 4.4/5.0

**Total Reviews:** 142

#### Who Is the Company Behind IBM watsonx.ai?

- **Seller:** [IBM](https://www.g2.com/sellers/ibm)
- **Company Website:** www.ibm.com
- **Year Founded:** 1911
- **HQ Location:** Armonk, New York, United States
- **Twitter:** @IBMSecurity  
74,660 Twitter followers
- **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=14b544adaece4fdbc987f1d7f7028048c22259946811200cc751263825586af9&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2F1009%2F&secure%5Burl_type%5D=linkedin_company_website)  
328,202 employees on LinkedIn®

#### Who Uses This Product?

- **Who Uses This:** Consultant
- **Top Industries:** Information Technology and Services, Computer Software
- **Company Size:** 41% Small, 32% Large

#### What Do G2 Reviewers Say About IBM watsonx.ai?

_AI-generated summary from verified user reviews_

##### Pros

- Users praise the **ease of use** of IBM watsonx.ai, facilitating straightforward integration and model development.
- Users value the **wide range of model types** in IBM watsonx.ai, enhancing flexibility and efficiency in development.
- Users appreciate the **user-friendly platform** that simplifies building and deploying AI models efficiently and effectively.
- Users appreciate the **user-friendly AI studio** of IBM watsonx.ai, enabling efficient chatbot creation with minimal coding.
- Users appreciate the **enterprise-grade AI** of IBM watsonx.ai, which integrates seamlessly for practical, reliable business solutions.

##### Cons

- Users find the **difficult learning** curve challenging, indicating the need for clearer documentation and better onboarding support.
- Users find the **complexity** of IBM watsonx.ai challenging, especially for beginners and small teams seeking easier solutions.
- Users find the **steep learning curve** of IBM watsonx.ai challenging, making it less approachable for non-technical teams.
- Users express concerns about the **high costs** of IBM watsonx.ai, finding it challenging and not budget-friendly for small teams.
- Users find the **complex setup** of IBM watsonx.ai challenging, especially for newcomers and small teams seeking ease of use.

#### What Are Recent G2 Reviews of IBM watsonx.ai?

**["IBM watsonx.ai Makes It Easy to Bring Foundation Models into Real Business Workflows"](https://www.g2.com/survey_responses/ibm-watsonx-ai-review-13271627)**

**Rating:** 4.5/5.0 stars

_— Balaji S._

[Read full review](https://www.g2.com/survey_responses/ibm-watsonx-ai-review-13271627)

**["Unified, Governed AI Studio with Strong Performance and Seamless IBM Integrations"](https://www.g2.com/survey_responses/ibm-watsonx-ai-review-13184421)**

**Rating:** 4.0/5.0 stars

_— Manan S._

[Read full review](https://www.g2.com/survey_responses/ibm-watsonx-ai-review-13184421)

### [DataRobot](https://www.g2.com/products/datarobot/reviews)

DataRobot’s enterprise AI platform democratizes data science with end-to-end automation for building, deploying, and managing machine learning models. This platform maximizes business value by delivering AI at scale and continuously optimizing performance over time. The company’s proven combination of cutting edge software and world-class AI implementation, training, and support services, empowers any organization – regardless of size, industry, or resources – to drive better business outcomes with AI.

**Average Rating:** 4.4/5.0

**Total Reviews:** 33

#### Who Is the Company Behind DataRobot?

- **Seller:** [DataRobot](https://www.g2.com/sellers/datarobot)
- **Year Founded:** 2012
- **HQ Location:** Boston, Massachusetts
- **Twitter:** @DataRobot  
19,225 Twitter followers
- **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=89c9e24da6d788dc433c6745ca09b189b87261921021a529df2781df784a2983&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2F2672915%2F&secure%5Burl_type%5D=linkedin_company_website)  
886 employees on LinkedIn®

#### Who Uses This Product?

- **Top Industries:** Computer Software, Financial Services
- **Company Size:** 50% Small, 29% Large

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

**["End-to-End AI Lifecycle in One Platform—Fast Experimentation to Reliable Production"](https://www.g2.com/survey_responses/datarobot-review-13334289)**

**Rating:** 4.5/5.0 stars

_— LOKESH G._

[Read full review](https://www.g2.com/survey_responses/datarobot-review-13334289)

**["DataRobot Streamlines Experimentation to Deployment with Helpful Automated Modeling"](https://www.g2.com/survey_responses/datarobot-review-13346402)**

**Rating:** 4.0/5.0 stars

_— Nourhan A._

[Read full review](https://www.g2.com/survey_responses/datarobot-review-13346402)

### [Pecan](https://www.g2.com/products/pecan/reviews)

Pecan AI is a predictive analytics platform that helps business teams understand what’s likely to happen next, while there is still time to act. With Pecan’s Predictive AI Agent, teams can turn business questions into reliable predictions for use cases like customer churn, demand forecasting, and lifetime value, without relying on long, complex data science projects. The platform automatically handles data preparation, feature engineering, modeling, validation, and delivery, and provides transparent, explainable predictions that integrate into tools like Salesforce, HubSpot, Snowflake, and BI systems to drive real business outcomes.

**Average Rating:** 4.7/5.0

**Total Reviews:** 39

#### Who Is the Company Behind Pecan?

- **Seller:** [Pecan.ai](https://www.g2.com/sellers/pecan-ai)
- **Company Website:** www.pecan.ai
- **Year Founded:** 2018
- **HQ Location:** US, Israel
- **Twitter:** @pecan\_ai  
1,135 Twitter followers
- **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=2d78b1b29b723df847ea1555f4d071a4e25f46a72c388bbee31fa8710688d47a&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2Fpecan-ai%2F&secure%5Burl_type%5D=linkedin_company_website)  
89 employees on LinkedIn®

#### Who Uses This Product?

- **Top Industries:** Retail, Computer Software
- **Company Size:** 52% Medium, 21% Large

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

_AI-generated summary from verified user reviews_

##### Pros

- Users praise the **ease of use** of Pecan, enabling quick model building without requiring deep expertise.
- Users praise the **excellent customer support** from Pecan, which assists them effectively throughout their predictive modeling journey.
- Users love the **speed of development** with Pecan, accelerating model deployment from months to weeks effortlessly.
- Users value Pecan's **effective problem-solving** capabilities and exceptional support for leveraging data into actionable insights.
- Users find Pecan's **implementation ease** exceptional, significantly accelerating model development with ample support and guidance.

##### Cons

- Users face a **learning difficulty** that requires an intermediate understanding of SQL and data structures for effective use.
- Users express a desire for **deeper control over model selection** and customization options in Pecan.
- Users feel the **limited features** of Pecan restrict customization and control over model selection and optimization metrics.
- Users face a **steep learning curve** with Pecan, particularly in grasping data structure and SQL requirements.
- Users feel the **limited customization** restricts their ability to fine-tune models for specific use cases effectively.

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

**["Pecan Makes Forecasting Easy, Plus Outstanding Team Support"](https://www.g2.com/survey_responses/pecan-review-13235577)**

**Rating:** 5.0/5.0 stars

_— Verified User in Manufacturing_

[Read full review](https://www.g2.com/survey_responses/pecan-review-13235577)

**["Intuitive Platform with Exceptional Support"](https://www.g2.com/survey_responses/pecan-review-12654479)**

**Rating:** 5.0/5.0 stars

_— J G._

[Read full review](https://www.g2.com/survey_responses/pecan-review-12654479)

### [Amazon SageMaker](https://www.g2.com/products/amazon-sagemaker/reviews)

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.

**Average Rating:** 4.3/5.0

**Total Reviews:** 54

#### Who Is the Company Behind Amazon SageMaker?

- **Seller:** [Amazon Web Services (AWS)](https://www.g2.com/sellers/amazon-web-services-aws-3e93cc28-2e9b-4961-b258-c6ce0feec7dd)
- **Year Founded:** 2006
- **HQ Location:** Seattle, WA
- **Twitter:** @awscloud  
2,232,483 Twitter followers
- **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/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 employees on LinkedIn®
- **Ownership:** NASDAQ: AMZN

#### Who Uses This Product?

- **Top Industries:** Information Technology and Services, Computer Software
- **Company Size:** 33% Medium, 33% Large

#### What Do G2 Reviewers Say About Amazon SageMaker?

_AI-generated summary from verified user reviews_

##### Pros

- Users find the **ease of use** of Amazon SageMaker exceptional, allowing quick adaptation and straightforward model training.
- Users value the **seamless AI integration** of Amazon SageMaker, streamlining the entire machine learning lifecycle efficiently.
- Users appreciate the **superior computing power** of Amazon SageMaker, significantly reducing model training time and enhancing productivity.
- Users praise Amazon SageMaker for its **efficient training process** , drastically reducing model training time and simplifying functionality.
- Users highlight the **fast processing** of Amazon SageMaker, significantly reducing model training time and enhancing productivity.

##### Cons

- Users find that Amazon SageMaker can become **expensive** , particularly with long-running jobs and complex pricing structures.
- Users find the **complex pricing structure** of Amazon SageMaker can lead to unexpected costs and confusion.
- Users find the **complexity of pricing** in SageMaker challenging, often leading to unexpected costs and confusion.
- Users find the **steep learning curve** for Amazon SageMaker challenging, particularly for those new to AWS services.
- Users find a **difficult learning curve** during the initial setup of Amazon SageMaker, impacting usability.

#### What Are Recent G2 Reviews of Amazon SageMaker?

**["End-to-End ML Platform That Streamlines the Full Lifecycle"](https://www.g2.com/survey_responses/amazon-sagemaker-review-13180609)**

**Rating:** 4.5/5.0 stars

_— Atharva P._

[Read full review](https://www.g2.com/survey_responses/amazon-sagemaker-review-13180609)

**["Fully Managed End-to-End ML in AWS with Powerful Distributed Training"](https://www.g2.com/survey_responses/amazon-sagemaker-review-12853074)**

**Rating:** 4.0/5.0 stars

_— Hem J._

[Read full review](https://www.g2.com/survey_responses/amazon-sagemaker-review-12853074)

#### What Are G2 Users Discussing About Amazon SageMaker?

- [What is Amazon SageMaker used for?](https://www.g2.com/discussions/what-is-amazon-sagemaker-used-for)
- [Is AWS SageMaker good?](https://www.g2.com/discussions/is-aws-sagemaker-good) - 1 upvote
- [Who uses SageMaker?](https://www.g2.com/discussions/who-uses-sagemaker)
- [How do you use Amazon SageMaker?](https://www.g2.com/discussions/how-do-you-use-amazon-sagemaker)
- [What does Amazon SageMaker do?](https://www.g2.com/discussions/what-does-amazon-sagemaker-do)

### [Azure Machine Learning](https://www.g2.com/products/microsoft-azure-machine-learning/reviews)

Azure Machine Learning is an enterprise-grade service that facilitates the end-to-end machine learning lifecycle, enabling data scientists and developers to build, train, and deploy models efficiently. Key Features and Functionality: - Data Preparation: Quickly iterate data preparation on Apache Spark clusters within Azure Machine Learning, interoperable with Microsoft Fabric. - Feature Store: Increase agility in shipping your models by making features discoverable and reusable across workspaces. - AI Infrastructure: Take advantage of purpose-built AI infrastructure uniquely designed to combine the latest GPUs and InfiniBand networking. - Automated Machine Learning: Rapidly create accurate machine learning models for tasks including classification, regression, vision, and natural language processing. - Responsible AI: Build responsible AI solutions with interpretability capabilities. Assess model fairness through disparity metrics and mitigate unfairness. - Model Catalog: Discover, fine-tune, and deploy foundation models from Microsoft, OpenAI, Hugging Face, Meta, Cohere, and more using the model catalog. - Prompt Flow: Design, construct, evaluate, and deploy language model workflows with prompt flow. - Managed Endpoints: Operationalize model deployment and scoring, log metrics, and perform safe model rollouts. Primary Value and Solutions Provided: Azure Machine Learning accelerates time to value by streamlining prompt engineering and machine learning model workflows, facilitating faster model development with powerful AI infrastructure. It streamlines operations by enabling reproducible end-to-end pipelines and automating workflows with continuous integration and continuous delivery (CI/CD). The platform ensures confidence in development through unified data and AI governance with built-in security and compliance, allowing compute to run anywhere for hybrid machine learning. Additionally, it promotes responsible AI by providing visibility into models, evaluating language model workflows, and mitigating fairness, biases, and harm with built-in safety systems.

**Average Rating:** 4.3/5.0

**Total Reviews:** 87

#### Who Is the Company Behind Azure Machine Learning?

- **Seller:** [Microsoft](https://www.g2.com/sellers/microsoft)
- **Year Founded:** 1975
- **HQ Location:** Redmond, Washington
- **Twitter:** @microsoft  
13,091,739 Twitter followers
- **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=9458f51bd6ded48ad432a804f19ad736469f007787569b63827154231c315630&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2Fmicrosoft%2F&secure%5Burl_type%5D=linkedin_company_website)  
231,632 employees on LinkedIn®
- **Ownership:** MSFT

#### Who Uses This Product?

- **Who Uses This:** Software Engineer
- **Top Industries:** Information Technology and Services, Computer Software
- **Company Size:** 40% Large, 33% Small

#### What Do G2 Reviewers Say About Azure Machine Learning?

_AI-generated summary from verified user reviews_

##### Pros

- Users find Azure Machine Learning's **ease of use** beneficial for implementing and managing machine learning projects effectively.
- Users appreciate the **scalability and integration** of Azure Machine Learning, enhancing deployment and management of models seamlessly.
- Users value the **excellent customer support** of Azure Machine Learning, appreciating the comprehensive documentation and community assistance.
- Users appreciate the **ease of use and robust data management features** that help in organizing and analyzing data effectively.
- Users value the **efficient environment** of Azure Machine Learning for launching and monitoring machine learning jobs seamlessly.

##### Cons

- Users report a challenging **learning curve** with Azure Machine Learning, requiring time to master its tools and interface.
- Users find Azure Machine Learning's **difficult navigation** frustrating, often struggling to locate options and understand workflows.
- Users find the **disordered user interface** of Azure Machine Learning frustrating, complicating their navigation and task completion.
- Users find the **complex interface** of Azure Machine Learning challenging, particularly due to non-intuitive navigation and missing features.
- Users find the **difficult learning** aspect challenging, especially those new to Azure or machine learning concepts.

#### What Are Recent G2 Reviews of Azure Machine Learning?

**["Cost-Efficient Medical Data Integration Backed by Great Support"](https://www.g2.com/survey_responses/azure-machine-learning-review-12845990)**

**Rating:** 5.0/5.0 stars

_— Giridharan U._

[Read full review](https://www.g2.com/survey_responses/azure-machine-learning-review-12845990)

**["An Enterprise-Grade Way to Operationalize ML"](https://www.g2.com/survey_responses/azure-machine-learning-review-12853548)**

**Rating:** 4.0/5.0 stars

_— Vytas J._

[Read full review](https://www.g2.com/survey_responses/azure-machine-learning-review-12853548)

#### What Are G2 Users Discussing About Azure Machine Learning?

- [What is Azure Machine Learning Studio used for?](https://www.g2.com/discussions/what-is-azure-machine-learning-studio-used-for) - 1 comment
- [What type of data analysis is azure machine learning studio intended for?](https://www.g2.com/discussions/what-type-of-data-analysis-is-azure-machine-learning-studio-intended-for)
- [What are the key features of Azure Machine Learning?](https://www.g2.com/discussions/what-are-the-key-features-of-azure-machine-learning)
- [How do I use Microsoft Azure for machine learning?](https://www.g2.com/discussions/how-do-i-use-microsoft-azure-for-machine-learning)
- [What is Azure Machine Learning Studio?](https://www.g2.com/discussions/what-is-azure-machine-learning-studio)

### [Neuton AutoML](https://www.g2.com/products/neuton-automl/reviews)

Neuton (https://neuton.ai), a new AutoML solution, allows users to build compact AI models with just a few clicks and without any coding. Neuton also happens to be the most EXPLAINABLE Neural Network Framework and AutoML solution currently available on the market. It allows users to evaluate the model quality from various perspectives and interpret prediction results. Neuton Explainability Office: - Exploratory Data Analysis - Feature Importance Matrix with class granularity - Model Interpreter - Feature Influence Matrix - Validate Model on New Data - Model-to-Data Relevance Indicators historical and for every prediction - Model Quality Index - Confidence Interval - Extensive list of supported metrics with Radar Diagram

**Average Rating:** 4.5/5.0

**Total Reviews:** 17

#### Who Is the Company Behind Neuton AutoML?

- **Seller:** [Bell Integrator](https://www.g2.com/sellers/bell-integrator)
- **Year Founded:** 2003
- **HQ Location:** San Jose, CA
- **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=201e18412b72e0be88371139c75b34c49a50fa64312cd24ba575b16d2ecbb1e7&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2Fbellintegrator%2F&secure%5Burl_type%5D=linkedin_company_website)  
703 employees on LinkedIn®

#### Who Uses This Product?

- **Company Size:** 35% Small, 35% Large

#### What Are Recent G2 Reviews of Neuton AutoML?

**["A Comprehensive and Efficient Solution for Automating Machine Learning Model Development"](https://www.g2.com/survey_responses/neuton-automl-review-7623010)**

**Rating:** 4.5/5.0 stars

_— Rajesh S._

[Read full review](https://www.g2.com/survey_responses/neuton-automl-review-7623010)

**["Cloud based ML platform for everyone."](https://www.g2.com/survey_responses/neuton-automl-review-8043519)**

**Rating:** 4.5/5.0 stars

_— Abhuday T._

[Read full review](https://www.g2.com/survey_responses/neuton-automl-review-8043519)

#### What Are G2 Users Discussing About Neuton AutoML?

- [What is Neuton AutoML used for?](https://www.g2.com/discussions/what-is-neuton-automl-used-for)

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[Browse Low-Code Machine Learning Platforms Themes](/categories/low-code-machine-learning-platforms/themes)

 ![Adam Crivello](/assets/transparent-ad5be28fbcd25b7b08d2cebe1d957125437fb5407d75ee717965ad22c8808791.gif "Adam Crivello")
AC

Researched and written by [Adam Crivello](https://research.g2.com/insights/author/adam-crivello)

Updated April 9, 2026

Low-code machine learning (ML) platforms enable businesses to build, train, and deploy ML models primarily through visual or guided interfaces, using drag-and-drop tools, AutoML workflows, and wizard-style guidance to make predictive modeling and AI development accessible to business analysts, subject matter experts, and data scientists without extensive coding expertise.

### Core Capabilities of Low-Code Machine Learning Platforms

To qualify for inclusion in the Low-Code Machine Learning (ML) Platforms category, a product must:

- Provide a graphical, low-code or no-code interface to build and train custom ML models on user-provided data
- Include built-in functionality to evaluate trained models
- Offer direct deployment options from the interface, such as batch scoring, API endpoints, or managed service environments
- Support data ingestion through uploads or connectors to databases, cloud storage, or other sources
- Enable collaboration and governance through features like role-based access, project or workspace management, or auditability

### Common Use Cases for Low-Code Machine Learning Platforms

Business analysts, data scientists, and non-technical teams use low-code ML platforms to accelerate AI adoption without deep programming expertise. Common use cases include:

- Building and deploying predictive models for use cases such as churn prediction, demand forecasting, and fraud detection
- Empowering non-technical subject matter experts to contribute to ML model development using visual interfaces
- Standardizing the deployment and governance of ML models into production environments across the enterprise

### How Low-Code Machine Learning Platforms Differ from Other Tools

Unlike traditional [data science and machine learning platforms](https://www.g2.com/categories/data-science-and-machine-learning-platforms), which require extensive programming and are primarily designed for experienced data scientists, low-code ML platforms deliver end-to-end ML lifecycle functionality through a user-friendly interface. Some enterprise cloud providers offer low-code ML capabilities within broader AI ecosystems, while dedicated vendors focus solely on visual model development and deployment.

### Insights from G2 on Low-Code Machine Learning Platforms

Based on category trends on G2, the visual model builder and AutoML capabilities stand out as standout features. These platforms deliver faster time-to-model deployment and reduced dependency on data science resources as primary benefits of adoption.

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