Top Free MLOps Platforms

How Many MLOps Platforms Products Does G2 Track?

Total Products under this Category: 384

Category Stats (Nov 2026)

  • Average Rating: 4.51/5 The average rating of products in this category, based on all submitted ratings
  • Top Trending Product: Rapidminer SLC (+0.61%) - Among all products in this category, Rapidminer SLC recorded the largest rating increase compared to last month

Last updated: November 01, 2026

How Does G2 Rank MLOps Platforms Products?

Why You Can Trust G2's Software Rankings:

  • 30 Analysts and Data Experts
  • 8,100+ Authentic Reviews
  • 384+ 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 MLOps Platforms

G2 Grid® for MLOps Platforms plotting products by satisfaction and market presence

Highlighted products: Databricks, Gemini platform, Vertex Explainable AI, IBM watsonx.ai, Amazon SageMaker, Microsoft Fabric, Roboflow, and Snowflake.

Underlying data: [Grid® JSON](https://www.g2.com/categories/mlops-platforms/grids.json?focus%5B%5D=databricks&focus%5B%5D=gemini-platform&focus%5B%5D=vertex-explainable-ai&focus%5B%5D=ibm-watsonx-ai&focus%5B%5D=amazon-sagemaker&focus%5B%5D=microsoft-fabric&focus%5B%5D=roboflow&focus%5B%5D=snowflake)

Databricks

Databricks is the Data and AI company. More than 20,000 organizations worldwide — including adidas, AT&T, Bayer, Block, Mastercard, Rivian, Unilever, and 70% of the Fortune 500 — rely on the Databricks Data + AI Platform to build and scale data and AI apps, analytics, and agents. Headquartered in San Francisco with 30+ offices around the globe, Databricks offers a unified platform that includes Genie, Lakebase, Agent Bricks, Lakeflow, Lakehouse, and Unity Catalog. Founded in 2013 by the original creators of Apache Spark™, Delta Lake, MLflow and Unity Catalog, Databricks is built on an open lakehouse architecture that brings data, analytics and AI together. The platform is used by data engineers, data scientists, analysts, developers, machine learning teams, AI teams and business users to collaborate across the full data and AI lifecycle. Key Databricks capabilities include: - Data engineering: Build, automate and manage reliable batch, streaming and real-time data pipelines. - Analytics and business intelligence: Run SQL analytics, create dashboards and enable business teams to explore data. - Data governance: Discover, secure and manage data and AI assets across teams, clouds and workloads. - Machine learning and AI: Develop models, build generative AI applications and create production-grade AI agents. - Data applications: Build and deploy data-driven applications using governed enterprise data. Available across AWS, Azure and Google Cloud, Databricks helps organizations work across clouds, reduce data silos and simplify collaboration across teams and tools. Customers use Databricks for use cases such as customer personalization, fraud detection, predictive maintenance, real-time analytics, cybersecurity, healthcare research, financial risk management, supply chain optimization and AI-powered decision-making. Databricks is used across industries including financial services, healthcare and life sciences, retail, manufacturing, energy and the public sector. Organizations use the platform to modernize data infrastructure, accelerate AI adoption and turn enterprise data into business value.

Average Rating: 4.6/5.0

Total Reviews: 1,368

How Do G2 Users Rate Databricks?

  • Ease of Use: 8.8/10 (Category avg: 8.9/10)
  • Scalability: 9.0/10 (Category avg: 9.0/10)
  • Metrics: 8.8/10 (Category avg: 8.7/10)
  • Framework Flexibility: 8.8/10 (Category avg: 8.7/10)

Who Is the Company Behind Databricks?

  • Seller: Databricks Inc.
  • Company Website:
  • Year Founded: 2013
  • HQ Location: San Francisco, CA
  • Twitter: @databricks
    92,269 Twitter followers
  • LinkedIn® Page: www.linkedin.com
    14,336 employees on LinkedIn®

Who Uses This Product?

  • Who Uses This: Data Engineer, Data Analyst
  • Top Industries: Information Technology and Services, Financial Services
  • Company Size: 47% Large, 38% Medium

What Do G2 Reviewers Say About Databricks?

AI-generated summary from verified user reviews

Pros
  • Users enjoy the ease of use and extensive features of Databricks, streamlining data warehousing and machine learning tasks.
  • Users appreciate the ease of use of Databricks, enhancing their experience with its intuitive interface and efficient features.
  • Users value the seamless integrations with AWS services that enhance efficiency and support diverse business needs.
  • Users value the seamless collaboration provided by Databricks, enhancing teamwork on data projects and insights sharing.
  • Users value the effective data management features of Databricks, simplifying their workflows and enhancing decision-making.
Cons
  • Users face a steep learning curve with Databricks, as its complexity can be confusing for newcomers.
  • Users note that the cost of Databricks can be quite high, particularly for large data projects and limited free options.
  • Users find the steep learning curve of Databricks challenging, particularly for those unfamiliar with big data tools.
  • Users express frustration over missing features in Databricks, limiting its effectiveness for complex deployments and custom setups.
  • Users find the complexity of Databricks challenging, especially during initial setup and navigation of advanced features.

What Are Recent G2 Reviews of Databricks?

What Are G2 Users Discussing About Databricks?

IBM watsonx.ai

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: 151

How Do G2 Users Rate IBM watsonx.ai?

  • Ease of Use: 8.7/10 (Category avg: 8.9/10)
  • Scalability: 8.8/10 (Category avg: 9.0/10)
  • Metrics: 9.1/10 (Category avg: 8.7/10)
  • Framework Flexibility: 8.7/10 (Category avg: 8.7/10)

Who Is the Company Behind IBM watsonx.ai?

  • Seller: IBM
  • Company Website:
  • Year Founded: 1911
  • HQ Location: Armonk, New York, United States
  • Twitter: @IBMSecurity
    74,660 Twitter followers
  • LinkedIn® Page: www.linkedin.com
    344,328 employees on LinkedIn®

Who Uses This Product?

  • Who Uses This: Consultant
  • Top Industries: Information Technology and Services, Computer Software
  • Company Size: 42% Small, 33% 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 feel that improvement is needed in 3rd party integration and intelligent model optimization for better performance.

What Are Recent G2 Reviews of IBM watsonx.ai?

Roboflow

Roboflow has everything you need to build and deploy computer vision applications. Over 1,000,000 users from businesses of every size — from startups to public companies — use the company's end-to-end platform for image and video collection, organization, annotation, preprocessing, model training, and deployment. Roboflow provides tools for each step in the computer vision deployment lifecycle and integrates with your existing solutions so you can tailor your pipeline to meet your needs.

Average Rating: 4.7/5.0

Total Reviews: 161

How Do G2 Users Rate Roboflow?

  • Ease of Use: 9.3/10 (Category avg: 8.9/10)
  • Scalability: 10.0/10 (Category avg: 9.0/10)
  • Metrics: 10.0/10 (Category avg: 8.7/10)

Who Is the Company Behind Roboflow?

  • Seller: Roboflow
  • Year Founded: 2019
  • HQ Location: Remote, US
  • Twitter: @roboflow
    13,577 Twitter followers
  • LinkedIn® Page: www.linkedin.com
    144 employees on LinkedIn®

Who Uses This Product?

  • Who Uses This: Founder, Researcher
  • Top Industries: Computer Software, Research
  • Company Size: 77% Small, 15% Medium

What Do G2 Reviewers Say About Roboflow?

AI-generated summary from verified user reviews

Pros
  • Users appreciate the ease of use of Roboflow, enabling efficient model training and collaboration with a user-friendly interface.
  • Users highlight Roboflow's efficiency in dataset management, streamlining tasks and significantly saving time and reducing errors.
  • Users value the annotation efficiency of Roboflow, enjoying time savings and reduced errors in dataset management.
  • Users love how Roboflow's data labeling simplifies collaboration, annotation, and export processes, saving time and reducing errors.
  • Users appreciate the powerful and versatile features of Roboflow, making it ideal for academic and large-scale projects.
Cons
  • Users find the cost prohibitive for advanced features, especially students needing budget-friendly options.
  • Users note the limited features of Roboflow, as some advanced options require higher-tier plans and constraints exist.
  • Users experience limited functionality in Roboflow, particularly with advanced features and flexibility for complex tasks.
  • Users find annotation issues with Roboflow, especially in auto-labeling and polygon marking for complex images.
  • Users find inefficient labeling processes cumbersome, especially in team environments with a lack of automation and shortcuts.

What Are Recent G2 Reviews of Roboflow?

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Snowflake

Snowflake makes enterprise AI easy, efficient and trusted. Thousands of companies around the globe, including hundreds of the world’s largest, use Snowflake’s AI Data Cloud to share data, build applications, and power their business with AI. The era of enterprise AI is here. Learn more at snowflake.com (NYSE: SNOW).

Average Rating: 4.6/5.0

Total Reviews: 723

How Do G2 Users Rate Snowflake?

  • Ease of Use: 9.0/10 (Category avg: 8.9/10)
  • Scalability: 9.4/10 (Category avg: 9.0/10)
  • Metrics: 8.9/10 (Category avg: 8.7/10)
  • Framework Flexibility: 9.5/10 (Category avg: 8.7/10)

Who Is the Company Behind Snowflake?

  • Seller: Snowflake, Inc.
  • Company Website:
  • Year Founded: 2012
  • HQ Location: 135 Constitution Drive, Menlo Park CA
  • Twitter: @SnowflakeDB
    278 Twitter followers
  • LinkedIn® Page: www.linkedin.com
    12,574 employees on LinkedIn®

Who Uses This Product?

  • Who Uses This: Data Engineer, Data Analyst
  • Top Industries: Information Technology and Services, Computer Software
  • Company Size: 45% Medium, 42% Large

What Do G2 Reviewers Say About Snowflake?

AI-generated summary from verified user reviews

Pros
  • Users appreciate the ease of use of Snowflake, which simplifies data sharing and enhances productivity across teams.
  • Users value the seamless scalability of Snowflake, enabling efficient handling of large datasets and workload changes without performance loss.
  • Users appreciate the ease of use and efficient data integration in Snowflake for their warehousing projects.
  • Users value the reliable features and user-friendly interface of Snowflake, enhancing data management and analytics efficiency.
  • Users appreciate the excellent integrations of Snowflake, enhancing data management and deployment across multiple cloud platforms.
Cons
  • Users highlight the high costs of Snowflake, making it less accessible for smaller businesses with limited budgets.
  • Users often struggle with high costs due to unoptimized queries and inadequate cost control measures in Snowflake.
  • Users find the cost structure challenging, requiring time to optimize for efficient use of Snowflake.
  • Users find the learning curve steep, requiring training due to its complexity and overwhelming interface for beginners.
  • Users find feature limitations in Snowflake, such as lack of code blocks and restricted permissions, frustrating.

What Are Recent G2 Reviews of Snowflake?

What Are G2 Users Discussing About Snowflake?

SAS Viya

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: 793

How Do G2 Users Rate SAS Viya?

  • Ease of Use: 8.2/10 (Category avg: 8.9/10)
  • Scalability: 8.2/10 (Category avg: 9.0/10)
  • Metrics: 8.7/10 (Category avg: 8.7/10)
  • Framework Flexibility: 8.5/10 (Category avg: 8.7/10)

Who Is the Company Behind SAS Viya?

  • Seller: SAS Institute
  • Company Website:
  • Year Founded: 1976
  • HQ Location: Cary, NC
  • Twitter: @SASsoftware
    60,863 Twitter followers
  • LinkedIn® Page: www.linkedin.com
    15,122 employees on LinkedIn®

Who Uses This Product?

  • Who Uses This: Student, Biostatistician
  • Top Industries: Pharmaceuticals, Banking
  • Company Size: 34% 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?

What Are G2 Users Discussing About SAS Viya?

Dataiku

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: 256

How Do G2 Users Rate Dataiku?

  • Ease of Use: 8.6/10 (Category avg: 8.9/10)
  • Scalability: 9.1/10 (Category avg: 9.0/10)
  • Metrics: 8.7/10 (Category avg: 8.7/10)
  • Framework Flexibility: 8.6/10 (Category avg: 8.7/10)

Who Is the Company Behind Dataiku?

  • Seller: Dataiku
  • Company Website:
  • Year Founded: 2013
  • HQ Location: New York, NY
  • Twitter: @dataiku
    22,917 Twitter followers
  • LinkedIn® Page: www.linkedin.com
    1,605 employees on LinkedIn®

Who Uses This Product?

  • Who Uses This: Data Scientist, Data Analyst
  • Top Industries: Financial Services, Pharmaceuticals
  • Company Size: 58% Large, 22% Medium

What Do G2 Reviewers Say About Dataiku?

AI-generated summary from verified user reviews

Pros
  • Users find Dataiku easy to use, simplifying ML development and helping detect opportunities and risks effortlessly.
  • Users appreciate how Dataiku simplifies ML development, enabling quick training, evaluation, and understanding of data easily.
  • 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 face slow performance with Dataiku when managing large datasets, impacting efficiency and productivity.
  • Users find the difficult learning curve challenging for beginners, impacting their ability to maximize the platform's potential.
  • Users find the pricing high for small companies and students, impacting accessibility for basic projects.

What Are Recent G2 Reviews of Dataiku?

What Are G2 Users Discussing About Dataiku?

SuperAnnotate

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: 355

How Do G2 Users Rate SuperAnnotate?

  • Ease of Use: 9.5/10 (Category avg: 8.9/10)
  • Scalability: 9.9/10 (Category avg: 9.0/10)
  • Metrics: 9.7/10 (Category avg: 8.7/10)
  • Framework Flexibility: 9.9/10 (Category avg: 8.7/10)

Who Is the Company Behind SuperAnnotate?

  • Seller: SuperAnnotate
  • Company Website:
  • Year Founded: 2018
  • HQ Location: San Francisco, CA
  • Twitter: @superannotate
    720 Twitter followers
  • LinkedIn® Page: www.linkedin.com
    413 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?

What Are G2 Users Discussing About SuperAnnotate?

JFrog

JFrog Ltd. (Nasdaq: FROG), the creators of the unified DevOps, DevSecOps, DevGovOps and MLOps platform, is on a mission to create a world of software delivered without friction from development to production. Driven by a “Liquid Software” vision to keep software continuously flowing, secure, and always up to date, the JFrog Platform serves as the definitive software supply chain system of record. It is uniquely engineered to power organizations as they build, manage, and distribute trusted software with unprecedented speed, security, and scale across hybrid and multi-cloud environments. As software engineering evolves in the AI era, JFrog’s newest offerings address the industry's most pressing trend: the rise of agentic software development and the hidden security risks of "Shadow AI." In response to threat actors increasingly targeting developer workflows including a massive surge in malicious open-source AI models and infected packages; JFrog has expanded its platform capabilities to deliver absolute end-to-end visibility and automated compliance. Key new innovations include the JFrog AI Catalog, which enables organizations to centralize, govern, and control the lifecycle of AI models approved for enterprise use. To secure autonomous coding environments, JFrog introduced the Universal MCP Registry and the Agent Skills Registry (developed alongside NVIDIA). These new solutions establish the industry’s first enterprise-grade trust layer to safely manage and store AI agent skills, monitor connections, and instantly block unsafe developer tools or malicious coding extensions right where developers work. Furthermore, the integration of advanced DevGovOps and Runtime Security tools allows teams to replace slow, manual compliance audits with continuous, background policy enforcement. By shifting security left directly into the binary pipeline, JFrog ensures that the volume of AI-assisted code does not outpace an organization's ability to verify its safety. Today, millions of users and approximately 6,600 organizations worldwide, including a majority of the Fortune 100, depend on the universal JFrog Platform to eliminate point-solution fatigue, bridge the governance gap, and securely embrace digital transformation. Learn more at www.jfrog.com or follow us on X @JFrog.

Average Rating: 4.3/5.0

Total Reviews: 173

How Do G2 Users Rate JFrog?

  • Ease of Use: 8.2/10 (Category avg: 8.9/10)
  • Scalability: 10.0/10 (Category avg: 9.0/10)

Who Is the Company Behind JFrog?

  • Seller: JFrog Ltd
  • Company Website:
  • Year Founded: 2008
  • HQ Location: Sunnyvale, CA
  • Twitter: @jfrog
    23,186 Twitter followers
  • LinkedIn® Page: www.linkedin.com
    2,527 employees on LinkedIn®

Who Uses This Product?

  • Who Uses This: Software Engineer, DevOps Engineer
  • Top Industries: Information Technology and Services, Computer Software
  • Company Size: 48% Large, 30% Medium

What Do G2 Reviewers Say About JFrog?

AI-generated summary from verified user reviews

Pros
  • Users appreciate the unified support for various package formats that simplifies DevOps management and integration.
  • Users praise JFrog for its efficient repository management, streamlining the storage and tracking of artifacts in DevOps workflows.
  • Users value the seamless integration of JFrog with CI/CD tools, enhancing efficiency in artifact management and security.
  • Users value the seamless integrations of JFrog with various tools, enhancing their CI/CD workflows significantly.
  • Users appreciate the easy integrations of JFrog, enhancing CI/CD processes and supporting various package formats seamlessly.
Cons
  • Users find the complexity of JFrog overwhelming, often needing extensive training to use all features effectively.
  • Users often find JFrog to be expensive, especially challenging for smaller teams and individual developers to afford.
  • Users find the steep learning curve of JFrog challenging, requiring significant time and investment to master.
  • Users note the difficult learning curve with JFrog, requiring extensive training to navigate its complex features effectively.
  • Users find the learning difficulty of JFrog challenging, requiring significant time investment to master its features.

What Are Recent G2 Reviews of JFrog?

What Are G2 Users Discussing About JFrog?

Arize AX

Arize AI is the continual learning and AI engineering platform for observing, evaluating, and improving AI agents and LLM applications across development and production. Trusted by leading AI startups, 25% of Fortune 100 companies, and 150+ enterprises, including Uber, DoorDash, Reddit, and Atlassian. Traditional APM tells teams whether an application is fast and available. Arize goes further, showing whether an AI system behaved as intended and delivered a high-quality, trustworthy response. OBSERVE how your agents actually behave. Trace every step of a session, including prompts, tool calls, retrievals, chains, and multi-agent swarms. ADB, Arize’s purpose-built datastore, unifies traces and eval data in open formats. Its elastic architecture supports real-time streaming and high-volume querying while allowing teams to access their data from existing tools and warehouses without exporting or duplicating it. EVALUATE agent quality using those same traces. Run LLM-as-a-judge, code-based, and Agent-as-a-Judge evaluations to assess quality and score outcomes. Run offline evaluations on datasets to test and compare changes before release, and online evaluations on production traces to monitor quality and detect regressions over time. IMPROVE CONTINUOUSLY by turning production feedback into an agent improvement loop. Signal, Arize’s always-on Agent SRE, continuously reviews production traces to surface emerging issues and failure patterns. Connect to a repository and Arize managed agents can investigate issues and propose fixes as pull requests for human review. Engineers can also use Arize Skills to investigate traces, create datasets and evals, and run experiments from Cursor, Claude Code, Codex, and other coding agents. Arize AI, the team behind OpenInference, provides open, portable instrumentation built on OpenTelemetry, with integrations across more than 40+ models, frameworks, and tools. Arize meets production-grade security and compliance requirements, including SOC 2 Type II, ISO 27001, HIPAA, and GDPR. The team also maintains Phoenix, the open-source AI observability and evaluation platform used by AI engineers worldwide.

Average Rating: 4.4/5.0

Total Reviews: 76

How Do G2 Users Rate Arize AX?

  • Ease of Use: 8.6/10 (Category avg: 8.9/10)
  • Scalability: 9.3/10 (Category avg: 9.0/10)
  • Metrics: 8.9/10 (Category avg: 8.7/10)
  • Framework Flexibility: 8.5/10 (Category avg: 8.7/10)

Who Is the Company Behind Arize AX?

  • Seller: Arize AI
  • Year Founded: 2020
  • HQ Location: San Francisco, California, United States
  • Twitter: @arizeai
    4,614 Twitter followers
  • LinkedIn® Page: www.linkedin.com
    241 employees on LinkedIn®

Who Uses This Product?

  • Top Industries: Information Technology and Services, Computer Software
  • Company Size: 40% Medium, 40% Small

What Do G2 Reviewers Say About Arize AX?

AI-generated summary from verified user reviews

Pros
  • Users commend the responsive and diligent support team of Arize AX, enhancing their overall experience with valuable assistance.
  • Users appreciate the smooth visualization capabilities of Arize AX, enhancing their ML monitoring experience effectively.
  • Users appreciate the comprehensive documentation of Arize AI, enabling quick implementations and effective ML monitoring.
  • Users praise the intuitive interface of Arize AX, making monitoring ML models easy and efficient.
  • Users praise the easy integrations of Arize AI, facilitating seamless setup and monitoring of machine learning models.
Cons
  • Users note the missing features in Arize AX, highlighting the absence of a prompt improvement toolkit and enhanced model explainability.
  • Users report performance issues with Arize AX, including slow response times and slow UMAP rendering.
  • Users find the slow performance frustrating, especially with rendering and response times in Arize AX.
  • Users feel that Arize AX lacks a robust API, limiting feature access and integration capabilities in their workflows.
  • Users experience a difficult learning curve with Arize AX, especially when navigating advanced features and documentation.

What Are Recent G2 Reviews of Arize AX?

Encord

Encord is the universal data layer for AI. The platform helps AI teams train and run their models with the right data - managing, curating, annotating, and aligning data across the full AI lifecycle. Encord works with over 300 leading AI teams, including Woven by Toyota, Zipline, AXA, and Flock Safety. Confidentially build production AI with rich multimodal data. Encord is SOC 2, AICPA SOC, HIPAA, and GDPR compliant.

Average Rating: 4.8/5.0

Total Reviews: 65

How Do G2 Users Rate Encord?

  • Ease of Use: 9.5/10 (Category avg: 8.9/10)
  • Scalability: 9.8/10 (Category avg: 9.0/10)
  • Metrics: 10.0/10 (Category avg: 8.7/10)
  • Framework Flexibility: 9.5/10 (Category avg: 8.7/10)

Who Is the Company Behind Encord?

  • Seller: Encord
  • Year Founded: 2020
  • HQ Location: San Francisco, US
  • Twitter: @encord_team
    1,014 Twitter followers
  • LinkedIn® Page: www.linkedin.com
    220 employees on LinkedIn®

Who Uses This Product?

  • Top Industries: Computer Software, Hospital & Health Care
  • Company Size: 51% Small, 40% Medium

What Do G2 Reviewers Say About Encord?

AI-generated summary from verified user reviews

Pros
  • Users praise Encord for its responsive customer support, ensuring quick solutions and seamless collaboration during projects.
  • Users commend Encord for its annotation efficiency, appreciating the smooth workflow and intuitive interface that enhances productivity.
  • Users value the intuitive interface and powerful AI features of Encord, enhancing their data annotation efficiency significantly.
  • Users value the efficiency of Encord, experiencing smooth workflows and quick data integration that accelerates their processes.
  • Users value the intuitive interface and comprehensive features of Encord, enhancing efficiency in data curation and annotation.
Cons
  • Users find that custom workflows can be challenging, but support from the team helps mitigate the difficulties.
  • Users find it challenging to keep up with frequent updates from Encord, despite support from their customer success team.
  • Users find it challenging to keep up with best practices due to frequent feature updates from Encord.

What Are Recent G2 Reviews of Encord?

ILUM

Ilum: A Data Platform Built by Data Engineers, for Data Engineers Ilum is a Data Lakehouse platform that unifies data management, distributed processing, analytics, and AI workflows for AI engineers, data engineers, data scientists, and analysts. It belongs to the Data Platform, Data Lakehouse, and Data Engineering software categories and supports flexible deployment across cloud, on-premise, and hybrid environments. Ilum enables technical teams to build, operate, and scale modern data infrastructure using open standards. It integrates tools for batch processing, stream processing, notebook-based exploration, workflow orchestration, and business intelligence, All In a Single Platform. Ilum supports modern open table formats like Delta Lake, Apache Iceberg, Apache Hudi, and Apache Paimon. It also offers native integration with Apache Spark and Trino for compute, with Apache Flink support currently in development. Key features include: - SQL Editor: Query Delta, Iceberg, Hudi, or Spark SQL with autocomplete, result previews, and metadata inspection. - Data Lineage & Catalog: Visualize data flow using OpenLineage and explore datasets through a searchable Data Catalog. - Notebook Integration: Use built-in Jupyter notebooks pre-wired to Spark, metadata, and your data environment for exploration or modeling. - Spark Job Management: Submit, monitor, and debug Spark jobs with integrated logs, metrics, scheduling, and a built-in Spark History Server. - Trino Support: Run federated queries across multiple data sources using Trino directly from within Ilum. - Declarative Pipelines: Define repeatable ETL and analytics pipelines, with dependency tracking and recovery logic. - Automatic ERD Diagrams: Instantly generate ER diagrams from schemas to aid in data understanding and onboarding. - ML Experimentation & Tracking: Includes MLflow for managing experiments, tracking parameters, metrics, and artifacts, fully integrated with notebooks and data pipelines to streamline model development workflows. - AI Integration & Deployment: Supports both classical ML and modern AI use cases, including GenAI workflows, vector search, and embedding-based applications. Models can be registered, versioned, and deployed for inference within declarative pipelines. - Built-in AI Agent Interface: Ilum integrates, providing a GPT-style interface to interact with your data, trigger pipelines, generate SQL, or explore metadata using natural language, bringing GenAI capabilities directly into your data platform. - BI Dashboards: Native support for Apache Superset, with JDBC integration for Tableau, Power BI, and other BI tools. Additional highlights: - Multi-Cluster Management: Connect multiple Spark or Kubernetes clusters to scale and isolate workloads. - Fine-Grained Access Control: LDAP, OAuth2, and Hydra integration for secure, role-based access. - Hybrid Ready: Designed to replace Databricks or Cloudera in environments where cloud adoption is partial, regulated, or not possible.

Average Rating: 4.9/5.0

Total Reviews: 23

How Do G2 Users Rate ILUM?

  • Ease of Use: 9.3/10 (Category avg: 8.9/10)
  • Scalability: 10.0/10 (Category avg: 9.0/10)
  • Metrics: 9.8/10 (Category avg: 8.7/10)
  • Framework Flexibility: 9.8/10 (Category avg: 8.7/10)

Who Is the Company Behind ILUM?

  • Seller: Ilum
  • Company Website:
  • Year Founded: 2019
  • HQ Location: Santa Fe, US
  • Twitter: @IlumCloud
    19 Twitter followers
  • LinkedIn® Page: www.linkedin.com
    5 employees on LinkedIn®

Who Uses This Product?

  • Top Industries: Telecommunications
  • Company Size: 52% Large, 35% Medium

What Do G2 Reviewers Say About ILUM?

AI-generated summary from verified user reviews

Pros
  • Users praise ILUM for its ease of use, with a clean UI and quick deployment enhancing productivity and workflow.
  • Users praise ILUM for its seamless integration, user-friendly interface, and excellent customer support, streamlining data management effectively.
  • Users value the seamless integrations of ILUM, enhancing productivity by connecting various systems and streamlining workflows.
  • Users love the ease of setup with ILUM, noting quick deployments and user-friendly interfaces that enhance productivity.
  • Users value the easy integrations of ILUM, enhancing their data workflows and simplifying complex processes effortlessly.
Cons
  • Users note that the complex setup of ILUM can be challenging, requiring time and effort to configure properly.
  • Users note the difficult setup of ILUM, requiring experimentation and digging for advanced configurations and integrations.
  • Users note the steep learning curve for new users, though intuitive daily use improves after initial setup.
  • Users note that the UX could be improved with more intuitive navigation and clearer configuration options.
  • Users find ILUM's complexity in advanced configurations may require time and effort to fully navigate and optimize.

What Are Recent G2 Reviews of ILUM?

Saturn Cloud

Saturn Cloud is the AI token factory platform for neoclouds, AI Factory operators, and enterprises. It turns GPU infrastructure into managed services for fine-tuning, model serving, and per-token billing, with standard inference endpoints, distributed training, and managed environments. Enterprise security and governance are built in, across public, private, and on-premises environments. Learn more at saturncloud.io.

Average Rating: 4.8/5.0

Total Reviews: 320

How Do G2 Users Rate Saturn Cloud?

  • Ease of Use: 9.4/10 (Category avg: 8.9/10)
  • Scalability: 9.5/10 (Category avg: 9.0/10)
  • Metrics: 9.3/10 (Category avg: 8.7/10)
  • Framework Flexibility: 9.1/10 (Category avg: 8.7/10)

Who Is the Company Behind Saturn Cloud?

  • Seller: Saturn Cloud
  • Year Founded: 2018
  • HQ Location: New York, US
  • Twitter: @saturn_cloud
    3,279 Twitter followers
  • LinkedIn® Page: www.linkedin.com
    41 employees on LinkedIn®

Who Uses This Product?

  • Who Uses This: Data Scientist, Student
  • Top Industries: Computer Software, Higher Education
  • Company Size: 82% Small, 12% Medium

What Do G2 Reviewers Say About Saturn Cloud?

AI-generated summary from verified user reviews

Pros
  • Users value the ease of use of Saturn Cloud, appreciating its intuitive setup and versatile notebook options.
  • Users appreciate the powerful GPU performance of Saturn Cloud, enabling faster simulations and efficient project development.
  • Users appreciate the powerful GPU resources of Saturn Cloud, enhancing their learning projects with ease and robustness.
  • Users enjoy the easy setup with Saturn Cloud, making it convenient to start working on projects quickly.
  • Users appreciate the easy integrations of Saturn Cloud, enabling seamless access to powerful resources for their projects.
Cons
  • Users find Saturn Cloud's pricing expensive compared to alternatives and suggest a more affordable plan for students.
  • Users feel the absence of custom image support and extensive LLM selection limits the platform's versatility and efficiency.
  • Users find the complexity issues of Saturn Cloud challenging, particularly with documentation and pricing confusion for beginners.
  • Users struggle with poor documentation that complicates the learning process and hinders effective use of Saturn Cloud.
  • Users find the difficult setup process challenging initially, but it improves with familiarity and updated documentation.

What Are Recent G2 Reviews of Saturn Cloud?

TrueFoundry

TrueFoundry is an Enterprise Platform as a Service that enables companies to build, observe, and govern Agentic AI applications securely, scalably, and with reliability through its AI Gateway and Agentic Deployment platform. Leading Fortune 1000 companies trust TrueFoundry to accelerate innovation and deliver AI at scale, with over 1 trillion tokens per day processed via the TrueFoundry AI Gateway and more than 1,000 clusters managed by its Agentic deployment platform. TrueFoundry’s vision is to become the central control plane for running Agentic AI at scale within enterprises, serving as the command center for enterprise AI. Headquartered in San Francisco, TrueFoundry operates across North America, Europe, and Asia-Pacific, supporting enterprise AI deployments for some of the world’s most innovative organizations. To learn more about TrueFoundry, visit truefoundry.com.

Average Rating: 4.6/5.0

Total Reviews: 58

How Do G2 Users Rate TrueFoundry?

  • Ease of Use: 9.0/10 (Category avg: 8.9/10)
  • Scalability: 9.2/10 (Category avg: 9.0/10)
  • Metrics: 8.1/10 (Category avg: 8.7/10)
  • Framework Flexibility: 8.4/10 (Category avg: 8.7/10)

Who Is the Company Behind TrueFoundry?

  • Seller: TrueFoundry
  • Company Website:
  • Year Founded: 2021
  • HQ Location: San Francisco, California
  • LinkedIn® Page: www.linkedin.com
    131 employees on LinkedIn®

Who Uses This Product?

  • Top Industries: Computer Software, Information Technology and Services
  • Company Size: 49% Medium, 36% Small

What Do G2 Reviewers Say About TrueFoundry?

AI-generated summary from verified user reviews

Pros
  • Users value the ease of use of TrueFoundry, making deployment and management of ML models effortless and efficient.
  • Users appreciate the easy-to-use UI of TrueFoundry, enabling seamless model deployment and effective monitoring.
  • Users appreciate the exceptional customer support from TrueFoundry, noting their quick and responsive assistance during deployment challenges.
  • Users appreciate the seamless deployment process of TrueFoundry, finding it easy and efficient for their needs.
  • Users commend the easy integrations of TrueFoundry, enhancing deployment efficiency and collaboration across teams.
Cons
  • Users desire a no code/low code environment for LLM Ops and enhanced dashboard support for data pipelines.
  • Users find the complexity of TrueFoundry challenging, especially for advanced features and custom setups.
  • Users find TrueFoundry's features complex to learn, requiring significant setup and expertise for optimal use.
  • Users report deployment issues with Hugging Face models on TrueFoundry, suggesting a need for more fine-tuning options.
  • Users find the difficult setup process challenging, especially without prior cloud or Kubernetes experience.

What Are Recent G2 Reviews of TrueFoundry?

V7 Darwin

V7 Darwin is a specialized AI platform for creating high-quality training data and managing annotation workflows. It is engineered for teams building sophisticated computer vision models and solving complex, domain-specific challenges with AI. V7 Darwin provides a comprehensive suite of tools for data labeling, video annotation, and medical imaging annotation. - Create pixel-perfect image and video annotations with Auto-Annotate and SAM for semantic masks, instance segmentation, keypoints, and polygons. - Develop medical AI with tools for DICOM, NIfTI, and WSI annotation, featuring an interface with MPR, 3D rendering, precise crosshairs, windowing, and oblique views. - Accelerate video annotation by up to 10x with AI-assisted auto-tracking for objects across frames. - Manage long videos, multi-camera views, and nested annotation classes. - Design multi-stage review workflows with conditional logic, consensus, and task assignment for your data labeling pipeline. - Organize, filter, and manage large datasets with custom views and tags, enabling real-time team collaboration for annotators, reviewers, and ML engineers. - Scale your annotation projects with professional data labeling services, including certified annotators and experts in various domains (medical, video, LLMs, scientific). You can seamlessly integrate V7 Darwin with your existing tech stack and import/export annotations with ease. Get complete control over your models, tasks, and datasets through the open API, Darwin-py SDK, and CLI.

Average Rating: 4.7/5.0

Total Reviews: 55

How Do G2 Users Rate V7 Darwin?

  • Ease of Use: 9.5/10 (Category avg: 8.9/10)
  • Scalability: 9.8/10 (Category avg: 9.0/10)
  • Metrics: 9.7/10 (Category avg: 8.7/10)
  • Framework Flexibility: 9.3/10 (Category avg: 8.7/10)

Who Is the Company Behind V7 Darwin?

  • Seller: V7
  • Year Founded: 2018
  • HQ Location: London, England
  • Twitter: @v7labs
    3,471 Twitter followers
  • LinkedIn® Page: www.linkedin.com
    105 employees on LinkedIn®

Who Uses This Product?

  • Top Industries: Information Technology and Services, Computer Software
  • Company Size: 55% Small, 35% Medium

What Do G2 Reviewers Say About V7 Darwin?

AI-generated summary from verified user reviews

Pros
  • Users admire the ease of use of V7 Darwin, finding it intuitive and efficient for managing HR tasks.
  • Users value the annotation efficiency of V7 Darwin, streamlining repetitive tasks and enhancing accuracy in processes.
  • Users praise the easy-to-use annotation tools of V7 Darwin, enhancing project efficiency and quality control.
  • Users commend the intuitive UI and comprehensive functionalities of V7 Darwin, enhancing HR management and efficiency.
  • Users appreciate the efficiency of V7 Darwin, which simplifies tasks and enhances productivity in HR management.
Cons
  • Users find the lack of features in V7 Darwin, such as file manipulation and intuitive navigation, disappointing.
  • Users desire missing features in V7 Darwin, such as polygon manipulation and improved export options for datasets.
  • Users find the limited features of V7 Darwin frustrating, particularly in annotations and dataset management capabilities.
  • Users experience annotation issues, including inability to retract submissions and lacking shortcuts for approval.
  • Users find the difficult navigation of V7 Darwin challenging, requiring time to adapt to its complex interface.

What Are Recent G2 Reviews of V7 Darwin?

What Are G2 Users Discussing About V7 Darwin?

Datature

Datature is an AI Vision platform that simplifies computer vision development by unifying data labeling, model training, and deployment into a single workflow. By eliminating the need for fragmented tools and complex infrastructure, teams can focus on solving real-world problems.

Average Rating: 4.9/5.0

Total Reviews: 41

How Do G2 Users Rate Datature?

  • Ease of Use: 9.5/10 (Category avg: 8.9/10)
  • Scalability: 9.5/10 (Category avg: 9.0/10)
  • Metrics: 9.4/10 (Category avg: 8.7/10)
  • Framework Flexibility: 9.0/10 (Category avg: 8.7/10)

Who Is the Company Behind Datature?

  • Seller: Datature
  • Year Founded: 2020
  • HQ Location: San Francisco, US
  • Twitter: @DatatureAI
    168 Twitter followers
  • LinkedIn® Page: www.linkedin.com
    19 employees on LinkedIn®

Who Uses This Product?

  • Top Industries: Computer Software, Research
  • Company Size: 63% Small, 27% Large

What Do G2 Reviewers Say About Datature?

AI-generated summary from verified user reviews

Pros
  • Users find Datature's efficiency in data labeling and model training significantly enhances their project progress and productivity.
  • Users value the annotation efficiency of Datature, noting its user-friendly tools and seamless workflow for projects.
  • Users find Datature's ease of use remarkable, simplifying computer vision projects without coding and enhancing efficiency.
  • Users value the extensive model management options in Datature, facilitating efficient bespoke computer vision model training.
  • Users praise Datature for its efficient AI capabilities, enhancing data labeling and model training with user-friendly tools.
Cons
  • Users feel the limited customization options may restrict advanced users who prefer more control and flexibility.
  • Users report annotation issues due to unclear instructions, though help is often available quickly through support channels.
  • Users find a difficult learning curve for setting up labeling jobs, despite the ease of model building.
  • Users find the difficult setup process challenging, particularly in starting and configuring labelling jobs effectively.
  • Users note the expensive price tag of Datature, which may be a drawback for personal use.

What Are Recent G2 Reviews of Datature?

Bijou Barry
BB
Researched and written by Bijou Barry
Updated April 9, 2026