Best Enterprise MLOps Platforms

How Many MLOps Platforms Products Does G2 Track?

Total Products under this Category: 383

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
  • 8,300+ Authentic Reviews
  • 383+ 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, Amazon SageMaker, Microsoft Fabric, SAS Viya, Azure Machine Learning, IBM Watson Studio, and Dataiku.

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=amazon-sagemaker&focus%5B%5D=microsoft-fabric&focus%5B%5D=sas-sas-viya&focus%5B%5D=microsoft-azure-machine-learning&focus%5B%5D=ibm-watson-studio&focus%5B%5D=dataiku&segment=enterprise)

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: 1,376

How Do G2 Users Rate Databricks?

  • Ease of Use: 8.8/10 (Category avg: 8.8/10)
  • 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
  • LinkedIn® Page: www.linkedin.com
    14,336 employees on LinkedIn®

Who Uses This Product?

  • Who Uses This: Data Engineer, Senior Data Engineer
  • Top Industries: Information Technology and Services, Financial Services
  • Company Size: 47% Large, 39% Medium

What Do G2 Reviewers Say About Databricks?

AI-generated summary from verified user reviews

Pros
  • Users appreciate the seamless integration of big data and AI in Databricks, enhancing collaboration and performance.
  • Users appreciate the ease of use of Databricks, valuing its seamless integration and fast performance.
  • Users appreciate the seamless integration of Databricks with big data processing and AI, enhancing collaboration and performance.
  • Users value the seamless collaboration features of Databricks, enhancing teamwork and data analysis across departments.
  • Users appreciate the seamless integration of big data processing and AI in Databricks, enhancing collaboration and efficiency.
Cons
  • Users find the learning curve steep with Databricks, requiring time and skills to adapt effectively.
  • Users note that the cost can scale quickly, making cost monitoring and control a significant challenge.
  • Users find the steep learning curve of Databricks challenging, requiring considerable effort to adapt for business use cases.
  • Users find the inadequate ingestion options for non-standard data sources a limitation in Databricks' functionality.
  • Users find Databricks' complexity challenging, especially for beginners and smaller teams trying to fine-tune the system.

What Are Recent G2 Reviews of Databricks?

What Are G2 Users Discussing About Databricks?

Gemini platform

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: 870

How Do G2 Users Rate Gemini platform?

  • Ease of Use: 8.4/10 (Category avg: 8.8/10)
  • Scalability: 8.9/10 (Category avg: 9.0/10)
  • Metrics: 8.3/10 (Category avg: 8.7/10)
  • Framework Flexibility: 8.4/10 (Category avg: 8.7/10)

Who Is the Company Behind Gemini platform?

  • Seller: Google
  • Year Founded: 1998
  • HQ Location: Mountain View, CA
  • LinkedIn® Page: www.linkedin.com
    301,144 employees on LinkedIn®
  • Ownership: NASDAQ:GOOG
  • Phone: +1 (650) 253-0000

Who Uses This Product?

  • Who Uses This: Data Scientist, Software Engineer
  • Top Industries: Computer Software, Information Technology and Services
  • Company Size: 44% Small, 28% Large

What Do G2 Reviewers Say About Gemini platform?

AI-generated summary from verified user reviews

Pros
  • Users appreciate the ease of use of the Gemini platform, enhancing productivity and streamlining workflows significantly.
  • Users praise the model variety of Gemini, finding it essential for seamless integration and productivity in projects.
  • Users value the multimodal capabilities of Gemini, seamlessly integrating text, images, code, and documents for enhanced productivity.
  • Users appreciate the multimodal capabilities of Gemini, enhancing productivity in software development and automation tasks.
  • Users value the easy integrations of Gemini, enhancing workflow efficiency through seamless connectivity with various platforms.
Cons
  • Users find the Gemini platform to be expensive due to unclear cost tracking and potential for overspending.
  • Users find the learning curve steep with Gemini, as navigating its complexities can be overwhelming for new users.
  • Users find the complexity of the Gemini platform challenging, especially with IAM roles and numerous configuration options.
  • Users find the complexity issues of Vertex AI daunting, particularly during project setup and resource management.
  • Users find the difficult learning curve of Gemini platform overwhelming, especially with configuration and cost management challenges.

What Are Recent G2 Reviews of Gemini platform?

What Are G2 Users Discussing About Gemini platform?

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.

Average Rating: 4.3/5.0

Total Reviews: 54

How Do G2 Users Rate Amazon SageMaker?

  • Ease of Use: 8.4/10 (Category avg: 8.8/10)
  • Scalability: 9.6/10 (Category avg: 9.0/10)
  • Metrics: 9.4/10 (Category avg: 8.7/10)
  • Framework Flexibility: 8.8/10 (Category avg: 8.7/10)

Who Is the Company Behind Amazon SageMaker?

Who Uses This Product?

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

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 adaptations and smooth training experiences.
  • Users value the seamless AI integration of Amazon SageMaker, enhancing model building and scalability in one platform.
  • Users commend the enhanced computing power of Amazon SageMaker, significantly speeding up deep learning model training times.
  • Users find Amazon SageMaker to be highly efficient, drastically reducing model training time and simplifying the entire process.
  • Users commend the fast processing of Amazon SageMaker, significantly reducing training times for deep learning models.
Cons
  • Users find Amazon SageMaker expensive, particularly for long-running jobs, with complex pricing leading to unexpected costs.
  • Users find Amazon SageMaker's pricing complexity challenging, especially with unexpected costs for extensive use and steep learning curve.
  • Users find the complexity issues with Amazon SageMaker's pricing and setup to be a significant hurdle for effective use.
  • Users find that the learning curve with Amazon SageMaker can be steep for those new to AWS services.
  • Users find a significant learning curve with Amazon SageMaker's initial setup, impacting their overall experience.

What Are Recent G2 Reviews of Amazon SageMaker?

What Are G2 Users Discussing About Amazon SageMaker?

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Microsoft Fabric

Microsoft Fabric is a comprehensive, AI-powered data analytics platform that unifies various data management and analysis tools into a single, integrated environment. It combines the capabilities of Microsoft Power BI, Azure Synapse Analytics, and Azure Data Factory, offering a seamless experience for data integration, engineering, warehousing, real-time analytics, data science, and business intelligence. By centralizing these services, Fabric simplifies data management, enhances collaboration, and accelerates the transformation of raw data into actionable insights. Key Features and Functionality: - Unified Data Lake (OneLake): Fabric provides a single, AI-ready data lake that centralizes and curates all business data within a unified, governed hub, ensuring all teams access accurate datasets securely. - AI-Powered Tools: The platform offers AI-enhanced tools tailored for various data projects, enabling teams to innovate faster and derive near real-time insights that drive business impact. - Integrated Analytics Solutions: Fabric encompasses data integration, data engineering, data warehousing, real-time analytics, data science, and business intelligence, all hosted on a lake-centric SaaS solution for simplicity and to maintain a single source of truth. - Built-in Security and Governance: With robust data security, governance, and compliance features, Fabric ensures that data is managed responsibly and in accordance with industry standards. Primary Value and User Solutions: Microsoft Fabric addresses the complexities associated with managing disparate data systems by providing a unified platform that streamlines data workflows. It empowers organizations to harness the full potential of their data, facilitating informed decision-making and fostering innovation. By integrating various data services, Fabric reduces operational overhead, enhances productivity, and supports the development of AI-driven solutions, positioning businesses to thrive in a data-centric landscape.

Average Rating: 4.6/5.0

Total Reviews: 44

How Do G2 Users Rate Microsoft Fabric?

  • Ease of Use: 9.0/10 (Category avg: 8.8/10)
  • Scalability: 9.3/10 (Category avg: 9.0/10)
  • Metrics: 8.9/10 (Category avg: 8.7/10)
  • Framework Flexibility: 9.3/10 (Category avg: 8.7/10)

Who Is the Company Behind Microsoft Fabric?

  • Seller: Microsoft
  • Year Founded: 1975
  • HQ Location: Redmond, Washington
  • LinkedIn® Page: www.linkedin.com
    232,750 employees on LinkedIn®
  • Ownership: MSFT
  • Phone: +1 (877) 276-2464

Who Uses This Product?

  • Top Industries: Information Technology and Services, Insurance
  • Company Size: 39% Large, 39% Medium

What Do G2 Reviewers Say About Microsoft Fabric?

AI-generated summary from verified user reviews

Pros
  • Users appreciate the ease of use of Microsoft Fabric, thanks to its intuitive design and seamless integration of tools.
  • Users value the exceptional customer support of Microsoft Fabric, ensuring guidance and assistance whenever needed.
  • Users appreciate the unified platform of Microsoft Fabric, enabling seamless integration of data engineering and analytics tools.
  • Users value the intuitive design of Microsoft Fabric, making it accessible for everyone, regardless of coding skills.
  • Users value the easy setup of Microsoft Fabric, enabling quick implementation without needing coding skills or tech expertise.
Cons
  • Users find the learning curve steep for Microsoft Fabric, especially those new to its unified experience.
  • Users find formula limitations in Microsoft Fabric challenging, causing confusion due to differences from Excel's familiar logic.
  • Users note a steep learning curve in Microsoft Fabric, making it challenging for newcomers to adapt effectively.
  • Users find Excel issues with Microsoft Fabric, requiring adjustments to familiar formulas, impacting productivity temporarily.
  • Users find Microsoft Fabric to be expensive, especially considering its complexity and cost for smaller teams.

What Are Recent G2 Reviews of Microsoft Fabric?

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: 8.8/10)
  • 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
  • LinkedIn® Page: www.linkedin.com
    15,122 employees on LinkedIn®
  • Phone: 1-800-727-0025

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 enjoy the ease of use of SAS Viya, thanks to its simple syntax and integrated features for analytics.
  • Users commend SAS Viya for its powerful and sophisticated features that enhance data analysis and collaboration across teams.
  • Users value the powerful analytics and integration features of SAS Viya, significantly enhancing data analysis capabilities.
  • Users value the powerful data analysis capabilities of SAS Viya, enhancing efficiency and integration for various tasks.
  • Users value the modern, intuitive user interface of SAS Viya, enhancing productivity with seamless navigation and integration.
Cons
  • Users find the learning difficulty of SAS Viya challenging, especially for beginners due to its complexity and limited resources.
  • Users find SAS Viya has a steep learning curve, making it challenging for newcomers to grasp effectively.
  • Users find SAS Viya's complexity and steep learning curve challenging, hindering effective use and self-learning opportunities.
  • Users find the difficult learning curve of SAS Viya challenging, especially for beginners seeking easier comprehension.
  • Users find the licensing costs of SAS Viya to be prohibitively expensive, especially for features they don't use.

What Are Recent G2 Reviews of SAS Viya?

What Are G2 Users Discussing About SAS Viya?

Azure Machine Learning

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

How Do G2 Users Rate Azure Machine Learning?

  • Ease of Use: 8.5/10 (Category avg: 8.8/10)
  • Scalability: 9.2/10 (Category avg: 9.0/10)
  • Metrics: 8.3/10 (Category avg: 8.7/10)
  • Framework Flexibility: 9.2/10 (Category avg: 8.7/10)

Who Is the Company Behind Azure Machine Learning?

  • Seller: Microsoft
  • Year Founded: 1975
  • HQ Location: Redmond, Washington
  • LinkedIn® Page: www.linkedin.com
    232,750 employees on LinkedIn®
  • Ownership: MSFT
  • Phone: +1 (877) 276-2464

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 value the ease of use of Azure Machine Learning, enabling efficient data handling and job management.
  • Users highlight the scalability and integration of Azure Machine Learning, enhancing AI deployment across applications.
  • Users praise the excellent customer support of Azure Machine Learning, highlighting helpful documentation and community resources.
  • Users appreciate the easy-to-use data management features of Azure Machine Learning, enhancing their data organization and accessibility.
  • Users find Azure Machine Learning to be highly efficient for launching and monitoring machine learning jobs effortlessly.
Cons
  • Users find the steep learning curve for Azure Machine Learning challenging, requiring time to master its tools and functions.
  • Users find the difficult navigation in Azure Machine Learning hampers their experience and efficiency in using the service.
  • Users find the user interface disordered, leading to a frustrating experience with many unnecessary clicks.
  • Users find the complex interface of Azure Machine Learning non-intuitive, leading to a frustrating experience when navigating flows.
  • Users face a difficult learning curve with Azure Machine Learning, especially if they are new to the platform.

What Are Recent G2 Reviews of Azure Machine Learning?

What Are G2 Users Discussing About Azure Machine Learning?

IBM Watson Studio

IBM Watson Studio on IBM Cloud Pak for Data is a leading data science and machine learning solution that helps enterprises accelerate AI-powered digital transformation. It allows businesses to scale trustworthy AI and optimize decisions. Build, run, and manage AI models on any cloud through an automated end-to-end AI lifecycle--simplifying experimentation and deployment, speeding up data exploration and preparation, and improving model development and training. Govern and monitor models to mitigate drift and bias, and manage model risk. Build a ModelOps practice that synchronizes application and model pipelines to operationalize responsible, explainable AI across your enterprise. As a key offering of IBM Cloud Pak for Data, a unified data and AI platform, Watson Studio integrates seamlessly with data management services, data privacy and security capabilities, AI application tooling, open source frameworks, and a robust technology ecosystem. It unites teams and empowers businesses to build the modern information architecture that AI requires and infuse it across the organization. IBM Watson Studio is code-optional, allowing both data scientists and business analysts to work on the same platform by providing the best of open source tools along with visual, drag-and-drop capabilities. It enables organizations to tap into data assets and inject predictions into business processes and modern applications—helping them maximize their business value. It's suited for hybrid multicloud environments that demand mission-critical performance, security, and governance. Features include: • AutoAI that eliminates time-consuming, repetitive tasks by automating data preparation, model development, feature engineering and hyperparameter optimization. • Text Analytics for uncovering insights from unstructured data • Drag-and-drop visual model-building with SPSS Modeler • Broad data access – flat files, spreadsheets, major relational databases • Sophisticated graphics engine for building stunning visualizations • Support for Python 3 Notebooks Watson Studio is available via several deployment options: • IBM Cloud Pak for Data – An open, extensible data and AI platform that runs on any cloud • IBM Cloud Pak for Data System – A hybrid cloud, on-premises platform-in-a-box • IBM Cloud Pak for Data as a Service – A set of IBM Cloud Pak for Data platform services fully managed on the IBM Cloud

Average Rating: 4.2/5.0

Total Reviews: 164

How Do G2 Users Rate IBM Watson Studio?

  • Ease of Use: 8.0/10 (Category avg: 8.8/10)
  • Scalability: 8.8/10 (Category avg: 9.0/10)
  • Metrics: 9.0/10 (Category avg: 8.7/10)
  • Framework Flexibility: 8.8/10 (Category avg: 8.7/10)

Who Is the Company Behind IBM Watson Studio?

  • Seller: IBM
  • Year Founded: 1911
  • HQ Location: Armonk, New York, United States
  • LinkedIn® Page: www.linkedin.com
    344,328 employees on LinkedIn®
  • Ownership: SWX:IBM
  • Phone: 1-866-277-7488

Who Uses This Product?

  • Who Uses This: Software Engineer, CEO
  • Top Industries: Information Technology and Services, Computer Software
  • Company Size: 49% Large, 31% Small

What Do G2 Reviewers Say About IBM Watson Studio?

AI-generated summary from verified user reviews

Pros
  • Users value the Auto AI capability of IBM Watson Studio for significantly reducing manual work and enhancing productivity.
  • Users admire the Auto AI capability of IBM Watson Studio, significantly reducing manual tasks and enhancing productivity.
  • Users appreciate the user-friendly interface of IBM Watson Studio, making it easy to start and collaborate on projects.
  • Users appreciate the Auto AI capability of IBM Watson Studio, significantly reducing manual work and enhancing productivity.
  • Users value the easy AI integration of IBM Watson Studio, enhancing their data science experience significantly.
Cons
  • Users find the high costs of IBM Watson Studio challenging, especially for individuals and small startups.
  • Users find the steep learning curve of IBM Watson Studio challenging, hindering their ability to use the platform effectively.
  • Users find the steep learning curve of IBM Watson Studio challenging, especially for those new to the platform.
  • Users find the complex interface of IBM Watson Studio challenging, especially those who are new to the platform.
  • Users find the steep learning curve of IBM Watson Studio challenging, especially due to its complex interface.

What Are Recent G2 Reviews of IBM Watson Studio?

What Are G2 Users Discussing About IBM Watson Studio?

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: 8.8/10)
  • 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
  • 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 appreciate the ease of use in Dataiku, facilitating intuitive workflows for both coders and non-coders alike.
  • Users value the intuitive AI features of Dataiku, enhancing collaboration and streamlining data workflows effectively.
  • Users appreciate the intuitive usability of Dataiku, enhancing collaboration and speeding up AI project delivery.
  • Users value the easy integrations of Dataiku, enhancing collaboration and streamlining workflows effectively.
  • Users value the productivity improvement in Dataiku, enjoying streamlined AI project workflows and enhanced collaboration.
Cons
  • Users find the steep learning curve of Dataiku daunting, especially for first-time and non-technical users.
  • Users find the steep learning curve of Dataiku challenging, especially for smaller teams or simpler projects.
  • Users experience slow performance when managing large datasets, which impacts efficiency and overall user satisfaction.
  • Users find the difficult learning experience due to Dataiku's overwhelming features and steep learning curve for newcomers.
  • Users find Dataiku expensive, with high costs deterring small companies and limiting accessibility for individual users.

What Are Recent G2 Reviews of Dataiku?

What Are G2 Users Discussing About Dataiku?

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: 731

How Do G2 Users Rate Snowflake?

  • Ease of Use: 9.0/10 (Category avg: 8.8/10)
  • 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: Menlo Park, California, United States
  • LinkedIn® Page: www.linkedin.com
    12,574 employees on LinkedIn®
  • Ownership: NYSE:SNOW

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 value the ease of use of Snowflake, enjoying its user-friendly interface and seamless scalability for data analysis.
  • Users appreciate the flexible scalability of Snowflake, allowing independent adjustments to performance and efficient resource management.
  • Users value the effortless data management Snowflake offers, enabling quick analysis without infrastructure concerns.
  • Users appreciate the user-friendly interface of Snowflake, making navigation easy and intuitive for SQL tasks.
  • Users value the strong integration ecosystem of Snowflake, which enhances versatility and simplifies data workflows seamlessly.
Cons
  • Users find Snowflake to be expensive, as costs can escalate quickly without careful usage monitoring and optimization.
  • Users highlight the high costs associated with Snowflake, especially for unoptimized workloads and usage spikes.
  • Users find cost management challenging, as compute expenses can escalate quickly without careful monitoring and optimization.
  • Users find the learning curve for advanced features in Snowflake steep, especially for newcomers needing rapid proficiency.
  • Users find feature limitations in Snowflake, particularly in cost management and control over optimization and dynamic scripting.

What Are Recent G2 Reviews of Snowflake?

What Are G2 Users Discussing About Snowflake?

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: 8.8/10)
  • 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
  • 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 commend the user-friendly interface of ILUM, enjoying seamless integration and straightforward workflows for daily tasks.
  • Users value the seamless integration of ILUM with existing tools, enhancing workflows and increasing efficiency.
  • Users value the seamless integrations of ILUM, which enhance workflow efficiency without major overhauls required.
  • Users appreciate the setup ease of ILUM, allowing seamless integration and quick deployment in existing environments.
  • Users value the easy integrations of ILUM, allowing seamless connection with existing tools and workflows.
Cons
  • Users note the complex setup of ILUM, requiring significant initial effort and expertise for optimal configuration.
  • Users find the difficult setup of ILUM challenging, requiring expertise and experimentation to optimize configurations effectively.
  • Users find the learning curve steep for ILUM's advanced features but appreciate effective support for navigation.
  • Users note that administrative settings could be more accessible, requiring extra effort to manage effectively.
  • Users highlight the complexity of initial setup requiring deep technical knowledge and extensive documentation for configuration.

What Are Recent G2 Reviews of ILUM?

SAP HANA Cloud

SAP HANA Cloud is a modern database-as-a-service (DBaaS) powering the next generation of intelligent data applications. SAP HANA Cloud offers a competitive edge by incorporating advanced machine learning and predictive tools grounded in modern data science. Its powerful in-memory performance safeguards efficient data processing. By securely storing vast amounts of data with its integrated multitier storage and handling various types on a single copy in its native multi-model database, SAP HANA Cloud simplifies data management and connects to other data sources. The seamless integration of these capabilities in a reliable, unified foundation makes it easier for developers to build high-demand intelligent data apps.

Average Rating: 4.3/5.0

Total Reviews: 522

How Do G2 Users Rate SAP HANA Cloud?

  • Ease of Use: 8.2/10 (Category avg: 8.8/10)

Who Is the Company Behind SAP HANA Cloud?

  • Seller: SAP
  • Year Founded: 1972
  • HQ Location: Walldorf
  • LinkedIn® Page: www.linkedin.com
    149,349 employees on LinkedIn®
  • Ownership: NYSE:SAP
  • Phone: +1 (800) 872-1727

Who Uses This Product?

  • Who Uses This: Consultant, SAP Consultant
  • Top Industries: Information Technology and Services, Computer Software
  • Company Size: 61% Large, 26% Medium

What Do G2 Reviewers Say About SAP HANA Cloud?

AI-generated summary from verified user reviews

Pros
  • Users value the ease of use of SAP HANA Cloud, benefiting from its seamless integration and efficient data management.
  • Users appreciate the easy integrations of SAP HANA Cloud, enhancing efficiency in reporting and decision-making across modules.
  • Users value the seamless integrations of SAP HANA Cloud, enhancing efficiency in reporting and decision-making processes.
  • Users value the exceptional processing speed of SAP HANA Cloud, enhancing real-time insights and decision-making capabilities.
  • Users value the scalability of SAP HANA Cloud, enabling efficient growth and flexibility aligned with business needs.
Cons
  • Users often face complexity in initial setup and configuration that complicates the effective use of SAP HANA Cloud.
  • Users are concerned about the high costs associated with SAP HANA Cloud, especially when managing workloads improperly.
  • Users often struggle with the learning curve and initial setup complexity, hindering efficient onboarding for new users.
  • Users find the difficult learning curve of SAP HANA Cloud hinders onboarding and requires significant technical expertise.
  • Users find the complex setup of SAP HANA Cloud challenging, particularly for new users and specialized features.

What Are Recent G2 Reviews of SAP HANA Cloud?

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: 352

How Do G2 Users Rate SuperAnnotate?

  • Ease of Use: 9.5/10 (Category avg: 8.8/10)
  • 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?

Who Uses This Product?

  • Who Uses This: Student, Data Annotator
  • 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 appreciate the simple and user-friendly interface of SuperAnnotate, making complex annotation tasks manageable.
  • Users value the simple and user-friendly interface of SuperAnnotate, enhancing productivity and task management.
  • Users commend the annotation efficiency of SuperAnnotate, highlighting its intuitive interface and advanced tools for seamless collaboration.
  • Users value the efficiency of SuperAnnotate, as it simplifies complex tasks and enhances productivity through streamlined workflows.
  • Users highlight the high quality and reliability of SuperAnnotate, emphasizing its structured workflow and clear review processes.
Cons
  • Users experience performance issues with loading times on larger projects, affecting productivity and overall user satisfaction.
  • Users often face slow performance with SuperAnnotate, especially when dealing with large files and complex tasks.
  • Users find the difficult learning curve challenging due to the abundance of advanced features on SuperAnnotate.
  • Users face complexity in instructions for advanced tools, impacting ease of use and performance with large datasets.
  • Users highlight a lack of guidance for new engineers, seeking clearer onboarding and advanced feature walkthroughs.

What Are Recent G2 Reviews of SuperAnnotate?

What Are G2 Users Discussing About SuperAnnotate?

Comet.ml

Comet provides an end-to-end model evaluation platform for AI developers, with best in class LLM evaluations, experiment tracking, and production monitoring.

Average Rating: 4.1/5.0

Total Reviews: 36

How Do G2 Users Rate Comet.ml?

  • Ease of Use: 8.3/10 (Category avg: 8.8/10)
  • Scalability: 8.0/10 (Category avg: 9.0/10)
  • Metrics: 7.9/10 (Category avg: 8.7/10)
  • Framework Flexibility: 7.5/10 (Category avg: 8.7/10)

Who Is the Company Behind Comet.ml?

  • Seller: Comet.ml
  • Year Founded: 2017
  • HQ Location: New York, NY
  • LinkedIn® Page: www.linkedin.com
    102 employees on LinkedIn®

Who Uses This Product?

  • Top Industries: Information Technology and Services, Computer Software
  • Company Size: 43% Small, 35% Large

What Are Recent G2 Reviews of Comet.ml?

What Are G2 Users Discussing About Comet.ml?

Apache Airflow

Apache Airflow is an open-source platform designed for authoring, scheduling, and monitoring complex workflows. Developed in Python, it enables users to define workflows as code, facilitating dynamic pipeline generation and seamless integration with various technologies. Airflow's modular architecture and message queue system allow it to scale efficiently, managing workflows from single machines to large-scale distributed systems. Its user-friendly web interface provides comprehensive monitoring and management capabilities, offering clear insights into task statuses and execution logs. Key Features: - Pure Python: Workflows are defined using standard Python code, allowing for dynamic pipeline generation and easy integration with existing Python libraries. - User-Friendly Web Interface: A robust web application enables users to monitor, schedule, and manage workflows without the need for command-line interfaces. - Extensibility: Users can define custom operators and extend libraries to fit their specific environment, enhancing the platform's flexibility. - Scalability: Airflow's modular architecture and use of message queues allow it to orchestrate an arbitrary number of workers, making it ready to scale as needed. - Robust Integrations: The platform offers numerous plug-and-play operators for executing tasks across various cloud platforms and third-party services, facilitating easy integration with existing infrastructure. Primary Value and Problem Solving: Apache Airflow addresses the challenges of managing complex data workflows by providing a scalable and dynamic platform for workflow orchestration. By defining workflows as code, it ensures reproducibility, version control, and collaboration among teams. The platform's extensibility and robust integrations allow organizations to adapt it to their specific needs, reducing operational overhead and improving efficiency in data processing tasks. Its user-friendly interface and monitoring capabilities enhance transparency and control over workflows, leading to improved data quality and reliability.

Average Rating: 4.4/5.0

Total Reviews: 129

How Do G2 Users Rate Apache Airflow?

  • Ease of Use: 8.5/10 (Category avg: 8.8/10)
  • Scalability: 9.1/10 (Category avg: 9.0/10)
  • Metrics: 8.5/10 (Category avg: 8.7/10)
  • Framework Flexibility: 9.0/10 (Category avg: 8.7/10)

Who Is the Company Behind Apache Airflow?

Who Uses This Product?

  • Who Uses This: Data Engineer
  • Top Industries: Information Technology and Services, Computer Software
  • Company Size: 46% Medium, 31% Large

What Do G2 Reviewers Say About Apache Airflow?

AI-generated summary from verified user reviews

Pros
  • Users appreciate the clean and intuitive UI of Apache Airflow, enhancing ease of use for pipeline management.
  • Users appreciate the clean and intuitive UI of Apache Airflow, enhancing workflow management and troubleshooting efficiency.
  • Users value the flexibility of Apache Airflow, benefiting from extensive plugins and seamless integrations for diverse workflows.
  • Users value the automation capabilities of Apache Airflow, enabling seamless scheduling and task management across various tools.
  • Users value the easy integrations offered by Apache Airflow, enhancing flexibility for managing complex workflows and systems.
Cons
  • Users find the difficult setup of Apache Airflow to be time-consuming and challenging, especially in restrictive environments.
  • Users find the learning curve steep, requiring time to master operators, connections, and backfills effectively.
  • Users find the steep learning curve of Apache Airflow challenging, particularly during initial setup and configuration.
  • Users find Apache Airflow has a challenging learning curve that complicates setup and usage for some workflows.
  • Users find the outdated user interface of Apache Airflow hampers usability and could benefit from modernization.

What Are Recent G2 Reviews of Apache Airflow?

What Are G2 Users Discussing About Apache Airflow?

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: 4.2/5.0

Total Reviews: 178

How Do G2 Users Rate JFrog?

  • Ease of Use: 8.1/10 (Category avg: 8.8/10)
  • 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
  • LinkedIn® Page: www.linkedin.com
    2,527 employees on LinkedIn®
  • Ownership: NASDAQ: FROG

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 highlight the centralized platform of JFrog, facilitating efficient management of artifacts and secure sharing across environments.
  • Users value the centralized repository management in JFrog, making artifact storage and sharing seamless and efficient.
  • Users appreciate the ease of deployment with JFrog, seamlessly integrating CI/CD and security into a single platform.
  • Users value the seamless integrations of JFrog with various CI/CD tools, enhancing their workflow and automation.
  • Users appreciate the easy integrations of JFrog with CI/CD tools, enhancing their DevOps workflows seamlessly.
Cons
  • Users find JFrog's complexity in workflows challenging, especially for newcomers navigating its powerful features.
  • Users note the expensive cost compared to similar products, making it a less attractive option for some.
  • Users find the steep learning curve of JFrog challenging, requiring significant time and training to navigate effectively.
  • Users find difficult learning curves with JFrog, often requiring extensive training to navigate its complex functionalities.
  • Users find the learning difficulty of JFrog frustrating, requiring significant time and training to master its features.

What Are Recent G2 Reviews of JFrog?

What Are G2 Users Discussing About JFrog?

Bijou Barry
BB
Researched and written by Bijou Barry
Updated