Best Enterprise Data Science and Machine Learning Platforms

How Many Data Science and Machine Learning Platforms Products Does G2 Track?

Total Products under this Category: 1,731

Category Stats (Nov 2026)

  • Average Rating: 4.45/5 (↓0.01 vs Oct 2026) The average rating of products in this category, based on all submitted ratings
  • Top Trending Product: Braintrust Data (+1.99%) - Among all products in this category, Braintrust Data recorded the largest rating increase compared to last month

Last updated: November 01, 2026

How Does G2 Rank Data Science and Machine Learning Platforms Products?

Why You Can Trust G2's Software Rankings:

  • 30 Analysts and Data Experts
  • 15,900+ Authentic Reviews
  • 1,731+ Products
  • Unbiased Rankings

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

G2 Grid® for Data Science and Machine Learning Platforms

G2 Grid® for Data Science and Machine Learning Platforms plotting products by satisfaction and market presence

Highlighted products: Databricks, Gemini platform, SAS Viya, Dataiku, Alteryx, IBM watsonx.ai, Deep Learning VM Image, and MATLAB.

Underlying data: [Grid® JSON](https://www.g2.com/categories/data-science-and-machine-learning-platforms/grids.json?focus%5B%5D=databricks&focus%5B%5D=gemini-platform&focus%5B%5D=sas-sas-viya&focus%5B%5D=dataiku&focus%5B%5D=alteryx&focus%5B%5D=ibm-watsonx-ai&focus%5B%5D=deep-learning-vm-image&focus%5B%5D=matlab&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: 1,384

How Do G2 Users Rate Databricks?

  • Application: 8.7/10 (Category avg: 8.5/10)
  • Managed Service: 8.5/10 (Category avg: 8.3/10)
  • Natural Language Understanding: 8.4/10 (Category avg: 8.3/10)
  • Ease of Admin: 8.4/10 (Category avg: 8.6/10)

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, Data Analyst
  • 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: 874

How Do G2 Users Rate Gemini platform?

  • Application: 8.3/10 (Category avg: 8.5/10)
  • Managed Service: 8.3/10 (Category avg: 8.3/10)
  • Natural Language Understanding: 8.5/10 (Category avg: 8.3/10)
  • Ease of Admin: 8.1/10 (Category avg: 8.6/10)

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: 43% 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?

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: 795

How Do G2 Users Rate SAS Viya?

  • Application: 7.8/10 (Category avg: 8.5/10)
  • Managed Service: 7.9/10 (Category avg: 8.3/10)
  • Natural Language Understanding: 7.8/10 (Category avg: 8.3/10)
  • Ease of Admin: 7.6/10 (Category avg: 8.6/10)

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?

AI can help you find the answers. G2 helps you trust them.

Connect G2 to Claude or ChatGPT for answers grounded in G2's trusted reviews, comparisons, and pricing from real user insights.

How it works
Connect G2 to ChatGPTConnect G2 to Claude

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?

  • Application: 8.3/10 (Category avg: 8.5/10)
  • Managed Service: 8.2/10 (Category avg: 8.3/10)
  • Natural Language Understanding: 7.8/10 (Category avg: 8.3/10)
  • Ease of Admin: 7.9/10 (Category avg: 8.6/10)

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?

Alteryx

Alteryx is the agentic analytics and automation company that helps organizations turn complex data into trusted insights, decisions, and action. Built for the people closest to the business, the Alteryx One platform is designed to work across an organization’s existing data and technology ecosystem. It enables business users to produce AI-ready datasets and define, maintain, and update business logic into visual workflows that AI uses to automate operations and insights. This business logic stays visible, understandable, repeatable, and auditable (VURA), so that teams can automate data-intensive workflows without coding. Alteryx closes the last-mile gap between data platforms and the decisions and operations that run the business—giving AI the trusted business context it needs to deliver reliable results. Over 8,000 customers worldwide, including more than half of the Global 2000, rely on Alteryx to improve revenue performance, manage costs, increase operational efficiency, and reduce risk. Collectively, they run more than 380 million workflows on Alteryx each year.

Average Rating: 4.6/5.0

Total Reviews: 933

How Do G2 Users Rate Alteryx?

  • Application: 8.7/10 (Category avg: 8.5/10)
  • Managed Service: 7.9/10 (Category avg: 8.3/10)
  • Natural Language Understanding: 7.9/10 (Category avg: 8.3/10)
  • Ease of Admin: 8.3/10 (Category avg: 8.6/10)

Who Is the Company Behind Alteryx?

  • Seller: Alteryx
  • Company Website:
  • Year Founded: 1997
  • HQ Location: Irvine, CA
  • LinkedIn® Page: www.linkedin.com
    2,312 employees on LinkedIn®
  • Ownership: Private

Who Uses This Product?

  • Who Uses This: Data Analyst, Analyst
  • Top Industries: Financial Services, Accounting
  • Company Size: 61% Large, 22% Medium

What Do G2 Reviewers Say About Alteryx?

AI-generated summary from verified user reviews

Pros
  • Users appreciate the ease of use of Alteryx, enabling them to work efficiently without coding expertise.
  • Users appreciate the automation capabilities of Alteryx, enabling efficient workflows and streamlined data processing.
  • Users find Alteryx to be super-intuitive, making complex data analysis and migration tasks significantly easier.
  • Users find Alteryx to be super-intuitive and easy to learn, greatly simplifying their data analysis processes.
  • Users commend Alteryx for its efficiency, with quick workflows and time savings in data preparation and automation.
Cons
  • Users find the expensive licensing costs of Alteryx a barrier for wider adoption and utilization.
  • Users find the learning curve steep, requiring time to grasp all the tools effectively.
  • Users are frustrated with missing features like improved email notifications and bugs in tools during transitions.
  • Users find that mastering advanced tools in Alteryx requires time, affecting overall ease of use.
  • Users experience slow performance with large workflows on Alteryx, impacting efficiency and usability significantly.

What Are Recent G2 Reviews of Alteryx?

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: 150

How Do G2 Users Rate IBM watsonx.ai?

  • Application: 8.8/10 (Category avg: 8.5/10)
  • Managed Service: 8.5/10 (Category avg: 8.3/10)
  • Natural Language Understanding: 8.6/10 (Category avg: 8.3/10)
  • Ease of Admin: 8.5/10 (Category avg: 8.6/10)

Who Is the Company Behind IBM watsonx.ai?

  • Seller: IBM
  • Company Website:
  • Year Founded: 1911
  • HQ Location: Armonk, New York, United States
  • LinkedIn® Page: www.linkedin.com
    344,328 employees on LinkedIn®
  • Ownership: SWX:IBM

Who Uses This Product?

  • Who Uses This: Consultant
  • Top Industries: Information Technology and Services, Computer Software
  • Company Size: 42% Small, 34% Large

What Do G2 Reviewers Say About IBM watsonx.ai?

AI-generated summary from verified user reviews

Pros
  • Users find IBM watsonx.ai extremely user-friendly, appreciating its ease of setup and convenience for real enterprise needs.
  • Users appreciate the model variety in Watsonx.ai, enabling tailored experiences through customizable, user-friendly interfaces.
  • Users value the integrated platform of IBM watsonx.ai for simplifying enterprise-grade AI development and management.
  • Users appreciate the integrated studio for enterprise-grade AI, making model testing and experimentation user-friendly and efficient.
  • Users find IBM watsonx.ai impressive for its enterprise-grade AI capabilities that simplify building and managing AI models.
Cons
  • Users find the difficult learning curve of IBM watsonx.ai intimidating, especially for those new to the platform.
  • Users often experience a steep learning curve and complexity with IBM watsonx.ai, making it daunting for newcomers.
  • Users frequently face a steep learning curve with IBM watsonx.ai, finding initial setup and navigation overwhelming.
  • Users find the cost to be quite high, especially for small teams that may struggle with complexity.
  • Users note the need for improved onboarding and transparency in IBM watsonx.ai for a smoother experience.

What Are Recent G2 Reviews of IBM watsonx.ai?

Deep Learning VM Image

Deep Learning VM Images are pre-configured virtual machine images optimized for data science and machine learning tasks. These images come with essential machine learning frameworks and tools pre-installed, enabling users to deploy and scale machine learning models efficiently on Google Cloud's infrastructure. Key Features and Functionality: - Pre-installed Frameworks: Support for TensorFlow Enterprise, TensorFlow, PyTorch, and generic high-performance computing, catering to various machine learning needs. - Operating System Options: Based on Debian 11 and Ubuntu 22.04, providing flexibility and compatibility with different environments. - Comprehensive Python Environment: Includes Python 3.10 with a suite of libraries such as NumPy, SciPy, Matplotlib, Pandas, NLTK, Pillow, scikit-image, OpenCV, and scikit-learn, facilitating a robust development experience. - JupyterLab Integration: Offers JupyterLab notebook environments for rapid prototyping and interactive development. - GPU Acceleration: Equipped with the latest NVIDIA drivers and packages, including CUDA 11.x and 12.x, CuDNN, and NCCL, to leverage GPU capabilities for accelerated computation. Primary Value and User Solutions: Deep Learning VM Images streamline the setup process for machine learning projects by providing ready-to-use environments with pre-installed frameworks and tools. This reduces the time and effort required for configuration, allowing data scientists and machine learning practitioners to focus on model development and experimentation. The integration with Google Cloud's scalable infrastructure ensures that users can efficiently manage and scale their machine learning workloads, whether they require CPU or GPU resources. Regular updates and community support further enhance the reliability and performance of these VM images, making them a valuable resource for accelerating machine learning initiatives.

Average Rating: 4.3/5.0

Total Reviews: 68

How Do G2 Users Rate Deep Learning VM Image?

  • Application: 8.8/10 (Category avg: 8.5/10)
  • Managed Service: 8.4/10 (Category avg: 8.3/10)
  • Natural Language Understanding: 8.6/10 (Category avg: 8.3/10)
  • Ease of Admin: 8.9/10 (Category avg: 8.6/10)

Who Is the Company Behind Deep Learning VM Image?

  • 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: Software Engineer
  • Top Industries: Computer Software, Information Technology and Services
  • Company Size: 47% Small, 36% Medium

What Do G2 Reviewers Say About Deep Learning VM Image?

AI-generated summary from verified user reviews

Pros
  • Users appreciate the ease of setup with Deep Learning VM Image, enabling quick initiation of machine learning tasks.
  • Users value the easy setup of Deep Learning VM Image, enabling quick start for deep learning tasks.
  • Users love the pre-installed ML frameworks and tools, which simplify setup and enhance deep learning project efficiency.
  • Users love the easy integrations of Deep Learning VM Image with Google Cloud, simplifying their workflow and saving time.
  • Users value the easy setup of Deep Learning VM Image, allowing for quick environment configuration for deep learning tasks.
Cons
  • Users express concerns about the high costs associated with the Deep Learning VM Image, impacting their budgets significantly.
  • Users find the high costs of Deep Learning VM Image to be a significant drawback compared to general-purpose options.
  • Users note the steep learning curve of Deep Learning VM Image, finding it overwhelming for newcomers.
  • Users find the difficult learning curve overwhelming, particularly for beginners without large datasets or user-friendly interfaces.
  • Users report feeling the impact of unnecessary dependencies that bloat the Deep Learning VM Image, leading to inefficiency.

What Are Recent G2 Reviews of Deep Learning VM Image?

MATLAB

MATLAB is a high-level programming and numeric computing environment widely utilized by engineers and scientists for data analysis, algorithm development, and system modeling. It offers a desktop environment optimized for iterative analysis and design processes, coupled with a programming language that directly expresses matrix and array mathematics. The Live Editor feature enables users to create scripts that integrate code, output, and formatted text within an executable notebook. Key Features and Functionality: - Data Analysis: Tools for exploring, modeling, and analyzing data. - Graphics: Functions for visualizing and exploring data through various plots and charts. - Programming: Capabilities to create scripts, functions, and classes for customized workflows. - App Building: Facilities to develop desktop and web applications. - External Language Interfaces: Integration with languages such as Python, C/C++, Fortran, and Java. - Hardware Connectivity: Support for connecting MATLAB to various hardware platforms. - Parallel Computing: Ability to perform large-scale computations and parallelize simulations using multicore desktops, GPUs, clusters, and cloud resources. - Deployment: Options to share MATLAB programs and deploy them to enterprise applications, embedded devices, and cloud environments. Primary Value and User Solutions: MATLAB streamlines complex mathematical computations and data analysis tasks, enabling users to develop algorithms and models efficiently. Its comprehensive toolboxes and interactive apps facilitate rapid prototyping and iterative design, reducing development time. The platform's scalability allows for seamless transition from research to production, supporting deployment on various systems without extensive code modifications. By integrating with multiple programming languages and hardware platforms, MATLAB provides a versatile environment that addresses the diverse needs of engineers and scientists across industries.

Average Rating: 4.5/5.0

Total Reviews: 755

How Do G2 Users Rate MATLAB?

  • Application: 8.6/10 (Category avg: 8.5/10)
  • Managed Service: 8.3/10 (Category avg: 8.3/10)
  • Natural Language Understanding: 8.5/10 (Category avg: 8.3/10)
  • Ease of Admin: 8.4/10 (Category avg: 8.6/10)

Who Is the Company Behind MATLAB?

  • Seller: MathWorks
  • Year Founded: 1984
  • HQ Location: Natick, MA
  • LinkedIn® Page: www.linkedin.com
    8,040 employees on LinkedIn®

Who Uses This Product?

  • Who Uses This: Student, Graduate Research Assistant
  • Top Industries: Higher Education, Research
  • Company Size: 42% Large, 31% Small

What Do G2 Reviewers Say About MATLAB?

AI-generated summary from verified user reviews

Pros
  • Users find MATLAB's ease of use refreshing, enabling quick transitions from ideas to effective solutions with minimal setup.
  • Users appreciate the strong mathematical capabilities of MATLAB, enhancing effectiveness for data analysis and visualization tasks.
  • Users value the powerful data visualization capabilities of MATLAB, enabling clear and professional presentation of complex results.
  • Users value the variety of tools in MATLAB, which enhances productivity in mathematical modeling and data analysis.
  • Users highly value the effective simulation capabilities of MATLAB, enabling seamless and accurate results in engineering tasks.
Cons
  • Users find MATLAB's high licensing costs a significant barrier, especially for individuals and small companies.
  • Users experience slow performance in MATLAB, particularly on less powerful machines with large datasets or complex simulations.
  • Users highlight the high system requirements of MATLAB, leading to slower performance on less powerful machines.
  • Users find the expensive licensing of MATLAB prohibitive, particularly for individuals and smaller organizations seeking access.
  • Users report lagging performance with MATLAB, especially when testing large scale applications, hindering effectiveness and usability.

What Are Recent G2 Reviews of MATLAB?

What Are G2 Users Discussing About MATLAB?

Google Cloud AI Hub

Google Cloud’s Artificial Intelligence (AI) Hub is a catalog of plug-and-play AI components, including end-to-end AI pipelines and out-of-the-box algorithms.

Average Rating: 4.4/5.0

Total Reviews: 43

How Do G2 Users Rate Google Cloud AI Hub?

  • Application: 8.5/10 (Category avg: 8.5/10)
  • Managed Service: 8.3/10 (Category avg: 8.3/10)
  • Natural Language Understanding: 8.3/10 (Category avg: 8.3/10)
  • Ease of Admin: 8.0/10 (Category avg: 8.6/10)

Who Is the Company Behind Google Cloud AI Hub?

  • 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?

  • Top Industries: Information Technology and Services, Computer Software
  • Company Size: 44% Small, 29% Large

What Are Recent G2 Reviews of Google Cloud AI Hub?

What Are G2 Users Discussing About Google Cloud AI Hub?

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: 732

How Do G2 Users Rate Snowflake?

  • Application: 9.2/10 (Category avg: 8.5/10)
  • Managed Service: 9.0/10 (Category avg: 8.3/10)
  • Natural Language Understanding: 8.6/10 (Category avg: 8.3/10)
  • Ease of Admin: 8.7/10 (Category avg: 8.6/10)

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?

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?

  • Application: 8.8/10 (Category avg: 8.5/10)
  • Managed Service: 8.9/10 (Category avg: 8.3/10)
  • Natural Language Understanding: 8.7/10 (Category avg: 8.3/10)
  • Ease of Admin: 8.3/10 (Category avg: 8.6/10)

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?

Cloudera

Cloudera is the only hybrid data and AI platform company that large organizations trust to bring AI to their data anywhere it lives. Unlike other providers, Cloudera delivers a consistent cloud experience that converges public clouds, on-prem data centers, and the edge, leveraging a proven open-source foundation. As the pioneer in big data, Cloudera empowers businesses to apply AI and assert control over 100% of their data, in all forms, improving security, governance, and real-time and predictive insights. The world’s largest brands across all industries rely on Cloudera to transform decision-making and ultimately boost bottom lines, safeguard against threats, and save lives. Cloudera Anywhere Cloud™: Build and scale applications across any environment. The modular hybrid data and AI platform engineered for the agentic era empowers teams to deploy production-grade data and AI workloads across multi-cloud, on-premises, and sovereign environments while maintaining digital sovereignty. The Cloudera data and AI platform includes: Cloudera AI: Deploy and scale any AI model, anywhere. Cloudera brings compute to governed data where it lives for Private AI anywhere by design. Complete control, security, and governance of mission-critical data, models, agents, and inference ensure faster sovereign AI deployments. Cloudera Data-in-Motion: Make fast decisions from real-time data anywhere. Move data with any structure from any source to any destination seamlessly across hybrid environments, enabling in-the-moment business-critical decisions by processing and analyzing real-time data anywhere, from the edge to AI, as business happens. Cloudera Open Data Lakehouse: Process any data, anywhere, for actionable insights. Make smart decisions with an open data lakehouse powered by Apache Iceberg that delivers trusted, reliable, and unified data to fuel agents, AI applications, and analytics, improving collaboration, breaking silos, and simplifying sharing. Cloudera Unified Data Fabric: Unify security and governance across the entire data estate. Move beyond fragmented data management: Break down silos and connect disparate data sources intelligently and securely to provide a unified view of all organizational data and centralized end-to-end control across complex hybrid data environments.

Average Rating: 4.2/5.0

Total Reviews: 208

How Do G2 Users Rate Cloudera?

  • Application: 9.5/10 (Category avg: 8.5/10)
  • Managed Service: 9.2/10 (Category avg: 8.3/10)
  • Natural Language Understanding: 9.7/10 (Category avg: 8.3/10)
  • Ease of Admin: 8.0/10 (Category avg: 8.6/10)

Who Is the Company Behind Cloudera?

  • Seller: Cloudera
  • Company Website:
  • Year Founded: 2008
  • HQ Location: Santa Clara, CA
  • LinkedIn® Page: www.linkedin.com
    3,505 employees on LinkedIn®
  • Phone: 888-789-1488

Who Uses This Product?

  • Who Uses This: Data Engineer, Software Engineer
  • Top Industries: Information Technology and Services, Banking
  • Company Size: 39% Large, 33% Small

What Do G2 Reviewers Say About Cloudera?

AI-generated summary from verified user reviews

Pros
  • Users appreciate the ease of use of Cloudera, enjoying its straightforward installation and maintenance for analytics.
  • Users commend Cloudera for its exceptional scalability, enabling seamless integration and efficient big data management.
  • Users commend Cloudera for its robust security, ideal for high-scale enterprise-level data engineering tasks.
  • Users appreciate the easy and secure data management provided by Cloudera, enabling smart analytics and decision-making.
  • Users value the user-friendly features of Cloudera, which simplify big data handling and improve workflow efficiency.
Cons
  • Users express concerns over the high costs associated with Cloudera, making it less accessible for some.
  • Users find Cloudera to have a complex setup and operational challenges, especially for inexperienced professionals.
  • Users find Cloudera setup to be difficult to learn, especially for beginners navigating its complex interface.
  • Users find the documentation lacking, which hampers navigating data configurations and resolving errors effectively.
  • Users face significant access issues with Cloudera, including limited documentation and authorization errors during task execution.

What Are Recent G2 Reviews of Cloudera?

What Are G2 Users Discussing About Cloudera?

IBM watsonx.data

IBM® watsonx.data® helps you access, integrate and understand all your data —structured and unstructured—across any environment. It optimizes workloads for price and performance while enforcing consistent governance across sources, formats and teams. Watch the demo to learn how watsonx.data empowers you to build gen AI apps and powerful AI agents. Free Trial available: https://ibm.biz/Watsonx-data_Trial

Average Rating: 4.4/5.0

Total Reviews: 171

G2 Deal: Save 30% on your first monthly or annual subscription. Offer ends 15 April 2026.

Get 30% off your new monthly or annual watsonx.data Enterprise subscription. Optimize data workloads at a fraction of the cost. Offer ends 15 April 2026.

Price: ~~61.81~~ → 88.30

View this exclusive G2 deal

How Do G2 Users Rate IBM watsonx.data?

  • Application: 5.8/10 (Category avg: 8.5/10)
  • Managed Service: 7.2/10 (Category avg: 8.3/10)
  • Natural Language Understanding: 8.0/10 (Category avg: 8.3/10)
  • Ease of Admin: 8.0/10 (Category avg: 8.6/10)

Who Is the Company Behind IBM watsonx.data?

  • Seller: IBM
  • Company Website:
  • Year Founded: 1911
  • HQ Location: Armonk, New York, United States
  • LinkedIn® Page: www.linkedin.com
    344,328 employees on LinkedIn®
  • Ownership: SWX:IBM

Who Uses This Product?

  • Who Uses This: Software Engineer, Software Developer
  • Top Industries: Computer Software, Information Technology and Services
  • Company Size: 34% Small, 32% Large

What Do G2 Reviewers Say About IBM watsonx.data?

AI-generated summary from verified user reviews

Pros
  • Users appreciate the ease of use of IBM watsonx.data, facilitating efficient data management and analytics integration.
  • Users value the efficient querying and analytics capabilities of IBM watsonx.data, enhancing data handling for complex tasks.
  • Users value the efficient data management capabilities of IBM watsonx.data, enhancing analytics and reducing costs effectively.
  • Users value the robust integration with various AI tools, enhancing analytics and simplifying model management within IBM watsonx.data.
  • Users value the fast querying and analytics capabilities of IBM watsonx.data, enhancing insight extraction from large datasets.
Cons
  • Users face a steep learning curve with watsonx.data, especially regarding documentation and initial setup processes.
  • Users find the complexity of IBM watsonx.data challenging, requiring time and expertise for effective use.
  • Users find the pricing high and confusing, making it challenging for small teams to justify the expense.
  • Users face a difficult setup with IBM watsonx.data, requiring extensive configuration and detailed documentation for effective use.
  • Users find difficulty in setup and documentation, requiring time and expertise to effectively utilize watsonx.data.

What Are Recent G2 Reviews of IBM watsonx.data?

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?

  • Application: 9.2/10 (Category avg: 8.5/10)
  • Managed Service: 9.3/10 (Category avg: 8.3/10)
  • Natural Language Understanding: 8.9/10 (Category avg: 8.3/10)
  • Ease of Admin: 7.7/10 (Category avg: 8.6/10)

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?

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?

  • Application: 8.6/10 (Category avg: 8.5/10)
  • Managed Service: 9.1/10 (Category avg: 8.3/10)
  • Natural Language Understanding: 9.3/10 (Category avg: 8.3/10)
  • Ease of Admin: 8.4/10 (Category avg: 8.6/10)

Who Is the Company Behind Amazon SageMaker?

Who Uses This Product?

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

What Do G2 Reviewers Say About Amazon SageMaker?

AI-generated summary from verified user reviews

Pros
  • Users find the ease of use of Amazon SageMaker exceptional, allowing quick 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?

Bijou Barry
BB
Researched and written by Bijou Barry
Updated