The Forrester Wave™: AI/ML Platforms, Q3 2024

AI/ML Platforms Vendor Product Comparisons 2024

Key features and pros and cons of top AI/ML Platforms products:

C3 AI - Leaders

Pros:

Strong capabilities in governance of data and models
Excellent applications and application development tools
Strong security features
Absolute commitment to implementation success
Bountiful library of prebuilt AI applications across industries and horizontal uses

Cons:

Could improve tooling for model training
Needs better prompt engineering management for genAI
Longer sales cycles and more complex change management
Focus on large organizations may limit broader market appeal

Databricks - Leaders

Pros:

Strong capabilities in data enrichment
Expansion of Unity Catalog provides deeper context about data across the lifecycle
Integration of MosaicML technology for pretraining foundation models
Strong ability to manage data across the ML lifecycle
Good interoperability of the platform with other tools
Multipronged strategy emphasizing data as fuel for AI

Cons:

Needs to add more tooling to support low-code users for data exploration
Must expand data exploration capabilities within Unity Catalog
Needs to improve training tooling experience for users with low-code needs

Google - Leaders

Pros:

Strengths in development for genAI
Strong AI infrastructure capabilities
Excellent technology ecosystem
Continues to outpace competitors in AI innovation, especially genAI
Strong roadmap to expand tooling for multirole AI teams
Large set of well-incentivized partners
Low-cost entry point

Cons:

Can improve Vertex AI by expanding tooling for noncoding users
Needs better data exploration tools for noncoding users
Needs to expand predictive application development tools for noncoding users

Palantir - Leaders

Pros:

Strong capabilities across data ingestion and preparation
Intuitive user interfaces and automation
Powerful platform with friendly tooling for multiple user types
Excellent mix of low-code and professional data science coding environments
Vision and roadmap for human-machine joint decision-making model
Significant success with customers and sustained growth
Strong support for multirole AI teams

Cons:

Needs to improve broader software ecosystem compared to peers
Must keep eye on go-to-market to raise profile in broader AI/ML market
Needs to build stronger set of external partnerships
Previous versions presented challenges for getting started

SAS - Leaders

Pros:

Strong capabilities for data ingestion and preparation
Platform shines with capabilities for model evaluation and assessment
Excellent support for mix of low-code and professional data science coding environments
Ability to operationalize models in third-party environments
Decades of experience in advancing analytics, machine learning, and AI
Beautifully designed tools accessing world-class analytical and AI methods

Cons:

Needs to expand linkage of predictive and genAI pipelining and applications
Strategy reinforces relevance to existing customers but isn't driving significant growth among new customers
Needs to reestablish forward-looking preeminence in market mindshare
Must win new generation of analytics and AI professionals

Altair - Contenders

Pros:

Exceptional ease of use for model training and evaluation tools
Thoughtfully designed tools for noncoding users
Breadth and depth of analytical and machine learning methods
Easy to learn and use by AI team members with varying expertise

Cons:

Needs to add more sophisticated application development tools
Must step up workload performance
Needs to improve data governance
Vision focuses more on portfolio fit than AI transformation of enterprises
Needs to boost roadmap to keep pace with platform capabilities of other vendors

Cloudera - Contenders

Pros:

Strong capabilities in data preparation
Excellent security features
Multicloud platform (runs on public and private clouds)
Unified platform that accommodates multiple workloads and use cases centered on data

Cons:

Needs to invest more in tools for training
Must improve application development capabilities
Needs to add approval workflows for governance
Stated value proposition insufficient in hypercompetitive market
Has unique opportunity to articulate vision for enterprise AI architecture but hasn't fully capitalized

Domino Data Lab - Contenders

Pros:

Strong capabilities in model governance
Excellent security certifications
Diverse set of open-source tools and technologies
Collaborative scaffolding to support large teams
Comprehensive end-to-end data platform for coding data scientists

Cons:

Needs to improve data exploration capabilities
Must add better tooling for generative application development
Needs better tools to help enterprises build full AI applications
Would benefit from bolder strategy for enterprise AI to get shortlisted more frequently

H2O.ai - Contenders

Pros:

Solid autoML for training models
Strong development tools for predictive and genAI
Good security features
End-to-end scope of platform
Built-in best practices from Kaggle Grandmasters
Ability to run platform air-gapped and/or cloud

Cons:

Needs to shore up data tools
Must improve workload performance
Needs stronger technical ecosystem
Needs to better differentiate from competitors to regain market momentum
Could selectively deepen partnerships with consulting and adjacent technology firms

Amazon Web Services - Strong Performers

Pros:

Added significant capabilities and functionalities over the years
Can ingest, train, and evaluate models effectively
Rich array of functionalities for data pipelining, model management, and deployment
Extensive capabilities leveraging wider AWS ecosystem
Marketplace mindset for ecosystem of advanced AI applications

Cons:

Vision for SageMaker and AI is functional but focus largely on data science role
Lags behind many vendors in tooling for training models that supports multiple user roles beyond data scientists
Lacks tooling and abstraction to appeal to greater variety of end users
Did not provide reference customers for evaluation

Dataiku - Strong Performers

Pros:

Strong capabilities for model training and evaluation
One of first vendors to formalize building and governing genAI applications with LLM Mesh
Platform's ease of use for business users
Good time to production
Intuitive experience for training, evaluating, and governing models for variety of end users

Cons:

Can improve platform by adding more native workload scalability and performance features
Needs pricing transparency
Must continue innovation to address needs of multirole, multidisciplinary AI teams

DataRobot - Strong Performers

Pros:

Strong tooling for users who like both low-code and professional data science coding environments
Superior tooling for explainability, governance, and interpretation of models
Platform designed to support collaboration between multiple stakeholders
Good support for both technical users and business users

Cons:

Can improve platform by adding more third-party security certifications
Must work hard and fast to avert threats from adjacent vendors now offering low-code capabilities
Needs to continue differentiating in increasingly competitive market

IBM - Strong Performers

Pros:

Strong capabilities in data preparation
Excellent tools for training, evaluating and governing models
Strong genAI application capabilities
Good technical ecosystem
Sophisticated yet easy-to-use tools for model training and genAI development
Can run in any cloud (public or private), allowing hyperscaler-agnostic approach

Cons:

Can improve platform by enhancing overall tools for AI application development
Tagline 'built for business' not differentiating as every vendor targets enterprise customers
Must distinguish itself beyond hyperscaler-agnostic positioning

Microsoft - Strong Performers

Pros:

Strong capabilities for data enrichment
Good model evaluation capabilities
Strong predictive and generative development tools
Secure access to OpenAI's foundation models
Powerful set of capabilities through complementary Azure ML and Azure AI

Cons:

Platform needs significant work in data exploration capabilities
Must improve training tools
Needs better data and model governance capabilities
Investment in OpenAI doesn't guarantee long-term differentiation
Must do more to construct ecosystem for deploying genAI applications at scale with confidence
Declined to participate in full Forrester Wave evaluation process

FAQs

Q: Which vendors are Leaders in 2024?

A: C3 AI, Databricks, Google, Palantir, SAS

Q: What does "Leader" mean in Forrester Wave terms?

A: Leaders in the Forrester Wave are vendors that have the strongest current offerings and strategies in the AI/ML platforms market. They demonstrate: 1) Strong capabilities across data management, model training and evaluation, governance, and application development for both predictive and generative AI; 2) A clear vision and roadmap for delivering industry-leading enterprise AI that keeps pace with or pushes the envelope of AI technology; 3) Tooling that maximizes productivity for multirole AI teams while enabling friction-free collaboration; 4) Strong market presence with significant customer adoption and revenue growth; and 5) Comprehensive strategies for helping enterprises transition to and become AI-native organizations.

Q: How do I interpret a Forrester Wave?

A: https://www.forrester.com/policies/forrester-wave-methodology/

Report:

Top Vendor Products for AI/ML Platforms in 2024

AI/ML Platforms Vendor Product Comparisons 2024

Pros:

Cons:

Pros:

Cons:

Pros:

Cons:

Pros:

Cons:

Pros:

Cons:

Pros:

Cons:

Pros:

Cons:

Pros:

Cons:

Pros:

Cons:

Pros:

Cons:

Pros:

Cons:

Pros:

Cons:

Pros:

Cons:

Pros:

Cons:

FAQs

Q: Which vendors are Leaders in 2024?

Q: What does "Leader" mean in Forrester Wave terms?

Q: How do I interpret a Forrester Wave?

Reference