The Forrester Wave™: AI Platforms, Q3 2026

AI Platforms Vendor Product Comparisons 2026

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

Amazon Web Services - Leaders

Pros:

Outstanding infrastructure and scalable training/inferencing for traditional ML and foundation models
Strong tooling for data scientists to train, build, and deploy custom models
Rich array of models available on the platform including unique Nova Forge offering for custom pretrained models
Powerful and customizable platform for highly scalable AI enterprises
Outstanding partner ecosystem

Cons:

Lacks strong cross-platform data modeling capability
User experience cohesion across AI platform services lags competitors
Overall experience described as complex and fragmented by customers
Requires deep AWS expertise and substantial custom integration work

C3 AI - Leaders

Pros:

Cohesive development environment spanning data modeling through agent deployment
Dozens of prebuilt solution accelerators for industry-specific use cases
Excellent governance controls
Strong focus on high-value industry use cases
White glove implementation and ongoing service
Excellent or solid scores across all major functional categories

Cons:

Could improve data integration capabilities
Data science capabilities need enhancement
Foundation model capabilities require improvement
Strategy has not led to faster growth as some customers expected
Could strengthen partner ecosystem and go-to-market efforts beyond core industries

Google - Leaders

Pros:

Excellent capabilities for data management
Strong capabilities for data scientists to build, train, tune, and deploy models
Gemini family of models provides multitude of modalities natively
All-in vision on agents as future of enterprise work with Gemini Enterprise Agent Platform
Strong innovation strategy supported by DeepMind research
Roadmap focuses on tooling for complex agents with rigorous hardening

Cons:

Should extend capabilities to provide more enterprisewide and process-oriented context for agents
Limited but growing ability to completely model enterprises in data
Some customers express frustration with rapid changes to packaging and branding
Did not provide reference customers for evaluation

Palantir - Leaders

Pros:

Ontology tools take data modeling to the next level
Enables rapid building and operationalization of complex, mission-critical AI solutions at scale
Excellent data integration capabilities
Full application development tools
Strong governance and security certifications
Comprehensive forward-deployed engineering consulting services ensure outsized business value

Cons:

Could improve platform user interface to accelerate adoption of subsequent use cases
Could improve partner ecosystem by forging stronger alliances with domain specialists and system integrators
Charges premium for high level of implementation support
Did not provide reference customers for evaluation

Pegasystems - Leaders

Pros:

Agent and app development tools excel, especially for workflows via Blueprint capability
Strong innovation, roadmap execution, and activated partner ecosystem
User experience is cohesive and addresses multiple user needs
On par with peers in platform management and security
Customers appreciate ease of use for technical developers and business users in creating hybrid workflows

Cons:

Could add to and improve data science capabilities
Foundation model evaluation and tuning tools need improvement
Extensive feature set can create pronounced learning curve
Could widen go-to-market aperture beyond core verticals (banking, financial services)
Only one reference customer responded to Forrester's outreach

Databricks - Strong Performers

Pros:

Excellent data integration capabilities allowing any enterprise data source into data lake
Solid platform for training and building data science models
Agent Bricks offers effective tooling for enterprise agent development
Effective governance and security capabilities
Cohesive platform experience
Flexible and scalable runtime architecture across clouds

Cons:

Needs to improve data modeling for enterprise workflows to enable full potential of agents
Lacks strong AppGen tools
Must expand planned role beyond delivering insights to driving processes
Not all customers satisfied with Agent Bricks despite functional capabilities

DataRobot - Strong Performers

Pros:

Excellent project-oriented experience for ML model and foundation model development lifecycle
Data scientists, business developers, and AI engineers can collaborate easily
Solid tools for agentic and AI application development
Strong governance and model evaluation capabilities
Customers appreciate ability to enable diverse teams to deploy and manage models at scale
Vision of democratizing data science and enabling all users to build AI agents with guardrails

Cons:

Data modeling capabilities lag behind peers
Some customers note big data processing mostly achieved in different platform
Must expand capabilities beyond core data science heritage

Dataiku - Strong Performers

Pros:

Outstanding data science capabilities
Excellent model evaluation tools
Strong governance and platform management
Beautifully designed UI for multiple enterprise personas
LLM Mesh provides sophisticated governance and monitoring of foundation models
Customers highlight collaborative environment and ability to accelerate time to value

Cons:

Must work to expand process understanding and control for agents
Needs to enhance partner ecosystem and go-to-market efforts beyond core user community
Customers desire more flexibility in pricing for rapidly emerging AI capabilities
Some customers have not yet used newer agentic capabilities

IBM - Strong Performers

Pros:

Excels at data science capabilities for wide variety of model types, training methods, and tuning techniques
Broad family of foundation models (Granite) for unique use cases
Deep data science heritage for complex AI initiatives across industries
Extensive global services and large partner network
Customers appreciate breadth and depth of data science capabilities

Cons:

Lags in data modeling capabilities
Weak agent development tools
Lacks strong AppGen tools
Governance controls need improvement
Needs to enable developers to create user interface layer for AI solutions
Some customers confused about what comprises IBM's AI offerings
Did not provide reference customers for evaluation

Microsoft - Strong Performers

Pros:

Comprehensive support for AI needs of all types
Notable runtime architecture serves as powerful foundation for enterprise agents
Excellent data integration functionality for data science and agentic context management
Solid data modeling capabilities
Deep understanding of customers' systems of work with Microsoft Fabric
Increased investment in native foundation model families (MAI models)

Cons:

Struggled to clearly differentiate agentic capabilities or expand on early Copilot lead
Platform potential constrained by lack of cohesive experiences across tools and interfaces
Customers can struggle connecting products for strategic success
Did not provide reference customers for evaluation

ServiceNow - Strong Performers

Pros:

Strong capabilities for process-oriented agent builders with knowledge graphs
Effective context management for agents
Cohesive experience for developers, business users, and AI engineers collaborating on agents
Strong governance controls
Impressive vision to build complete system of work with enterprise knowledge and workflows
Customers like vision and pace of innovation in agentic space

Cons:

Offers less support for training, evaluating, and tuning ML and foundation models
Steep hill to climb connecting vision to real-world execution
Current vision somewhat ahead of customers' current capabilities
Must prove ability to integrate knowledge graphs into single enterprise view

Oracle - Contenders

Pros:

Powerful and scalable runtime architecture for building agentic apps at scale
Solid capabilities for building and deploying agents
Distinctive approach of enabling open-source and external agent builder platforms
Prebuilt integrations and templates for third-party frameworks
Customers value data security and privacy, flexible cloud deployments, and service engagement

Cons:

Must improve data modeling, integration, and data science capabilities
Needs to provide cohesive experience for multiple user types on same projects
Strategy of supporting third-party frameworks has meant less emphasis on internal innovation
Customers express concern over platform costs and foundation model performance

Salesforce - Contenders

Pros:

Solid capabilities and tools for creating agents using well-modeled data and context
Shifted to headless AI approach making Agentforce more appealing for broader enterprise domains
Beginning to capitalize on Mulesoft assets for AI orchestration
Early arrival to agentic-driven AI world

Cons:

Data integration and modeling lag peers, limiting potential to those invested in Data Cloud
Lacks significant native data science modeling and deployment capabilities
Must extend ability to manage and execute process-driven agentic apps
Vision and innovation lag other evaluated vendors
Customers report challenges using tools to build accurate, business-value-delivering agents
Did not provide reference customers for evaluation

SAS - Contenders

Pros:

Exceptional data science capabilities
Thoughtfully cohesive interface design balancing usability and power
Handles data ingestion, preparation, advanced predictive modeling, and real-time scoring in unified environment
Deep roots in analytical research
Customers value platform maturity for complex model and analytical development

Cons:

Gaps in AppGen and agent development impact competitive position
Roadmap and innovation lag peers
Must increase depth of tooling for agent and application development beyond analytics workflows
Customers question commitment to next-generation agentic application building
Only one reference customer responded to Forrester's outreach

UiPath - Contenders

Pros:

Agent development functionality shines with vast library of tools connecting to existing IT systems
Solid capabilities for building data models to support AI applications and workflows via Data Fabric
Deep expertise in automation extended to AI agents
Customers praise ability to quickly automate previously intractable processes across disparate systems

Cons:

Could improve by adding data science capabilities for predictive models
Needs data processing capabilities for very large datasets and streaming data
Roadmap could be strengthened with industry-specific solution accelerators for complex use cases
Steep learning curve to understand all built-in RPA agent tools

FAQs

Q: Which vendors are Leaders in 2026?

A: Amazon Web Services, C3 AI, Google, Palantir, Pegasystems

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

A: Leaders in the Forrester Wave have the highest combined scores for current offering and strategy. They demonstrate strong execution across comprehensive AI platform capabilities including data science, agent development, application building, and enterprise deployment. Leaders show clear vision for the future of AI platforms, particularly around agentic AI, and back this with innovation, robust roadmaps, and strong partner ecosystems. They are best positioned to carry enterprises into the future of AI-driven work transformation.

Q: How do I interpret a Forrester Wave?

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

Report:

Top Vendor Products for AI Platforms in 2026

AI Platforms Vendor Product Comparisons 2026

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FAQs

Q: Which vendors are Leaders in 2026?

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

Q: How do I interpret a Forrester Wave?

Reference