The Forrester Wave™: AI Governance Solutions, Q3 2025

AI Governance Solutions Vendor Product Comparisons 2025

Key features and pros and cons of top AI Governance Solutions products:

Credo AI - Leaders

Pros:

Focuses on AI governance exclusively with highest adoption rate by large global enterprises
Thought leader in responsible AI and AI policy
Excels at AI asset and controls management, transparency, and risk management
Superior asset catalog, policy management, policy compliance audit, accelerators, and UI/UX
Capabilities align to best-practice workflows for AI governance
Strong relationships, customer education, and responsible AI advocacy

Cons:

Abandoned ModelOps/AIOps capabilities when shifting from data science tasks to wider AI risk assessment
Data governance needs to be added to align with governance team purviews
More aggressive roadmap could help differentiate from others
Pricing and packaging strategy could be better aligned to enterprise scaling efforts

IBM - Leaders

Pros:

Applies own AI governance best practices to watsonx.governance solution
Decade of customer partnerships in AI and data
Strong presence in complex and highly regulated industries
Robust support for transparency, control, and process
Strong AI observability, AI policy management, AI policy compliance audit, and AI accelerators
R&D rapidly introduces sophisticated evaluations and controls for challenging model risks like security features for jailbreaking

Cons:

Utilization monitoring, integration and interoperability, and data governance capabilities are in other solutions rather than the governance tool
Vision, adoption, and pricing/packaging flexibility are solid but could provide more differentiation in scaling and risk mitigation for AI unknowns
Customers would like to see deeper risk assessment of models

Collibra - Strong Performers

Pros:

Brings semantic-driven data governance and intelligence to AI governance
AI asset catalog and data governance create strong bridge between data and AI
Single pane of glass for governing AI assets and managing policies
Can share and map governance artifacts between data and AI - a standout feature
Strong adoption through existing customer relationships
Partnerships across data and AI ecosystem

Cons:

ModelOps/AIOps does not provide as detailed risk assessment as others; missing statistical and performance tests
User interface is clunky and requires training for non-governance roles
Vision, innovation, and roadmap keep up with peers but bias toward data governance limits resources to set product apart
Pricing and packaging should be unified across data and AI

Dataiku - Strong Performers

Pros:

Solid AI governance solution tied closely to analytic and operational processes of AI development and release
Ensures data governance capabilities are part of overall AI responsibility
Solid AI asset catalog
Very flexible solution with new feature requests addressed within 3-6 months according to customers
All-in-one platform for AI and AI governance

Cons:

Policy management capabilities are limited, concentrating on linking policies over addressing the lifecycle
Lacks AI utilization monitoring tied to AI consumers
Innovation is currently a weak spot
Balancing AI enablement with governance is a tough trade-off
Recent turnover has impacted customer support

Monitaur - Strong Performers

Pros:

Doubling down on AI governance as core focus
Vision and pricing/packaging flexibility in tune with AI governance needs
Helps enterprises get AI governance up and running quickly
Standout accelerators to capture and manage AI policies and assets
Focus on training and educational references to uplevel AI literacy
Received most positive and consistent customer feedback - Customer Favorite
Strong responsiveness to customers

Cons:

Roadmap has inconsistency or incompleteness for given release dates
Partnerships are limited, lacking both partnership model and defined relationships with broader AI ecosystem
Integration and interoperability limited to custom integrations
AI quality and testing workflows, utilization monitoring, and data governance need improvement
Automate feature is not yet mature

Coralogix - Contenders

Pros:

Superior ModelOps/AIOps, AI utilization, and data governance
Granular evaluations for toxicity, hallucination, and security controls
Flexibility to create user-defined evaluations
Ability to link evaluations to imported policies and monitor systems
Window into end-user AI use and alignment with data governance
Strong visibility into not only what AI is doing but details needed to address issues

Cons:

Vision, roadmap, and partner ecosystem strategies need attention to convert from niche tool to solution
Policy management is limited, relying on other tools and sources
Limited accelerators and audits for security, privacy, and regulatory purposes
Narrow value proposition needs to address broader AI governance requirements
Growth putting pressure on ability to scale customer support
Did not provide reference customers for evaluation

Cranium - Contenders

Pros:

Differentiates by focusing on third-party AI risk
AI observability enables simulations of models and suggests remediations with code
Independently assesses models for stronger verification
Quantifies AI risk beyond leaderboards and model cards
Customers celebrate focus on third-party AI risk and strength of scanners
Helps influence AI vendors to be more transparent

Cons:

Roadmap prioritizes third-party risk capabilities while wider AI governance capabilities are on slower trajectory
AI asset catalog, AI policy management, AI accelerators, and UI/UX are limited to third-party risk and security vs. wider AI governance scope
Other AI governance roles and responsibilities supported through integration with other tools rather than natively
Still primarily a solution for risk organizations rather than broader governance

DataRobot - Contenders

Pros:

Strong partner strategy for cloud/SaaS, GPU/private cloud, and application platforms
Strategic relationships with NVIDIA and SAP
Integration and interoperability and ModelOps/AIOps capabilities stand out
Rich repository of metadata about models and production environment
Captive customer base from AI platform
Customers praise depth of explainability and flexibility

Cons:

AI observability is notable weakness, lacking lineage views of AI conditions
AI accelerators and AI regulatory compliance audit capabilities are limited
Vision, adoption, innovation, and pricing/packaging flexibility do not reinforce broader AI governance enablement
AI governance still positioned as add-on with expectation to use another tool
Customers would like to do AI governance all in one place

Monte Carlo - Contenders

Pros:

Data-first approach to trusted AI
ModelOps/AIOps and data governance bring profiling and lineage to pinpoint issues
View of AI utilization by asset
Integration and interoperability within AI data landscape
Strong communication and effective positioning across marketing channels
Customers like data pipeline to model visibility for remediation

Cons:

AI asset catalog, AI policy management, and AI accelerators are limited by data-observability-centric view
Capabilities that directly support governance activities are lacking
Adoption, innovation, and roadmap strategies lag others
Strong AI message not backed by capabilities that meet current enterprise requirements
Needs to add AI controls, expanded metadata capture for models, and observability into RAG systems
Did not provide reference customers for evaluation

Saidot - Contenders

Pros:

Solid vision driving customer adoption
Appropriate pricing and packaging flexibility
Covers basics of policy management
Strong AI, regulatory, security, and privacy accelerators
Robust graph-based taxonomy standard for AI assets
Clean user interface
Strong connections with regulators and consortiums

Cons:

Innovation, product roadmap, and partner ecosystem are weaknesses
AI asset catalog has limited AI governance viability without associated workflows, controls, observability, and audits
User interface too bare, lacking detail and workflows seen in other solutions
Emphasis on AI assets under management without underpinning capabilities ready for governance activity
Lagging release of operational AI governance features requires aggressive development
Only one reference customer responded to Forrester's outreach

FAQs

Q: Which vendors are Leaders in 2025?

A: Credo AI, IBM

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

A: Leaders in the Forrester Wave for AI Governance Solutions are vendors whose current offerings and strategies best position them to help enterprises scale AI deployments with trust and governance. These vendors demonstrate the strongest capabilities in AI asset and controls management, transparency, and risk management. They provide complete solutions that enable enterprises to scale AI beyond a handful of use cases, set standards for workflows, classify use risk, distribute development and ownership, undertake compliance audits, and remediate audit failures. Leaders typically have AI governance as their core mission or have successfully applied their own AI governance best practices to their solutions, with strong adoption rates among large global enterprises.

Q: How do I interpret a Forrester Wave?

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

Report:

Top Vendor Products for AI Governance Solutions in 2025

AI Governance Solutions Vendor Product Comparisons 2025

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FAQs

Q: Which vendors are Leaders in 2025?

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

Q: How do I interpret a Forrester Wave?

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