Top 9 AI Governance Tools 2024

AI governance tools are software or platforms that help organizations manage and regulate the development, deployment, and use of artificial intelligence (AI) systems. By supporting disciplined AI governance, these tools provide features and functionalities that help organizations implement ethical and responsible AI practices – and also create competitive advantage.
We analyzed the best AI governance software for different teams and organizations, their features, pricing, and strengths and weaknesses to help you determine the best tool for your business.

TABLE OF CONTENTS
ToggleFeatured Partners: Governance, Risk, and Compliance (GRC) SoftwareTop AI Governance Software ComparisonIBM Cloud Pak for Data: Best for End-to-end Governance Capabilities for AI ProjectsAmazon SageMaker: Best for Building & Training ML ModelsDataiku DSS: Best for CollaborationAzure Machine Learning: Best for Responsible AI Applications in Machine LearningDatatron MLOps Platform: Best for Risk Management & ComplianceQlik Staige: Best for AI-Powered Analytics & Data VisualizationMonitaur: Best for Regulated IndustryHolistic AI: Best for AI Risk Management & AuditingCredo AI: Best for AI-Powered Enterprise UseHow to Choose the Best AI Governance Software for Your BusinessHow We Evaluated the Best AI Governance SoftwareFrequently Asked Questions (FAQs) About AI Governance SoftwareBottom Line: AI Governance Software



Featured Partners: Governance, Risk, and Compliance (GRC) Software






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Top AI Governance Software Comparison
See the high-level feature and pricing comparison of the top-rated artificial intelligence governance tools and software to help you determine the best solution for your business.

Free trial
Starting price
Top feature

IBM Cloud Pak for Data
60 days
$350 per month per virtual processor core
Identify sensitive data and enforce data protection rules dynamically

Amazon SageMaker
60 days
Pay-as-you-go
Automatic Model Tuning (AMT)

Dataiku DSS
14 days
Available upon request
Provides visual interactive data preparation (80+ processors)

Azure Machine Learning
12 months
Pay-as-you-go
Responsible AI to build explainable models

Datatron MLOps Platform
No
(Demo, hands-on trial)
Available upon request
Provides an overall health of all models in the system

Qlik Staige
30 days
$20 per user per month, billed annually
Native predictive analytics with full explainability

Monitaur
No

Monitors for bias, data drift, and anomalies

Holistic AI
No
Available upon request
AI risk posture reporting

Credo AI
No
Available upon request
AI adoption tracking

IBM Cloud Pak for Data: Best for End-to-end Governance Capabilities for AI Projects
Overall rating: 4.6

Cost: 4.1
Feature set: 5
Ease of use: 4.5
Customer support: 5

IBM Cloud Pak for Data is an integrated data and AI platform that helps organizations accelerate their journey to AI-driven insights. Built on a multicloud architecture, it provides a unified view of data and AI services, enabling data engineers, data scientists, and business analysts to collaborate and build AI models faster.
The platform includes a wide array of governance features, including data cataloging, data lineage, data quality monitoring, and compliance management. IBM’s end-to-end governance capabilities allow organizations to govern their AI projects by addressing key concerns such as data privacy, security, compliance, and model explainability.
IBM Cloud Pak for Data as a Service deployments dashboard.
Pros & Cons

Pros
Cons

Responsive customer support.
Users reported the complexity of setup and configuration.

Address data quality issues.
It’s pricey for small businesses and startups.

Pricing
IBM requires intending buyers to contact their sales team for custom quotes. However, our research found that IBM Cloud Pak for Data Standard Option with 48 VPCs costs $19,824 per month and $237,888 per year. Meanwhile, the IBM Cloud Pak for Data Enterprise Option with 72 VPCs costs $59,400 per month and $712,800 billable annually.
Further research shows that the IBM Cloud Pak for Data standard edition costs $350 per month per virtual processor core, while the enterprise edition costs $699 per month per virtual processor core. You can also try the tool free of cost for 60 days before making a financial commitment.
Features

Data integration.
Data observability.
Identify sensitive data and enforce data protection rules dynamically.
Advanced data discovery.
360-degree views of enterprise data.
Available for self-hosting or as a managed service on IBM Cloud.

Visit IBM Cloud Pak for Data

Amazon SageMaker: Best for Building & Training ML Models
Overall rating: 4.6

Cost: 4.1
Feature set: 5
Ease of use: 4.5
Customer support: 5

Amazon SageMaker offers developers and data scientists an all-in-one integrated development environment (IDE) that lets you build, train, and deploy ML models at scale using tools like notebooks, debuggers, profilers, pipelines, and MLOps.
SageMaker provides various tools and capabilities, including built-in algorithms, a data-labeling feature, model tuning, automatic scaling, and hosting options. It simplifies machine learning workflow, from data preparation to model deployment, and offers an integrated development environment for managing the entire process. The platform enables you to manage and control access to your ML projects, models, and data, ensuring compliance, accountability, and transparency in your ML workflows.
SageMaker integrates with AWS services such as AWS Glue for data integration, AWS Lambda for serverless computing, and Amazon CloudWatch for monitoring and logging.
Visualizing Amazon SageMaker machine learning predictions with Amazon QuickSight.
Pros & Cons

Pros
Cons

Offers tools that enable developers to build generative AI applications.
The user interface can be improved.

Amazon SageMaker Canvas boosts collaboration between business analysts and data science teams.
Infrequently lags when processing large amounts of data.

Pricing
SageMaker offers two choices for payment: on-demand pricing that offers no minimum fees and no upfront commitments, and the SageMaker savings plans that provide a usage-based pricing model. You can scroll through the platform’s pricing page for your actual rate.
Features

No-code interface for building ML models.
Automatic Model Tuning (AMT).
The SageMaker Studio supports various fully managed integrated development environments (IDEs) for ML development, including JupyterLab, Code Editor based on Code-OSS (Visual Studio Code—Open Source), and RStudio.
SageMaker Canvas chat helps you create data preparation flows using LLMs.

Visit Amazon SageMaker

Dataiku DSS: Best for Collaboration
Overall rating: 3.5

Cost: 2.2
Feature set: 3.8
Ease of use: 4.5
Customer support: 3.5

Dataiku DSS (Data Science Studio) is a collaborative and end-to-end data science platform that enables data teams to build, deploy, and monitor predictive analytics and machine learning models. It provides a visual interface for data preparation, analysis, and AI modeling, as well as the ability to deploy models into production and monitor their performance.
Dataiku DSS strongly focuses on collaboration, allowing both technical users (coders) and business users (noncoders) to work together on data projects in a shared workspace. This collaborative feature enables data scientists, data analysts, business analysts, and AI consumers to contribute their expertise and insights to the data projects.
As part of its AI governance initiative, Dataiku Govern centralizes the tracking of multiple data initiatives, ensuring that the proper workflows and processes are in place to deliver Responsible AI. This centralized oversight is significant as companies scale their AI footprint and embark on generative AI initiatives, as it helps maintain visibility into the various projects and reduces the risk of potential issues.
Dataiku DSS in-database charts.
Pros & Cons

Pros
Cons

Built-in project wikis for centralized knowledge base management.
Lacks pricing transparency.

It is helpful for both coders and noncoders and enables them to work with both structured data and unstructured data.
Customer support could be better.

Pricing
The following are the different plans offered by Dataiku. To get your actual rate, contact the company for a custom quote. The company also offers a 14-day free trial.

Free edition: Free for up to three users.
Discover: Best for small teams of up to five users.
Business: Best for midsize teams of up to 20 users.
Enterprise: Best for scalable automation and governance.

Features
Dataiku DSS offers several capabilities, including data preparation, data visualization, machine learning, DataOps, MLOps, analytic apps, collaboration, governance, explainability, and architecture. It also supports functionalities through plug-ins and connectors such as OpenAI GPT, Geo Router, GDPR, Splunk, Collibra Connector, and more.

Allows users to build generative AI applications at an enterprise scale.
Offers visual ML and automated features preprocessing.
Enables users to install and run on their cloud—AWS, Azure, and GCP.
Provides visual interactive data preparation (80+ processors).

Visit Dataiku DSS

Azure Machine Learning: Best for Responsible AI Applications in Machine Learning
Overall rating: 4.3

Cost: 4.2
Feature set: 5
Ease of use: 4.5
Customer support: 2.5

Azure Machine Learning supports AI governance, by providing tools, services, and frameworks to streamline the machine learning process, from data preparation and model training to deployment and monitoring, enabling data scientists and developers to build, train, and deploy machine learning models at scale.
Azure Machine Learning provides tools and features that enable users to implement Responsible AI practices in their machine learning projects. This includes features such as model interpretability, fairness, and transparency that help data scientists and developers understand and mitigate potential biases, ensure the ethical use of their models, and maintain transparency in the decision-making process.
Azure Machine Learning responsible AI is built on six core principles: fairness, reliability and safety, privacy and security, inclusiveness, transparency, and accountability in machine learning models and processes. These principles are, in essence, the core of AI governance.
Responsible AI dashboard in Azure ML studio.
Pros & Cons

Pros
Cons

Built-in and custom policies for compliance management.
Quality of support can be

Detect drift and maintain model accuracy.
Less flexibility in terms of integration.

Pricing
Azure offers three pricing options: pay-as-you-go, Azure savings plan for compute, and reservations. You can consult the Azure pricing table to learn about your rates or contact the company sales team for personalized quotes.
Features

Drag-and-drop designer.
Built-in support for open-source libraries and frameworks like Scikit-learn, PyTorch, TensorFlow, Keras, and Ray RLlib.
Built-in governance, security, and compliance for running machine learning workloads.
Responsible AI to build explainable models.
Error analysis.

Visit Azure Machine Learning

Datatron MLOps Platform: Best for Risk Management & Compliance
Overall rating: 3.7

Cost: 2.4
Feature set: 4.8
Ease of use: 4
Customer support: 3

Datatron MLOps offers an AI model monitoring and AI governance platform that helps organizations manage and optimize their MLOps. The platform provides robust monitoring and tracking features to ensure that models perform as expected and meet compliance standards. This includes real-time model performance monitoring, data drift identification, and setting up alerts and notifications for any anomalies or deviations.
The platform provides a unified dashboard to monitor the performance and health of deployed models in real time, allowing organizations to identify and address issues proactively. Datatron’s explainability capability plays a critical role in risk management and compliance. It provides insights into how AI models make decisions, enabling organizations to understand and evaluate the potential biases or risks associated with these judgments.
This helps businesses ensure fairness, transparency, and accountability in their AI systems, which is particularly important in regulated industries.
The Datatron platform dashboard.
Pros & Cons

Pros
Cons

Anomaly detection capability.
Some users say the interface is complex.

Alerts and notifications via email, Slack, or PagerDuty.
Occasionally slow.

Pricing
Contact the company for a personalized quote.
Features

Provides an overall health of all models in the system.
Bias, drift, performance, anomaly detection.
You can set up monitoring and reporting based on custom metrics.
Activity log and audit trail.

Visit Datatron MLOps Platform

Qlik Staige: Best for AI-Powered Analytics & Data Visualization
Overall rating: 3.4

Cost: 2.7
Feature set: 3.5
Ease of use: 4
Customer support: 3.5

Qlik Staige is an AI governance solution enabling AI-powered analytics, allowing businesses to dynamize visualizations, generate natural language readouts that provide easy-to-understand summaries, and allow interactive, conversation-based experiences with data.
The platform empowers businesses to harness the capability of AI while maintaining control, security, and governance over their AI models and data. It provides data integration, quality assurance, and transformation capabilities to create AI-ready datasets. The tool facilitates the automation of machine learning processes, allowing analytics teams to generate predictions with explainability and integrate models in real time for comprehensive what-if analysis.
Qlik AutoML model metrics.
Pros & Cons

Pros
Cons

Alerting for data changes.
Expensive.

It lets you codevelop analyses with others.
Initial learning curve.

Pricing

Standard: $20 per user per month, billed annually. Ten users minimum.
Premium: Starts at $2,700 per month, billed annually. Supports 50 GB and 10 full users.
Enterprise: Custom quote.

Features

Interactive data visualizations and dashboards.
Augmented analytics with advanced AI.
Native predictive analytics with full explainability.
Real-time predictions via API.
AI-generated analyses and insights, including search and natural language interaction.

Visit Qlik Staige

Monitaur: Best for Regulated Industry
Overall rating: 3.3

Cost: 1.5
Feature set: 4.8
Ease of use: 4
Customer support: 2.5

Monitaur facilitates orchestration and collaboration among various teams and stakeholders involved in the AI model development and deployment process, including ML engineers, data scientists, compliance officers, underwriters, and executive decision-makers. Monitaur helps organizations demonstrate that their AI models are compliant and trustworthy by centralizing governance processes and providing a library of standard policy controls.
The platform is especially beneficial for regulated industries with strict standards and compliance requirements. Thanks to its centrally managed library, organizations in these industries can adhere to regulations and easily demonstrate compliance.
Monitaur AI governance overview dashboard.
Pros & Cons

Pros
Cons

Compliance assurance.
Average customer support score.

Centralized governance.
Lacks transparent pricing.

Pricing
Contact the company for a personalized quote.
Features

Monitors for bias, data drift, and anomalies.
Consolidate visibility across model projects and purposes, including AI, machine learning, LLM, GPT, and statistical models.
Compliance monitoring.
Risk management.

Visit Monitaur

Holistic AI: Best for AI Risk Management & Auditing
Overall rating: 3.7

Cost: 2.2
Feature set: 4.8
Ease of use: 4
Customer support: 3.5

Holistic AI’s Governance Platform offers a range of features and functionalities to address the various aspects of AI governance, including risk management and compliance. The platform enables organizations to conduct comprehensive audits of their AI systems and generates detailed audit reports documenting the system’s performance, vulnerabilities, and areas requiring improvement. The reporting functionality also includes context-specific impact analysis to understand the implications of AI systems on business processes and stakeholders.
Holistic AI supports regulation-specific assessments, ensuring your AI systems comply with relevant laws and regulations. It helps you map, mitigate, and monitor risks associated with specific rules, enabling you to comply.
Pros & Cons

Pros
Cons

Identify and mitigate bias.
Support can be better.

Comprehensive audit reporting.
Documentation could be improved.

Pricing
Contact the company for quotes.
Features

AI risk posture reporting.
AI third-party vendor management.
AI conformity assessment.
AI bias assessment.

Visit Holistic AI

Credo AI: Best for AI-Powered Enterprise Use
Overall rating: 3.8

Cost: 1.5
Feature set: 4.1
Ease of use: 4
Customer support: 4

The Credo AI governance platform caters to the needs of AI-powered enterprises by offering features such as a centralized repository of AI metadata, a risk center for visualizing AI risk and value, and automated governance reports for building trust with stakeholders. It also offers an AI registry for tracking AI initiatives, and an AI governance workspace for collaboration on AI use cases.
Credo AI generates automated governance reports, including model cards, impact assessments, reports, and dashboards, which can be shared with executives, board members, customers, and regulators to build trust and transparency around AI initiatives. The company’s AI registry feature provides visibility into the risk and value of all AI projects by registering them and capturing metadata to prioritize projects based on revenue potential, impact, and risk.
Credo AI risk center.
Pros & Cons

Pros
Cons

Automated governance reports.
Integration requires technical knowledge.

Flexible deployment—on-premise, public cloud, and private cloud.
Received a poor score for non-transparent pricing.

Pricing
Available upon request.
Features

AI adoption tracking.
AI risk management.
Artifacts for audit.
Generative AI guardrails.
Regulatory compliance.
Vendor risk assessment.

Visit Credo AI

How to Choose the Best AI Governance Software for Your Business
Many factors help determine the best AI governance software for your business. Some solutions excel in data and AI privacy regulations, while others are well suited for setting compliance standards, ethical guidelines, or risk assessment.
When shopping for the best AI governance solution, you should look for software that offers features such as data governance, model management, compliance automation, and monitoring capabilities. Depending on the nature of your business, you may need industry-specific AI governance software tailored to meet your sector’s unique requirements.
For example, healthcare organizations may need software compliant with HIPAA regulations, while financial institutions may require fraud detection and risk assessment tools. Conduct thorough research, evaluate your options, and consider your needs – and budget – to determine the best AI governance software for your business.
How We Evaluated the Best AI Governance Software
Pricing – 25%
We looked at the cost of the software and whether it provides value for the price. Tools that offer free trials and transparent pricing earned higher marks in this category.
Feature Set – 35%
The feature set of the AI governance software was a significant factor in our evaluation. We assessed the range of features offered,

Focus on ethics in AI.
Full range of security measures.
Handles model documentation.
Quality of reporting tools and dashboards.

We also considered whether the software could be customized to meet the specific needs of different organizations.
Ease of Use – 25%
We assessed the software’s user interface and user experience to determine how easy it is for users to navigate, set up, and use the software. We considered whether the software offers intuitive workflows and customization options.
Customer Support – 15%
We evaluated the level of customer support the software provider offers, including availability, responsiveness, and expertise. We looked at support channels, documentation, training resources, and user communities.
Frequently Asked Questions (FAQs) About AI Governance Software
How do AI governance practices align with ethical considerations?
AI governance practices align with AI ethical considerations by ensuring that AI systems are developed, deployed, and used to uphold ethical principles such as fairness, transparency, accountability, and privacy.
Which industries are leading in the adoption of AI governance?
Industries such as financial services, healthcare, and technology are leading the adoption of AI governance, as these sectors often deal with sensitive data and high-stakes decisions where ethical considerations are crucial.
How can businesses benefit from effective AI governance in the current landscape?

Increased trust and credibility with customers and stakeholders.
Reduced risks of bias, discrimination, and legal liabilities.
Improved data security and privacy protections.
Compliance with regulations and industry standards.

Bottom Line: AI Governance Software
As more and more organizations across various sectors continue to implement artificial intelligence solutions in their workflow, it becomes critical to have AI governance in place to ensure the responsible and ethical use of AI.
If AI is left unchecked, it can quickly become a source of biased decisions, privacy breaches, and other unintended consequences. Therefore, AI governance tools should not be an afterthought but instead an integral part of your company’s AI strategy.
For a full portrait of the AI vendors serving a wide array of business needs, read our in-depth guide: 150+ Top AI Companies 2024
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