Sold by: Atlan
Deployed on AWS
Atlan is the context layer for AI, the infrastructure that makes enterprise AI accurate, trustworthy, and scalable. Trusted by Mastercard, Workday, General Motors, and hundreds of global enterprises, Atlan connects your entire data estate and enriches it with the certified definitions, lineage, ownership, and access policies every AI agent needs. Native integrations with Amazon Redshift, S3, Glue, Athena, Lake Formation, and SageMaker Unified Studio mean your AWS stack is context-ready from day one. Recognized as a Leader in the Gartner Magic Quadrant for Data & Analytics Governance 2026 and Customer Favorite in the Forrester Wave for Data Governance Q3 2025.
4.5
Overview
Atlan is the context layer for AI: the infrastructure that stores, manages, and delivers the business understanding every agent needs to produce accurate, trustworthy outcomes at enterprise scale. That means certified definitions, data lineage, ownership, quality scores, and access policies, available to every agent in your stack, at runtime, through open standards. Without it, agents work with an incomplete picture of your business. With it, they're grounded in what your organization has already built, approved, and certified. Context Agents mine your entire AWS data estate automatically, from S3 and Glue through Redshift and Athena into SageMaker and BI, generating business context that used to take 9-12 months to document manually. 89% of AI-generated context is rated equal to or better than what a human analyst would write, and what took a year now takes two weeks. Context Engineering Studio then runs the full Context Development Lifecycle, Build, Test, Review, Approve, Deploy, Learn, where AI bootstraps context and simulates tests while domain experts resolve ambiguity, add tacit knowledge, and approve before anything ships. Every interaction feeds a compounding learning loop: memory, feedback, and traces make the context layer smarter with every agent query. Atlan's MCP Servers deliver context from a single layer to every agent harness through one open protocol, Amazon Quick Suite, SageMaker Unified Studio, Claude, Copilot, Cursor, Gemini, and any MCP-compatible tool, so every agent operates on the same certified understanding of your business. Native integrations span Amazon Redshift, Amazon S3, AWS Glue, Amazon Athena, AWS Lake Formation, and SageMaker Unified Studio, plus Snowflake, Databricks, dbt, Airflow, and leading BI platforms. Recognized as a Leader in the Gartner Magic Quadrant for Data & Analytics Governance 2026 and Customer Favorite in the Forrester Wave for Data Governance Q3 2025.
Highlights
- Context Mining automatically ingests from your entire AWS data estate, Redshift, S3, Glue, Athena, Lake Formation, and SageMaker. Context Agents then generate business context in two weeks. 89% of AI-generated context rated equal to or better than human-written.
- Context Engineering Studio runs the full Context Development Lifecycle, Build, Test, Review, Approve, Deploy, Learn, where AI bootstraps and simulates while domain experts resolve ambiguity and approve. A compounding learning loop improves context with every agent interaction.
- MCP Servers deliver unified context to every AI agent on AWS, Amazon Quick Suite, SageMaker Unified Studio, Claude, Copilot, and more, through one open protocol.
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Pricing
Pricing is based on the duration and terms of your contract with the vendor. This entitles you to a specified quantity of use for the contract duration. If you choose not to renew or replace your contract before it ends, access to these entitlements will expire.
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Dimension | Description | Cost/12 months |
|---|---|---|
Atlan Platform | Subscription to Atlan Platform starting at | $100,000.00 |
Dimensions summary
This listing offers one pricing dimension: a subscription to the Atlan Platform, measured in units. You buy under a contract commitment, and your cost scales with the number of units you subscribe to. There are no separate tiers, instance sizes, or usage add-ons to compare. The unit-based structure lets you size your subscription to your needs. The platform connects your business systems into one context layer for AI agents and analysts. Since only one dimension exists, your pricing depends on the unit quantity you select for your contract term.
Top-of-mind questions for buyers
The pricing table lists the subscription as measured in units but does not define what one unit represents in concrete terms, such as connectors, users, or cataloged assets. Contact the vendor to confirm how units map to your specific deployment and how they are counted.
Your subscription unifies context across your data estate. It connects natively to 80+ enterprise systems, including data warehouses, BI tools, and pipeline orchestrators. It pulls lineage, query history, and quality signals into one context layer that AI agents and analysts query through the MCP server, SQL, and open APIs.
Your cost scales with the number of units you subscribe to under your contract. There are no separate tiers or instance sizes. To grow, you adjust your unit quantity. The vendor's data does not detail how added systems or expanded usage map to unit changes, so confirm scaling rules with the vendor.
atlan.com
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Software as a Service (SaaS)
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Resources
Vendor resources
Support
Vendor support
- Customers can raise support case via ask@atlan.com or via portal https://atlan.zendesk.com/auth/v2/login/signin
- https://ask.atlan.com would be doc site ( same as docs.atlan.com )
AWS infrastructure support
AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.
Updated weekly
By Atlan
By Alation Inc.
By Collibra
Top
10
In Data Catalogs, Data Governance
Top
10
In Data Catalogs, Data Governance, Master Data Management
Top
10
In Data Catalogs, Data Governance
Sentiment is AI generated from actual customer reviews on AWS and G2
Functionality
Ease of use
Customer service
Cost effectiveness
Positive reviews
Mixed reviews
Negative reviews
AI generated from product descriptions
Automated Data Discovery and Context Generation
Automatically ingests from AWS data estate including Redshift, S3, Glue, Athena, Lake Formation, and SageMaker to generate business context with certified definitions, lineage, ownership, and quality scores in two weeks.
Context Development Lifecycle Management
Provides Build, Test, Review, Approve, Deploy, and Learn stages where AI bootstraps context and simulates tests while domain experts resolve ambiguity and approve before deployment.
Multi-Agent Context Delivery Protocol
Delivers unified context through MCP Servers to multiple AI agents including Amazon Quick Suite, SageMaker Unified Studio, Claude, Copilot, Cursor, and Gemini via a single open protocol.
Native AWS Data Platform Integrations
Natively integrates with Amazon Redshift, S3, Glue, Athena, Lake Formation, and SageMaker Unified Studio, plus Snowflake, Databricks, dbt, Airflow, and leading BI platforms.
Compounding Learning Loop
Continuously improves context quality through memory, feedback, and traces from every agent interaction, enabling the context layer to become smarter with each query.
Metadata Centralization
Centralizes metadata from disparate sources into a unified platform for discovering, describing, governing, and managing data assets including data, BI reports, and AI models.
Behavioral Analysis Engine
Incorporates a Behavioral Analysis Engine to provide advanced analytics and insights across data assets.
Data Lineage and Tracking
Enables documentation of insights and tracking of data lineage across teams for transparency and compliance purposes.
Self-Service Analytics
Supports self-service analytics capabilities allowing users to independently discover and analyze data assets.
AI Governance Framework
Provides an AI governance framework that ensures data quality, transparency, and compliance for AI initiatives.
AI Governance Framework
Active metadata-based governance with rules, processes and responsibilities to ensure ethical AI practices, mitigate risk, adhere to legal requirements, and protect privacy
Automated Data Lineage
End-to-end lineage tracking providing transparency into data transformation and flow across systems, including both summary-level business lineage and detailed technical lineage
Unified Data Catalog
Multi-cloud and hybrid environment data discovery with business context including data origin, ownership, usage patterns, and access to reports, AI models and data products
Data Quality Automation
Automated monitoring and rule management system for enterprise-wide data quality management replacing manual processes
Privacy and Compliance Workflow
Centralized automation of privacy workflows to operationalize privacy requirements and address global regulatory compliance
Standard contract
No
No
Customer reviews
Transportation/Trucking/Railroad
Visual Design, Collaboration, and Outstanding Customer Support
Reviewed on Jul 31, 2026
Review provided by G2
What do you like best about the product?
The support and enablement team has been excellent throughout the journey, and the platform is continuously evolving with new capabilities and improvements. It is a highly collaborative, user-friendly tool that makes it easy for both technical and business users to work together effectively.
What do you dislike about the product?
Some connectors that would be necessary for us are still missing, as well as some workflow and policy capabilities that would help us automate governance processes more effectively. While the platform’s continuous evolution is certainly a positive aspect, it also creates additional effort to assess, test, and adopt new features, which can sometimes make it difficult to stay focused on the original objectives. In addition, the platform is not yet able to automatically detect certain types of information, such as PII and other data classifications, which increases the manual effort required for governance and compliance activities.
What problems is the product solving and how is that benefiting you?
Atlan solves our lack of visibility into what data exists, where it lives, and whether it's trustworthy, previously that knowledge lived only in a few people's heads. It gives us searchable data discovery, end-to-end lineage, and clear ownership/documentation in one place. This has cut down repetitive "where do I find X" questions, sped up root-cause analysis when reports break, and reduced our dependency on a handful of key people. Overall, it's made data more self-service and trustworthy across both technical and business teams.
Keith G.
Atlan as a Central Metadata Hub with Powerful Lineage and AI-Assisted Documentation
Reviewed on Jul 14, 2026
Review provided by G2
What do you like best about the product?
Atlan has become much more than a catalog for us. It acts as a central metadata platform that helps connect technical assets, business context, ownership, and lineage in a way that is accessible to both engineers and business users.
The lineage capabilities provide valuable visibility into how data flows across our ecosystem, while the business context and stewardship features help improve trust in enterprise data. We have also seen significant value from Atlan's AI-assisted metadata capabilities, which helped accelerate documentation activities across a large number of assets.
Another differentiator has been the vendor partnership. The Atlan team has been highly engaged, responsive to feedback, and willing to collaborate on emerging use cases involving Snowflake, semantic models, and AI-driven data experiences
The lineage capabilities provide valuable visibility into how data flows across our ecosystem, while the business context and stewardship features help improve trust in enterprise data. We have also seen significant value from Atlan's AI-assisted metadata capabilities, which helped accelerate documentation activities across a large number of assets.
Another differentiator has been the vendor partnership. The Atlan team has been highly engaged, responsive to feedback, and willing to collaborate on emerging use cases involving Snowflake, semantic models, and AI-driven data experiences
What do you dislike about the product?
The platform is evolving quickly, which is generally positive, but to realize the full value of Atlan, you need well-defined stewardship processes, ownership models, and standards.
Some of the more advanced capabilities require organizational change management and process maturity to be successful. However, we found that these challenges were more related to governance adoption than limitations of the platform itself.
Some of the more advanced capabilities require organizational change management and process maturity to be successful. However, we found that these challenges were more related to governance adoption than limitations of the platform itself.
What problems is the product solving and how is that benefiting you?
Atlan has helped us establish a trusted metadata foundation for our Snowflake environment by improving visibility into data assets, lineage, ownership, and business definitions.
The platform has supported our efforts to enable self-service discovery, improve onboarding, and prepare for AI-powered use cases. The goal is for teams to spend less time searching for information or relying on tribal knowledge and more time focusing on delivering business value.
It has also become an important component of initiatives involving Snowflake Intelligence, semantic modeling, data products, and AI-driven data experiences by ensuring that trusted metadata and context are available wherever users need it.
The platform has supported our efforts to enable self-service discovery, improve onboarding, and prepare for AI-powered use cases. The goal is for teams to spend less time searching for information or relying on tribal knowledge and more time focusing on delivering business value.
It has also become an important component of initiatives involving Snowflake Intelligence, semantic modeling, data products, and AI-driven data experiences by ensuring that trusted metadata and context are available wherever users need it.
Manufacturing
Great Lineage and Data Cataloging with Easy Integrations but slow
Reviewed on May 27, 2026
Review provided by G2
What do you like best about the product?
Great lineage and data cataloging functionality. Easy integrations, competitive pricing with good support.
What do you dislike about the product?
The platform can feel sluggish at times. The UI is bland. The initial AI features were incomplete.
What problems is the product solving and how is that benefiting you?
We had a lack of data knowledge across the company. None of our data was AI ready. Atlan is helping us solve both of those problems.
Retail
Great Service and Product—Easy, Intuitive, and Accessible
Reviewed on May 18, 2026
Review provided by G2
What do you like best about the product?
Great service, great product, easy to use
What do you dislike about the product?
Nothing jumps out to me overall very intuitive and accessible
What problems is the product solving and how is that benefiting you?
UNderstanding lineage and how data is flowing and how calculations are performed
Manufacturing
Outstanding Support and Effortless User Adoption
Reviewed on May 14, 2026
Review provided by G2
What do you like best about the product?
The product support and the ease of adoption for users.
What do you dislike about the product?
The sales process could be a bit smoother, but overall no issues
What problems is the product solving and how is that benefiting you?
The Data Product Marketplace is critical to our Data & AI Strategy, a one stop shop to search, discover, learn, contribute and request access