Amazon Quick is AWS’s business intelligence and agentic-assistant platform, and its knowledge bases index documents and app content so its chat and dashboards can search across them. AWS’s supported-integrations table lists 61 integrations, and six of them can create a knowledge base. Atlan is a dedicated enterprise data catalog: it tracks schema, lineage, ownership, and business glossary terms across an entire estate, AWS included, and hands that governed context to AI agents through an MCP server. The two get compared because a searchable knowledge base sounds like a catalog. It answers a different question.
This comparison uses six criteria, stated up front rather than defaulted: primary purpose, cross-cloud reach, metadata depth, governance and access control, AI-agent and MCP accessibility, and cost. Atlan doesn’t win every row by default here. Amazon Quick genuinely wins on speed of setup for an AWS-only BI team and on bundling BI, search, and automation into one AWS bill. What follows breaks down where each tool actually earns its place, and where AWS-native teams end up needing both.
| Dimension | Amazon Quick | Atlan |
|---|---|---|
| What it is | AWS’s BI and agentic-assistant platform, evolved from Amazon QuickSight | Enterprise data catalog and context layer for AI agents |
| What it does | Answers questions and automates workflows using indexed documents, apps, and data | Tracks schema, lineage, ownership, and glossary terms; serves that context to humans and agents |
| Who owns it | Business analysts and BI teams inside AWS | Data governance, platform, and data engineering teams across the whole estate |
| Primary data surface | 61 listed integrations, six of which can build a knowledge base for search | Schema, lineage, and metadata read directly from source systems, not just indexed content |
| AI-agent access | MCP client; connects out to remote MCP servers and tools | MCP server; external agents query Atlan’s governed context directly |
| Governance model | Document-level ACLs on four knowledge base sources, plus AWS IAM and compliance certifications | Persona-based governance, access policies, and quality signals applied consistently across every connected platform |
| Cost model | Five plans from $0 to $100/user/month, with a $250/account monthly fee on Professional and Enterprise | Platform pricing scoped to catalog footprint and AI-agent use cases |
| Best for | AWS-committed teams that want BI, chat, and automation in one product | Teams that need a governed, cross-platform catalog feeding AI agents trustworthy context |
Amazon Quick vs Atlan: what’s actually being compared?
Amazon Quick and Atlan solve adjacent but different problems, and most of the confusion starts with vocabulary: Quick’s indexed content store is called a knowledge base, and a reader searching for a data catalog reasonably assumes the two are interchangeable.
AWS renamed Amazon QuickSight to Amazon Quick Suite on 9 October 2025, according to AWS’s own announcement, folding BI dashboards together with Quick Research, Quick Automate, and Quick Flows into one agentic product. The live user guide and pricing page now say Amazon Quick, and the documentation has moved to docs.aws.amazon.com/quick/. This piece keeps “Quick Suite” in the title to match how readers still search for it, and uses AWS’s current name in the text.
A knowledge base is a real capability. It just isn’t the same capability as a data catalog for AI or the kind of system AI agents for data catalog work assume exists. A catalog’s job is describing what data exists, how it’s structured, where it came from, and who’s allowed to see it. A knowledge base’s job is making a company’s documents, tickets, dashboards, and connected apps searchable inside a chat interface. Those overlap at the edges, particularly around enterprise search with AI, but a search index and a governed metadata system answer different questions.
What is an Amazon Quick knowledge base?
A knowledge base is the indexed content store inside Amazon Quick. It pulls documents and files from connected sources into a searchable index that powers Quick’s chat, dashboards, and autonomous agents, and AWS keeps it in sync as those sources update.
The numbers are narrower than the product’s surface area suggests. AWS’s supported-integrations table lists 61 integrations, and exactly six of them can create a knowledge base: Amazon S3, Atlassian Confluence Cloud, Google Drive, Microsoft OneDrive, Microsoft SharePoint Online, and the Web Crawler. Everything else on that table is an Actions integration, which calls out to a tool rather than indexing it. Snowflake appears there once, as a Cortex Agent under Actions. Databricks does not appear on the table at all. Existing Amazon Q Business indexes can be reused through Bring Your Own Index, so teams that already indexed data there don’t have to redo the work, capped at two indexes per Region.
Core components of a Quick knowledge base
- Indexed content store: documents and files from connected sources, kept in sync as those sources update
- Six knowledge base sources: S3, Confluence Cloud, Google Drive, OneDrive, SharePoint Online, and the Web Crawler, per AWS’s integrations table
- Actions integrations: the other 55 entries call an external tool, including Model Context Protocol servers, which AWS states cannot be used as knowledge base data sources for document indexing
- Bring Your Own Index: an existing Amazon Q Business index becomes available in Quick without re-indexing, with a maximum of two per Region that cannot be raised. Under IAM Identity Center, AWS states users “only see content they have permission to access.” Without Identity Center, AWS states all Quick users automatically receive access to connected Q Business indexes, which drops the per-user and per-group distinctions Q Business enforced.
- Document-level ACLs: AWS documents per-document access control for S3, SharePoint, Confluence, and Google Drive knowledge bases, and says Quick “does not ingest documents that lack an associated ACL entry.” The setting is chosen at creation time and cannot be changed afterward.
A knowledge base answers “what does this document say.” The certified definition of a metric and the upstream table that populated a field sit elsewhere: AWS scopes that catalog metadata to Glue Data Catalog and Amazon DataZone. That’s where a semantic layer for AI agents or an MCP-connected data catalog picks up. Quick’s own semantic layer scopes to BI metric definitions, not estate-wide metadata.
What is Atlan’s approach to data cataloging?
Atlan is an enterprise data catalog and context layer that maps schema, lineage, ownership, and business meaning across an organization’s entire data estate, then exposes that governed context to both people and AI agents.
Where a knowledge base consolidates content for search, Atlan builds an Enterprise Data Graph: a structured map of every table, column, dashboard, and pipeline, enriched through metadata management for AI with glossary terms and ownership, kept current as systems change. That’s a data catalog built for humans and a context layer built for AI agents at runtime, the core split behind this whole comparison.
Core components of Atlan’s data catalog
- Enterprise Data Graph: schema, lineage, and relationships mapped across every connected platform, not just what’s been indexed for search
- Business glossary: certified definitions and metric ownership, the kind of structured metadata an AI agent needs to act on, not just read
- Column-level lineage: data lineage for AI that traces a field back through every transformation, across clouds
- Governance policies: role- and persona-based access rules enforced consistently, addressing the same zero-trust governance and AI agent governance concerns AWS’s own stack targets
- MCP server: exposes governed context directly to external AI agents, rather than requiring the agent to search raw files
For a team evaluating AI agents against human data discovery as the front door to their data estate, that governed-context layer is the part neither a knowledge base nor most native cloud catalogs ship with by default.
Amazon Quick vs Atlan: head-to-head comparison
The sharpest differences between Amazon Quick and Atlan show up in cross-cloud reach, metadata depth, and which direction MCP runs, not in raw integration counts.
| Dimension | Amazon Quick | Atlan |
|---|---|---|
| Primary focus | BI, chat, and workflow automation over indexed content | Governed metadata, lineage, and business context across the estate |
| Cross-cloud reach | Six source types can build a knowledge base; Snowflake is an Actions integration and Databricks is not on AWS’s table | Reads and governs schema/lineage natively across AWS, Snowflake, Databricks, and on-prem |
| Technical metadata | AWS scopes table, schema, and partition metadata to Glue Data Catalog and Amazon DataZone | Schema, column lineage, and relationships mapped as a core function |
| Business glossary | AWS documents glossary terms in Amazon DataZone and, since June 2026, in a Glue Data Catalog preview | Certified business glossary with ownership, tied to the underlying schema |
| Governance/access | Document-level ACLs on four knowledge base sources, set permanently at creation, plus AWS IAM | Persona-based governance and access policies applied uniformly across every connected platform |
| AI-agent accessibility | MCP client; pulls in tools and data from remote MCP servers | MCP server; agents query Atlan’s context directly as the source |
| Data movement | Indexes content into knowledge bases and keeps it in sync as sources update | Reads metadata only; never copies or moves the underlying data |
| Time to value | Fast for an AWS-only BI/chat rollout with existing integrations | Scales with the size of the estate being cataloged and governed |
| Cost model | Per-user subscription plus index-storage tiers and a $250 per account monthly fee on two plans | Platform pricing scoped to catalog footprint and agent use cases |
| Failure mode | Search answers stay only as good as what’s indexed | Requires upfront cataloging investment before the context payoff shows up |
Example. A retailer runs Redshift for its warehouse and Snowflake for a recently acquired brand’s analytics. Amazon Quick can chart both for BI dashboards and chat, answering “what were Q3 sales” from whichever source has the freshest data. The certified definition of “revenue” and the lineage behind it sit in a different layer, and in AWS’s own stack that layer is Glue Data Catalog or Amazon DataZone. Atlan’s business glossary and lineage, the same distinction covered in context layer vs knowledge graph, make that call explicit across clouds, so an agent querying Atlan’s MCP server gets the certified answer instead of a plausible one from whichever index matched the search terms.
How do Amazon Quick and Atlan work together?
For AWS-native teams already using Amazon Quick, the practical question isn’t which tool to rip out. It’s how to give Quick’s chat and agents access to context that’s actually governed, not just whatever a keyword search over indexed files surfaces.
Atlan as a connected data source
Amazon Quick connects to Redshift and other analytics sources for dashboards and chat. When those platforms are cataloged in Atlan, Quick’s dashboards query data whose schema and lineage stay tracked somewhere, not floating free.
Custom MCP integration for governed answers
Because Amazon Quick acts as an MCP client, a team can build a custom MCP server fronting Atlan’s context and register it with Quick’s agents, using the same AgentCore Runtime or Gateway pattern AWS documents for other external tools. That lets a Quick agent pull a certified metric definition from Atlan’s glossary instead of guessing from whichever document ranked highest in the index. Two AWS limits shape the design: MCP operations time out at a fixed 60 seconds and fail with HTTP 424, and AWS states MCP integrations cannot be used as knowledge base data sources for document indexing. Access decisions made in Atlan carry through to what the agent is allowed to retrieve, so neither product overrides the other’s permission model.
Start with Amazon Quick alone when the team is AWS-only, the estate is small, and search-grade answers over indexed documents are enough. Add Atlan once a second cloud enters the picture, once “which system is the source of truth” becomes a recurring argument, or once an AI agent needs to act on data rather than just summarize it. Context layer evaluation criteria and the build vs buy vs bundle framework both cover that call in more depth.
How Atlan approaches data catalog and AI-agent context together
Amazon Quick isn’t built to replace a data catalog, and AWS’s own governance for schema and access lives in separate services, Glue Data Catalog and DataZone. Those two reach further outside AWS than they used to: Glue catalog federation reads Snowflake Polaris and Databricks Unity Catalog, and SageMaker Lakehouse federated catalogs query 12 external sources in place, BigQuery and Azure SQL among them. That reach is query-time federation of tables. The glossary terms, ownership, and policies that make an answer certified still live per-service, and a team running more than one cloud ends up stitching them together. Most AWS-native teams don’t feel that until an agent needs to act on data, not just summarize it, closer to a knowledge graph for AI agents built for provenance than retrieval.
Atlan reads schema, lineage, and ownership from AWS services alongside Snowflake, Databricks, BigQuery, and on-prem sources, keeping that Enterprise Data Graph current as systems change. An agent connecting through Atlan’s MCP server gets the same governed context regardless of cloud, the same agent context layer architecture behind multi-cloud context. It only reads metadata; it never copies or moves the underlying data, where AWS documents Quick indexing content into knowledge bases and keeping it in sync.
At Mastercard, that scale requirement was the starting point:
"AI initiatives require more context than ever. Atlan's metadata lakehouse is configurable, intuitive, and able to scale to hundreds of millions of assets. As we're doing this, we're making life easier for data scientists and speeding up innovation."
— Andrew Reiskind, Chief Data Officer, Mastercard
A retailer running Amazon Quick for BI already has a search layer. What it’s missing, until it adds a catalog like Atlan, is the certified answer to which system owns a metric, why a number changed, and whether the agent answering a question is allowed to see the table it pulled from. That’s the seam a reference architecture for a context layer is built to close, and it’s worth weighing against building that layer by hand before deciding to stitch Quick, Glue, and DataZone together internally.
Choosing between Amazon Quick and Atlan for a data catalog
The real decision isn’t Amazon Quick versus Atlan. It’s whether search over indexed content is enough, or whether the team needs a governed record of what the data actually is, where it came from, and who’s allowed to touch it. Amazon Quick answers the first question well, at AWS-native speed and cost. Atlan exists for the second, across every cloud the estate runs on, not just the one AWS bills for.
AWS-only teams with a small estate and mainly BI-plus-chat needs may not need Atlan yet. Quick now carries document-level ACLs for S3, SharePoint, Confluence, and Google Drive knowledge bases, and AWS’s default there is strict: it won’t ingest a document that lacks an ACL entry. What changes the calculation is a second cloud, an AI agent that needs to act on data rather than summarize it, or a policy that has to hold the same way on every platform at once.
FAQs about Amazon Quick vs Atlan for data catalog
1. Is Amazon Quick a data catalog?
Not in the technical-metastore sense of the term. Amazon Quick’s knowledge bases index documents and app content for chat and BI dashboards. AWS scopes catalog metadata, meaning tables, schemas, partitions, and lineage, to AWS Glue Data Catalog and Amazon DataZone instead. A dedicated catalog like Atlan tracks that metadata across every connected platform, AWS included.
2. Where does lineage live in the AWS stack?
AWS documents lineage in Amazon DataZone and Amazon SageMaker Unified Studio, built on OpenLineage-compatible events captured from Glue, EMR, and Redshift connections. Amazon Quick’s own documentation covers knowledge bases, agents, and dashboards. Atlan captures lineage natively across AWS services and every other platform it connects to, including Snowflake and Databricks.
3. Can AI agents query Amazon Quick directly through MCP?
AWS documents Amazon Quick as an MCP client that calls out to remote MCP servers, including custom ones on Amazon Bedrock AgentCore. AWS also states MCP integrations cannot be used as knowledge base data sources for document indexing, and that MCP operations carry a fixed 60-second timeout. Atlan runs an MCP server, so external agents query its governed context directly.
4. What is an Amazon Quick knowledge base?
A knowledge base is the indexed content store behind Amazon Quick’s chat and dashboards. AWS’s supported-integrations table lists 61 integrations, and six of them can create a knowledge base: Amazon S3, Atlassian Confluence Cloud, Google Drive, Microsoft OneDrive, Microsoft SharePoint Online, and the Web Crawler. The rest are Actions integrations, which call a tool rather than index it. Snowflake appears once, as a Cortex Agent under Actions.
5. Do Amazon Quick and Atlan work together?
Yes. Amazon Quick connects to Atlan-governed sources like Snowflake and Redshift the same way it connects to any data source, and a custom MCP integration can let its agents pull curated business definitions from Atlan instead of raw file search. Atlan supplies the governed context; Amazon Quick supplies the BI and chat surface on top of it.
6. What does Atlan add that Amazon Quick’s knowledge bases don’t?
Atlan adds schema and column-level lineage, a governed business glossary, role-based access policies, and quality signals, tracked consistently across every platform, not just what’s indexed for search. It also runs an MCP server so external AI agents, not only Amazon Quick’s own assistant, can query that governed context directly.
7. How much does Amazon Quick cost?
AWS’s pricing page lists five plans. Free is $0 with 1 GB of index storage. Plus is $20 per user per month on an annual commitment, $25 billed monthly, with 10 GB. Max is $100 per user per month annual, $125 monthly, with 50 GB. Professional is $20 per user per month with 25 GB pooled, and Enterprise is $40 per user per month with 50 GB pooled. Both of those carry a $250 per account per month infrastructure fee on top. Enterprise overages run $3 per agent hour and $5 per GB per month.
Sources
- Reimagine business intelligence: Amazon QuickSight evolves to Amazon Quick Suite, AWS Business Intelligence Blog (2025). https://aws.amazon.com/blogs/business-intelligence/reimagine-business-intelligence-amazon-quicksight-evolves-to-amazon-quick-suite/
- How Amazon Quick works, AWS Documentation. https://docs.aws.amazon.com/quick/latest/userguide/how-quicksuite-works.html
- Supported integrations in Amazon Quick, AWS Documentation. https://docs.aws.amazon.com/quick/latest/userguide/supported-integrations.html
- Bring your own index in Amazon Quick, AWS Documentation. https://docs.aws.amazon.com/quick/latest/userguide/quick-byoa.html
- Overview of Amazon Q Business indexes in Amazon Quick, AWS Documentation. https://docs.aws.amazon.com/quick/latest/userguide/qbiz-indexes-overview.html
- Amazon Q Business availability change, AWS Documentation. https://docs.aws.amazon.com/amazonq/latest/qbusiness-ug/qbusiness-availability-change.html
- Amazon Quick pricing, AWS. https://aws.amazon.com/quicksuite/pricing/
- Integrate external tools with Amazon Quick Agents using Model Context Protocol (MCP), AWS Machine Learning Blog (2026). https://aws.amazon.com/blogs/machine-learning/integrate-external-tools-with-amazon-quick-agents-using-model-context-protocol-mcp/
- Federated catalogs in SageMaker Lakehouse, AWS Documentation. https://docs.aws.amazon.com/sagemaker-lakehouse-architecture/latest/userguide/federated-catalogs.html
- Introducing catalog federation for Apache Iceberg tables in the AWS Glue Data Catalog, AWS Big Data Blog (2025). https://aws.amazon.com/blogs/big-data/introducing-catalog-federation-for-apache-iceberg-tables-in-the-aws-glue-data-catalog/
