--- title: "Amazon Quick Suite vs. Atlan for Data Catalog" url: "https://atlan.com/know/ai-agent/aws/amazon-quick-suite-vs-atlan-for-data-catalog/" description: "See how Amazon Quick Suite's Quick Index compares to Atlan on cross-cloud reach, metadata depth, governance, and MCP access for AI agents today." author: "Emily Winks" author_role: "Data Governance Expert" published: "2026-08-13" updated: "2026-08-13T00:00:00.000Z" --- --- Amazon Quick Suite is AWS's business intelligence and agentic-assistant platform, and its Quick Index feature connects over 50 built-in sources plus 1,000+ apps into a single searchable 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 Quick Index 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. Quick Suite 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 Suite | 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 | 50+ built-in connectors plus 1,000+ apps via OpenAPI/MCP, indexed for search | Schema, lineage, and metadata read directly from source systems, not just indexed content | | AI-agent access | MCP client; connects out to external MCP servers and tools | MCP server; external agents query Atlan's governed context directly | | Governance model | Inherits AWS IAM and compliance certifications (SOC, HIPAA, ISO 27001, GDPR) | Persona-based governance, access policies, and quality signals applied consistently across every connected platform | | Cost model | $20-$40/user/month plus index-storage tiers and a $250/account infrastructure fee | 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 Suite vs Atlan: what's actually being compared? Amazon Quick Suite and Atlan solve adjacent but different problems, and most of the confusion starts with a naming coincidence: Quick Suite's search feature is called "Quick Index," and a reader searching for a data catalog reasonably assumes the two are interchangeable. AWS renamed Amazon QuickSight to Amazon Quick Suite on October 9, 2025, according to [AWS's own announcement](https://aws.amazon.com/blogs/business-intelligence/reimagine-business-intelligence-amazon-quicksight-evolves-to-amazon-quick-suite/), folding BI dashboards together with Quick Research, Quick Automate, Quick Flows, and Quick Index into one agentic product. AWS shortened the name again to Amazon Quick in its 2026 documentation; this piece uses Amazon Quick Suite throughout to match how readers search for it. Quick Index is a real capability. It just isn't the same capability as a [data catalog for AI](https://atlan.com/know/data-catalog-for-ai/) or the kind of system [AI agents for data catalog](https://atlan.com/know/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. Quick Index'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](https://atlan.com/know/ai-agent/data-for-ai/enterprise-search-with-ai/), but a search index and a governed metadata system answer different questions. --- ## What is Amazon Quick Suite's Quick Index? Quick Index is the knowledge-consolidation layer inside Amazon Quick Suite. It securely pulls documents, files, and structured data from connected sources into a unified, searchable repository that powers Quick's chat, dashboards, and autonomous agents. According to [Amazon's own announcement](https://www.aboutamazon.com/news/aws/amazon-quick-suite-agentic-ai-aws-work), Quick Index ships with "over 50 built-in connectors for applications like Adobe Analytics, SharePoint, Snowflake, Google Drive, OneDrive, Outlook, ServiceNow, Databricks, Amazon Redshift, and Amazon S3," plus access to 1,000+ additional apps through OpenAPI or [Model Context Protocol](https://atlan.com/know/mcp-vs-function-calling/) connectors from providers like Atlassian, Asana, Box, and Zapier. Existing [Amazon Q Business indexes](https://docs.aws.amazon.com/quicksuite/latest/userguide/qbiz-indexes-overview.html) can also plug in directly, so teams that already indexed data there don't have to redo the work. ### Core components of Quick Index - **Unified repository**: documents, files, and structured/unstructured data consolidated into one searchable index - **50+ built-in connectors**: Snowflake, Databricks, Redshift, S3, SharePoint, ServiceNow, and more, per AWS's connector list - **1,000+ app connectors**: reached through OpenAPI and MCP integrations, extending well past AWS-native sources - **Amazon Q Business index reuse**: existing indexed content becomes available in Quick Suite without re-indexing - **Access-control inheritance**: [AWS's documentation](https://docs.aws.amazon.com/quicksuite/latest/userguide/qbiz-indexes-overview.html) states users "only see content they have permission to access," carried over from the source system's own permissions Quick Index answers "what does this document say," not "what's the certified definition of this metric" or "which upstream table populated this field." That's where a [semantic layer for AI agents](https://atlan.com/know/ai-agent/semantic-layer-for-ai-agents/) or an [MCP-connected data catalog](https://atlan.com/know/mcp-connected-data-catalog/) picks up. Quick Suite's own [semantic layer](https://atlan.com/know/ai-agent/aws/amazon-quick-suite-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](https://atlan.com/know/what-is-the-enterprise-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 Quick Index 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](https://atlan.com/know/ai-agent/data-for-ai/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](https://atlan.com/know/data-catalog-vs-context-layer/) 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](https://atlan.com/know/ai-agent/data-for-ai/types-of-metadata-for-ai-agents/), not just read - **Column-level lineage**: [data lineage for AI](https://atlan.com/know/ai-agent/data-for-ai/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](https://atlan.com/know/zero-trust-data-governance/) and [AI agent governance](https://atlan.com/know/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](https://atlan.com/know/ai-agents-vs-humans-data-discovery/) as the front door to their data estate, that governed-context layer is the part neither Quick Index nor most native cloud catalogs ship with by default. --- ## Amazon Quick Suite vs Atlan: head-to-head comparison The sharpest differences between Amazon Quick Suite and Atlan show up in cross-cloud reach, metadata depth, and which direction MCP runs, not in raw connector counts. | Dimension | Amazon Quick Suite | Atlan | |---|---|---| | Primary focus | BI, chat, and workflow automation over indexed content | Governed metadata, lineage, and business context across the estate | | Cross-cloud reach | Indexes Snowflake, Databricks, and 50+ other sources for search; governance stays AWS-scoped | Reads and governs schema/lineage natively across AWS, Snowflake, Databricks, and on-prem | | Technical metadata | Not documented as tracking table/column schema as a system of record | Schema, column lineage, and relationships mapped as a core function | | Business glossary | No dedicated glossary feature documented; Quick Suite's semantic layer scopes to BI metrics | Certified business glossary with ownership, tied to the underlying schema | | Governance/access | Inherits source-system permissions plus AWS IAM and compliance certifications | Persona-based governance and access policies applied uniformly across every connected platform | | AI-agent accessibility | MCP client; pulls in tools and data from external MCP servers | MCP server; agents query Atlan's context directly as the source | | Data movement | Indexes and stores content copies inside Quick Suite for retrieval | Reads metadata only; never copies or moves the underlying data | | Time to value | Fast for an AWS-only BI/chat rollout with existing connectors | Scales with the size of the estate being cataloged and governed | | Cost model | Per-user subscription plus index-storage tiers and a flat account fee | Platform pricing scoped to catalog footprint and agent use cases | | Failure mode | Search answers stay only as good as what's indexed; no cross-cloud governance layer | 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. Quick Suite can index both for BI dashboards and chat, answering "what were Q3 sales" from whichever source has the freshest data. It can't tell an analyst which system is the certified source of truth for "revenue," because that governance layer lives outside Quick Suite. Atlan's business glossary and lineage, the same distinction covered in [context layer vs knowledge graph](https://atlan.com/know/ai-agent/context-layer/context-layer-vs-knowledge-graph/), make that call explicit, 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 Suite and Atlan work together? For AWS-native teams already using Quick Suite, the practical question isn't which tool to rip out. It's how to give Quick Suite'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 Quick Index already connects to Snowflake, Redshift, and Databricks the same way it connects to any other data source. When those platforms are cataloged in Atlan, Quick Suite's dashboards query data whose schema and lineage stay tracked somewhere, not floating free. ### Custom MCP integration for governed answers Because Quick Suite acts as an MCP client, a team can build a [custom MCP server](https://atlan.com/know/ai-agent/how-to-build-mcp-servers-for-enterprise-data/) fronting Atlan's context and register it with Quick's agents, using the same [AgentCore Runtime or Gateway pattern](https://aws.amazon.com/blogs/machine-learning/integrate-external-tools-with-amazon-quick-agents-using-model-context-protocol-mcp/) AWS documents for other external tools. That lets a Quick Suite agent pull a certified metric definition from Atlan's glossary instead of guessing from whichever document ranked highest in Quick Index. 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 Quick Suite 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](https://atlan.com/know/ai-agent/how-to-give-ai-agents-access-to-enterprise-data/). [Context layer evaluation criteria](https://atlan.com/know/ai-agent/context-layer/context-layer-evaluation-criteria/) and the [build vs buy vs bundle](https://atlan.com/know/ai-agent/context-layer/context-layer-tco-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 Suite 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](https://atlan.com/know/ai-agent/aws/aws-datazone-vs-glue-data-catalog/), both scoped to the AWS boundary. Most AWS-native teams don't feel that gap until an agent needs to act on data, not just summarize it, closer to [a knowledge graph for AI agents](https://atlan.com/know/ai-agent/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](https://atlan.com/know/agent-context-layer/) architecture behind [multi-cloud context](https://atlan.com/know/ai-agent/context-layer/multi-cloud-context-layer/). It only reads metadata; it never copies or moves the underlying data, unlike Quick Suite's indexing model. 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 Watch Now → A retailer running Quick Suite 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](https://atlan.com/know/ai-agent/context-layer/context-layer-reference-architecture/) is built to close, and it's worth weighing against [building that layer by hand](https://atlan.com/know/ai-agent/context-layer/diy-context-layer/) before deciding to stitch Quick Suite, Glue, and DataZone together internally. --- ## Choosing between Amazon Quick Suite and Atlan for a data catalog The real decision isn't Quick Suite 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. Quick Suite 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. A second cloud, an AI agent that needs to act on data rather than summarize it, or a governance requirement Quick Suite's IAM inheritance doesn't cover, is worth closing before an agent, or an auditor, finds the gap first. Book a Demo --- ## FAQs about Amazon Quick Suite vs Atlan for data catalog ### 1. Is Amazon Quick Suite a data catalog? Not in the technical-metastore sense of the term. Quick Index, the search layer inside Amazon Quick Suite, unifies documents, apps, and structured data into a knowledge base for BI dashboards and chat. It doesn't track table-level schema, column lineage, or a governed business glossary the way AWS Glue Data Catalog, AWS DataZone, or a dedicated catalog like Atlan does. ### 2. Does Amazon Quick Suite track data lineage? AWS doesn't document column-level or table-level lineage tracking inside Quick Suite itself. Lineage in the AWS ecosystem lives in AWS Glue Data Catalog and AWS DataZone. 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 Suite directly through MCP? Quick Suite acts as an MCP client, connecting out to external MCP servers, including custom ones on Amazon Bedrock AgentCore, to pull in tools and data. As of August 2026, AWS hasn't documented Quick Suite exposing its own MCP server for outside agents, the direction Atlan's MCP server runs. ### 4. What is Amazon Quick Suite's Quick Index? Quick Index is the knowledge-consolidation layer inside Amazon Quick Suite. It connects over 50 built-in sources, including SharePoint, Snowflake, Databricks, and Amazon S3, plus more than 1,000 apps through OpenAPI and MCP connectors, and indexes that content so Quick's chat and dashboards can answer questions across it. ### 5. Do Amazon Quick Suite and Atlan work together? Yes. Quick Suite 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; Quick Suite supplies the BI and chat surface on top of it. ### 6. What does Atlan add that Quick Suite's Quick Index doesn'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 Quick Suite's own assistant, can query that governed context directly. ### 7. How much does Amazon Quick Suite cost? Per AWS's pricing page, Quick Suite runs $20 per user per month on the Plus and Professional plans and $40 per user per month on Enterprise, with 25 GB (Free/Plus) or 50 GB (Professional/Enterprise) of pooled index storage per user included. Overage index storage is $5 per GB per month, and Professional and Enterprise accounts pay a flat $250 per account per month infrastructure fee. --- ## Sources 1. 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/ 2. Meet Amazon Quick Suite: The agentic AI application reshaping how work gets done, About Amazon (2025). https://www.aboutamazon.com/news/aws/amazon-quick-suite-agentic-ai-aws-work 3. Overview of Amazon Q Business indexes in Amazon Quick Suite, AWS Documentation. https://docs.aws.amazon.com/quicksuite/latest/userguide/qbiz-indexes-overview.html 4. Amazon Quick Index product page, AWS. https://aws.amazon.com/quick/index/ 5. Amazon Quick Suite Pricing, AWS. https://aws.amazon.com/quicksuite/pricing/ 6. Amazon Quick announces autonomous agents, multi-dataset analytics, and redesigned activity feed, AWS What's New (2026). https://aws.amazon.com/about-aws/whats-new/2026/06/amazon-quick/ 7. 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/ 8. Amazon DataZone vs AWS Glue Data Catalog, AWS. https://d1.awsstatic.com/datazone-assets/Amazon-DataZone-AWS-Glue-Data-Catalog.pdf