Amazon Quick Suite, AWS’s agentic AI workspace, launched October 9, 2025 as an evolution of Amazon QuickSight, and AWS shortened the name to Amazon Quick within months. Atlan, Alation, Collibra, Informatica, and DataHub each take a different approach to the context layer that grounds agents like the ones inside Quick, and AWS just built its own version, scoped to its own cloud. This page covers what Amazon Quick is, its six components, who it’s for, what it costs, and where a cross-cloud context layer fits once an agent needs to see past AWS.
Every agent inside Amazon Quick answers from context Quick itself can reach: a knowledge base, a dataset in a Quick Sight space, or a document a user manually attached. That scope is deliberate, not a limitation AWS is hiding. Atlan takes a wider scope by design: governed enterprise knowledge served to any agent, in AWS or anywhere else, through an MCP server, an Enterprise Data Graph, and Context Agents that keep definitions current without manual upkeep.
- Chat-first interface backed by default and custom agents
- Six surfaces: Quick Sight, Quick Flows, Quick Automate, Quick Research, Apps in Quick, desktop app
- 61 listed integrations, six of which can build a knowledge base
- Five plans, free through enterprise, with a $250 per account monthly fee on two of them
- Live in four AWS regions at launch, expanding since
Quick facts
| Attribute | Detail |
|---|---|
| What it is | AWS’s agentic AI workspace for research, business intelligence, and automation |
| Current name | Amazon Quick; AWS’s user guide and pricing page dropped “Suite” with no dated announcement |
| Launched | October 9, 2025, evolving Amazon QuickSight |
| Core components | Quick Sight, Quick Flows, Quick Automate, Quick Research, Apps in Quick, desktop application |
| Connects via | 61 listed integrations, six of which build a knowledge base, plus MCP and OpenAPI action connectors |
| Pricing | Free ($0), Plus ($20/user/mo), Max ($100/user/mo), Professional ($20/user/mo + $250/account/mo), Enterprise ($40/user/mo + $250/account/mo) |
| Availability at launch | US East (N. Virginia), US West (Oregon), Asia Pacific (Sydney), Europe (Ireland) |
| How Atlan complements it | Extends governed context beyond AWS’s boundary to the rest of a multi-cloud stack via MCP |
What is Amazon Quick, and why did AWS rename QuickSight?
Amazon Quick is AWS’s brand for an agentic AI workspace combining chat-driven research, business intelligence, and task automation in one product. AWS introduced it on October 9, 2025 as Amazon Quick Suite, describing it in its own Business Intelligence Blog as the evolution of Amazon QuickSight into a considerably wider product, framed in the AWS News Blog launch post as “your agentic AI-powered workspace.”
The name changed again within months, without an announcement. AWS’s live user guide sits at docs.aws.amazon.com/quick/ and says Amazon Quick throughout; the pricing page URL still reads /quicksuite/ while its text says Amazon Quick. QuickSight’s original BI capability still exists inside the product as a component AWS’s feature table calls “Quick Sight,” with a space. For the full rename history back to 2020’s QuickSight Q, see what happened to QuickSight and Glue; this page covers what the current product does, not its naming history.
Atlan’s own AWS Glue and QuickSight connectors kept working through both renames, because they read from AWS’s underlying services, not the marketing name on top. A context layer built to survive one rename survives the next one too.
How does Amazon Quick work?
Chat is the entry point to every Amazon Quick interaction. A user types a question or an instruction, and Quick decides how to respond: answering from connected data, generating content, running a workflow, researching a topic, or taking action in another application, per AWS’s own documentation on how the product fits together.
Behind that chat sits an agent. AWS ships a default agent and lets organizations build custom ones, configured with instructions, knowledge sources, and tools each can call. Agents draw on “spaces,” which bundle the documents, dashboards, datasets, knowledge bases, and action connectors a team shares, so everyone works from the same context rather than rebuilding it per conversation.
Four integration types connect an agent outward. Knowledge bases pull in content from S3, SharePoint, OneDrive, Confluence, Google Drive, and web crawlers. Action connectors, built from OpenAPI specs or MCP servers rather than plain function calls, let an agent read data and act in an external service. Extensions embed Quick inside Chrome, Slack, Teams, and Microsoft 365. Structured data connections link Quick Sight to databases and lakes, a different job than lineage carried over MCP. An agent is only as accurate as the context reachable through these channels, the same constraint that shapes any agent context layer: the model rarely fails, the context feeding it does.
What are the six components of Amazon Quick?
Amazon Quick is not one feature. It is six capabilities that share the same chat interface, agent layer, and spaces underneath them, so a user experiences one product rather than six separate tools stitched together.
| Component | What it does | Built from |
|---|---|---|
| Quick Sight | Interactive dashboards, analytics, and embedded BI | Structured data connections, datasets, SPICE |
| Quick Flows | Automates repetitive tasks using connected data and apps | Action connectors, spaces, agent logic |
| Quick Automate | Builds end-to-end business process automations with human-in-the-loop checkpoints | Action connectors, agents, contextual decisions |
| Desktop application | Local access to files, email, and calendar from the same chat interface | Spaces, knowledge bases, extensions |
| Quick Research | Conducts research across the web and internal data, delivered as a cited report | Spaces, knowledge bases, the public web |
| Apps in Quick | Builds interactive web applications from a natural-language description | Structured data, action links, Quick Sight visuals |
Quick Sight is the direct descendant of the original QuickSight product; everything else is new territory for what used to be a BI-only tool. Grounding runs underneath all six through knowledge bases. 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 other 55 are Actions integrations, which call a tool rather than index it. Snowflake appears once, as a Cortex Agent under Actions, and Databricks does not appear on that table.
Not every component ships to every account type. AWS’s pricing documentation draws a line: chat, spaces, Quick Flows, and Apps in Quick reach personal accounts at aws.com/quick, but Quick Sight dashboards and Quick Automate need the AWS Management Console, the same enterprise-readiness question behind any agent rollout.
Who is Amazon Quick built for?
Amazon Quick targets two overlapping audiences: individuals who want an AI assistant without touching an AWS console, and organizations rolling out governed AI, spanning the different types of AI agents, across every business function. AWS solutions architects Esra Kayabali and Donnie Prakoso described the launch as bringing “AI-powered research, business intelligence, and automation capabilities into a single workspace,” aimed at business users rather than only data teams.
That framing shows up in the use cases AWS highlights: RFP responses, meeting prep, invoice processing, and account reconciliation, the kind of repetitive cross-application work that sits in marketing, finance, and operations. Larry Dignan, Editor in Chief of Constellation Insights at Constellation Research, an independent analyst, described it as functioning like “a desktop companion that leverages multiple agents and a context graph based on various enterprise systems,” closer to how enterprises are actually putting AI agents to work than a traditional BI tool.
Data and platform teams still matter, in a different role. They provision Quick through the AWS Management Console with IAM Identity Center, wire up knowledge base sources, and decide which spaces the organization draws from. Someone has to govern what an agent can see before a business user opens the chat window, closer to a types of metadata question, and a who-owns-it question, than a BI one.
How much does Amazon Quick cost?
Five plans, from a free personal tier to enterprise governance. Per AWS’s official pricing page, Free costs $0 per user monthly with 1 GB of index storage and includes chat, custom agents, research, the desktop app, spaces, and browser extensions for a single user. Plus adds Microsoft 365 extensions and deliverable creation at $20 per user monthly billed annually, $25 billed monthly, with 10 GB. Max runs $100 per user monthly annually, $125 monthly, with 50 GB.
Professional and Enterprise move to organization-wide, console-provisioned accounts. Professional runs $20 per user monthly plus a flat $250 per account monthly infrastructure fee, with four monthly agent hours, 25 GB of pooled storage per user, dashboards, and single sign-on. Enterprise runs $40 per user monthly plus the same fee, doubles the agent hours to eight, raises pooled storage to 50 GB per user, and adds data sovereignty controls and 24/7 support. Both tiers charge $3 per extra agent hour and $5 per GB monthly over the pooled allocation, a metered model closer to what it actually costs to run AI agents at scale than a flat license. AWS offers a 30-day trial waiving fees for up to 25 users.
Free and Plus are recent additions, not carried over from the October 2025 launch. AWS’s April 2026 announcement introduced them so individuals could sign up with a personal email, no AWS account required, while Professional and Enterprise stayed the path for organization-wide rollout with governance. A cost estimate built on the launch-era framing is out of date; the five plans above are current.
Where does Amazon Quick fit, and what are its current limitations?
Amazon Quick sits alongside, not inside, the rest of AWS’s AI stack. Amazon Bedrock is AWS’s foundation-model layer for custom applications; Quick is the packaged, business-facing product on top. AWS Glue Data Catalog, the metadata store underneath much of an AWS estate, is a separate service Quick reads from but does not replace, covered in Atlan’s breakdown of Glue Data Catalog’s limitations. Quick also absorbs ground Amazon Q Business used to cover: Q Business closed to new customers on 30 July 2026, the same consolidation pattern at Databricks with Genie Ontology and Snowflake with Horizon.
Three limitations stand out. Regional availability is still narrow: four regions at launch, expanding since. The product is young: AWS renamed it twice in under a year, the second time with no announcement anyone can cite, and split pricing into two account models within months of GA. And its grounding stays scoped to what it can index inside its own workspace: AWS documents its compliance controls clearly, but a boundary is still a boundary.
Definitions living in a non-AWS warehouse or another vendor’s BI tool still need to reach Quick’s agents, and a wide source list is not the same as a context graph that treats every system as equally native, an evaluation question worth asking of any agentic workspace. A multi-cloud context layer becomes relevant once an agent needs a definition living in Snowflake, Salesforce, or a tool with no AWS footprint, which describes most enterprises within a year or two of adopting any single-cloud agent product.
How Atlan extends context beyond Amazon Quick’s workspace
The context layer for AI is built for the opposite scope: governed enterprise knowledge served to any model, any agent, and any team, in AWS or well past it. It does not replace Amazon Quick’s knowledge bases, Quick Sight, or Glue Data Catalog; it sits above all three, pulling governed metadata into one living graph with column-level lineage, so an answer about a metric or a customer record stays consistent whether it originates in AWS or somewhere else.
The three pieces that do the work
Enterprise Data Graph: 80-plus sources, including AWS Glue Data Catalog, Amazon QuickSight, Snowflake, and Databricks, feed one living enterprise data graph, joining what Amazon Quick’s own workspace reaches with everything else in the same governed model.
Context Agents: teammates that generate descriptions, link business terms, infer metrics, and propose ontologies automatically. Per AI Labs research, they have produced 690,000-plus descriptions across 50-plus enterprise customers, with 87% rated on par with or better than human-written equivalents, the certified definitions an agent needs before it can structure context and answer accurately.
MCP server: exposes that governed context through the same open protocol Amazon Quick uses for its own action connectors, so an agent built on Quick, Bedrock, or another framework can call the same context repository without a custom integration for each.
For where Quick’s built-in semantic layer modeling stops, and for a direct look at data catalog and governance ground, see the two companion pages linked at the top of this article.
AWS built a strong workspace. Most enterprises don’t run on one cloud
Amazon Quick is a serious product. Folding research, business intelligence, and automation into one chat interface, backed by agents that draw on shared spaces and 61 listed integrations, is a real step past the dashboard-and-query pattern QuickSight shipped for a decade. The pricing structure, five plans from a free individual tier to Enterprise, suggests AWS expects this to reach well past its existing QuickSight customer base.
None of that changes where the context boundary sits. Quick’s agents are precise about what they can see: AWS’s own services, plus whatever an organization connects through a knowledge base, MCP, or OpenAPI. Most enterprises run Snowflake alongside Redshift, Salesforce alongside Amazon Connect, and a handful of tools with no AWS footprint at all, the same data catalog versus context layer scope question that shows up no matter which vendor’s console it’s asked inside. For those organizations, the practical question is not whether to adopt Amazon Quick. It is whether the context feeding it, and every other agent in the stack, gets governed once, in one place, or rebuilt separately inside every vendor’s own workspace.
FAQs about Amazon Quick Suite
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What is Amazon Quick Suite?
AWS’s agentic AI workspace, announced October 9, 2025 as an evolution of Amazon QuickSight, bringing research, business intelligence, and automation into one chat-driven product. -
Is Amazon Quick Suite the same as Amazon Quick?
Yes. AWS’s live user guide and pricing page say Amazon Quick, and the documentation moved to docs.aws.amazon.com/quick/. AWS published no dated announcement for it. Only the name changed. -
What are the six components of Amazon Quick?
AWS’s feature table lists Quick Sight (analytics), Quick Flows (task automation), Quick Automate (process automation), Quick Research (cited reports), Apps in Quick (natural-language app building), and a desktop application. Knowledge bases sit underneath as the indexed content store. -
How much does Amazon Quick cost?
Five plans: Free at $0 with 1 GB, Plus at $20 per user monthly billed annually with 10 GB, Max at $100 per user monthly with 50 GB, Professional at $20 per user monthly with 25 GB pooled, and Enterprise at $40 per user monthly with 50 GB pooled. Professional and Enterprise add a $250 per account monthly fee. A 30-day trial waives fees for up to 25 users. -
What is an Amazon Quick knowledge base, and how many integrations are there?
A knowledge base is the indexed content store behind Quick’s agents. AWS’s supported-integrations table lists 61 integrations, and six can create a knowledge base: Amazon S3, Confluence Cloud, Google Drive, OneDrive, SharePoint Online, and the Web Crawler. The rest are Actions integrations reached through OpenAPI specs or MCP servers. -
Does Amazon Quick replace Amazon Q Business?
AWS is positioning it that way. Amazon Q Business closed to new customers on 30 July 2026, per AWS’s Service Availability Updates post, and AWS calls Amazon Quick the next evolution of Q Business. Existing customers keep bug fixes and security updates, with no shutdown date published. -
How does Atlan work with Amazon Quick?
Amazon Quick grounds its agents in what its own workspace reaches: knowledge bases, action integrations, and whatever a user attaches to a space. Atlan supplies governed context across the rest of the stack and serves it through an MCP server, so agents can answer questions whose definitions live outside AWS.
Sources
- Introducing Amazon Quick Suite: Your Agentic AI-Powered Workspace, AWS News Blog
- Reimagine Business Intelligence: Amazon QuickSight Evolves to Amazon Quick Suite, AWS Business Intelligence Blog
- Reimagine the Way You Work With AI Agents in Amazon Quick Suite, AWS News Blog
- What Is Amazon Quick?, AWS Documentation
- How Amazon Quick Works, AWS Documentation
- Amazon Quick Plans and Pricing, AWS Documentation
- Amazon Quick Pricing, AWS
- Start Using Amazon Quick for Free in Minutes With Free and Plus Pricing Plans, AWS What’s New
- Supported Integrations in Amazon Quick, AWS Documentation
- Amazon Q Business Availability Change, AWS Documentation
- AWS Launches Amazon Quick, Connect Family of Business Apps, OpenAI Managed Agents, Constellation Research
- AWS Service Availability Updates, AWS What’s New (June 30, 2026)
- Bring Your Own Index in Amazon Quick, AWS Documentation
