lightdash · gwenwindflower · Aug 27, 2026 · Aug 27, 2026
diff --git a/.mintlify/components.md b/.mintlify/components.md
@@ -61,9 +61,12 @@ Deprecated is not a maturity level — it is where a feature goes after GA. Its
 | Condition | Badge |
 | --- | --- |
 | Enterprise plan | `<Badge icon="building-plus" color="blue" size="sm" shape="pill">Enterprise</Badge>` |
+| AI add-on, on any plan | `<Badge icon="sparkles" color="blue" size="sm" shape="pill">AI add-on</Badge>` |
 | Self-hosted deployments only | `<Badge icon="server" color="blue" size="sm" shape="pill">Self-hosted</Badge>` |
 | Lightdash Cloud only | `<Badge icon="cloud-bolt" color="blue" size="sm" shape="pill">Cloud</Badge>` |
 
+The four values and every gated feature that carries one are mapped on [feature availability](../support/feature-availability.mdx) — the availability counterpart to feature maturity levels. A page whose gate changes updates that map in the same change.
+
 **A badge gates, it never labels.** It answers whether the reader can use the thing, and a reader who cannot is meant to stop. A badge describing what the page is about — the tool it uses, the surface it covers — fails the test, because that badge could be justified on any page and so distinguishes none of them. If the page's area, title, or first sentence already implies it, it is a label.
 
 **The colour carries the axis, the icon carries the value.** Availability is blue throughout, so a reader learns that blue means "who can reach this" and reads the icon for which condition. Lifecycle spends its colours on urgency instead: orange for the earliest stage, purple for the settled-but-moving one, grey for the one on its way out.

diff --git a/.mintlify/ia-map.yml b/.mintlify/ia-map.yml
@@ -113,7 +113,7 @@ self-host/nats-workers:
   for: Offloaded query execution.
 
 support:
-  for: Getting help and diagnosing problems.
+  for: Getting help, diagnosing problems, and the cross-cutting explainers badges point at — feature maturity and availability.
 
 api-reference/v1/introduction:
   for: OpenAPI-generated endpoint reference and SDKs.
diff --git a/agents.mdx b/agents.mdx
@@ -3,9 +3,9 @@ title: "AI agents"
 description: "Ask questions in plain language and get governed answers, charts, and dashboards from your metrics"
 ---
 
-<Info>
-  AI agents are available as an add-on for all plans. [View pricing](https://www.lightdash.com/pricing)
-</Info>
+import AiAddOnAvailability from '/snippets/ai-add-on-availability.mdx';
+
+<AiAddOnAvailability />
 
 Lightdash AI agents let your team ask questions in natural language and get answers built from your semantic layer — the saved dashboards, metrics, dimensions, joins, and descriptions you've already defined. An agent picks the relevant models and metrics, builds and runs the query with the right filters and [parameters](/semantic-layer/parameters), and returns the result as the chart, table, or summary that fits the question. Because every answer runs through the semantic layer, it respects the same project permissions and user attributes as the rest of Lightdash.
 

diff --git a/agents/agent-context.mdx b/agents/agent-context.mdx
@@ -5,6 +5,10 @@ description: "Give every agent in a project the same terminology, table choices,
 doc-type: reference
 ---
 
+import AiAddOnAvailability from '/snippets/ai-add-on-availability.mdx';
+
+<AiAddOnAvailability />
+
 Agent context is shared background knowledge every AI agent in a project reads before answering. It lives in a single file, `lightdash.project_context.yml`, that sits next to `lightdash.config.yml` inside your dbt project directory. Notes you add here change future agent behavior without changing the semantic layer.
 
 The file is committed to your dbt repo and travels with the rest of your project metadata.

diff --git a/agents/agents-as-code.mdx b/agents/agents-as-code.mdx
@@ -5,6 +5,10 @@ description: "Download AI agents to YAML, review them in Git, and promote them b
 doc-type: reference
 ---
 
+import AiAddOnAvailability from '/snippets/ai-add-on-availability.mdx';
+
+<AiAddOnAvailability />
+
 You can serialize a project's AI agents to YAML with the Lightdash CLI, keep them under version control, and promote them between environments — preview, staging, production — using the standard `lightdash download` / `lightdash upload` flow.
 
 Use agents as code when you want to:

diff --git a/agents/ai-coding-agent.mdx b/agents/ai-coding-agent.mdx
@@ -1,14 +1,13 @@
 ---
-title: AI coding agent (Beta)
+title: AI coding agent
 description: Ask an agent in chat to edit any connected repository and open a pull request
 doc-type: tutorial
+tag: "Beta"
 ---
 
-<Warning>
-  The AI coding agent is currently in Beta and gated by a feature flag. It is an
-  **enterprise** feature. Contact Lightdash support to enable it for your
-  organization.
-</Warning>
+<Info>
+  <Badge icon="flask" color="purple" size="sm" shape="pill">Beta</Badge> <Badge icon="sparkles" color="blue" size="sm" shape="pill">AI add-on</Badge> Off by default even where agents are enabled — contact Lightdash support to turn it on for your organization. [What Beta means](/support/feature-maturity-levels). [What each availability level means](/support/feature-availability).
+</Info>
 
 The AI coding agent lets you ask an AI agent in chat to change files in **any repository your organization has connected to Lightdash** — not only the dbt repo behind a project — and have the agent open a pull request (or GitLab merge request) with the change.
 

diff --git a/agents/ai-writeback.mdx b/agents/ai-writeback.mdx
@@ -4,9 +4,13 @@ description: Ask an AI agent to edit your dbt project in chat and open a pull re
 doc-type: tutorial
 ---
 
+import AiAddOnAvailability from '/snippets/ai-add-on-availability.mdx';
+
+<AiAddOnAvailability />
+
 AI writeback lets you ask an AI agent in chat or Slack to change something in the dbt repository that backs your project — rename a metric, add a dimension, edit a model's SQL, fix a YAML description — and have the agent open a pull request (or GitLab merge request) with the change.
 
-This is the same writeback capability you would normally trigger from the Custom Metric or SQL Runner menus, surfaced inside the conversation with your AI agent.
+This is the same capability you would normally trigger from the Custom Metric or SQL Runner menus, surfaced inside the conversation with your AI agent. Triggering it from those menus is [dbt write-back](/integrations/dbt/write-back), which is available to every Lightdash user with Developer access — it is asking an *agent* to do it that needs the add-on.
 
 For changes that don't belong in the dbt project — for example, editing an app repo, a service, or a docs site — use the [AI coding agent](/agents/ai-coding-agent) instead. It runs on the same E2B + Anthropic pipeline but targets any repository your organization has connected, and delegates verification to that repository's own CI.
 

diff --git a/agents/autopilot.mdx b/agents/autopilot.mdx
@@ -7,7 +7,7 @@ tag: "Beta"
 ---
 
 <Info>
-  <Badge icon="flask" color="purple" size="sm" shape="pill">Beta</Badge> Autopilot is available on **Enterprise** and **Cloud Pro** plans only. [What Beta means](/support/feature-maturity-levels).
+  <Badge icon="flask" color="purple" size="sm" shape="pill">Beta</Badge> <Badge icon="sparkles" color="blue" size="sm" shape="pill">AI add-on</Badge> Cloud Pro and above, and self-hosted instances with an Enterprise license. [What Beta means](/support/feature-maturity-levels). [What each availability level means](/support/feature-availability).
 </Info>
 
 Autopilot is a scheduled agent that runs against a project on its own and keeps it healthy. Each run, it reviews recent activity, finds problems, fixes what it safely can, and flags the rest for review.

diff --git a/agents/create-evaluation-suites.mdx b/agents/create-evaluation-suites.mdx
@@ -4,6 +4,10 @@ description: "Test and validate your AI agent's performance with custom evaluati
 doc-type: tutorial
 ---
 
+import AiAddOnAvailability from '/snippets/ai-add-on-availability.mdx';
+
+<AiAddOnAvailability />
+
 Create custom evaluation suites to batch test your agent's performance and ensure consistent, high-quality responses across different scenarios.
 
 <Frame>

diff --git a/agents/data-access.mdx b/agents/data-access.mdx
@@ -4,6 +4,10 @@ description: How agents reach your data, and how to scope them with tags and use
 doc-type: reference
 ---
 
+import AiAddOnAvailability from '/snippets/ai-add-on-availability.mdx';
+
+<AiAddOnAvailability />
+
 AI agents offer flexible data access control to balance insights with privacy and security. By default, agents have data access enabled, and you can disable it per agent when you want metadata-only behavior.
 
 ## Data access modes

diff --git a/agents/deep-research.mdx b/agents/deep-research.mdx
@@ -7,7 +7,7 @@ tag: "Beta"
 ---
 
 <Info>
-  <Badge icon="flask" color="purple" size="sm" shape="pill">Beta</Badge> Deep research is available wherever [AI agents](/agents/set-up-agents) are enabled, including eligible AI trials. Lightdash Cloud enables the feature flag by default. Self-hosted deployments need the same Enterprise Edition license and AI provider configuration required by AI agents, and must enable the `ai-deep-research` feature flag. [What Beta means](/support/feature-maturity-levels).
+  <Badge icon="flask" color="purple" size="sm" shape="pill">Beta</Badge> <Badge icon="sparkles" color="blue" size="sm" shape="pill">AI add-on</Badge> Available wherever [AI agents](/agents/set-up-agents) are enabled, including eligible AI trials. Lightdash Cloud turns the feature flag on by default; self-hosted deployments enable the `ai-deep-research` flag alongside the license and model provider that agents already require. [What Beta means](/support/feature-maturity-levels).
 </Info>
 
 Deep research is a long-running mode for Lightdash AI agents. It is designed for questions that need several queries, competing explanations, and a reusable report rather than one immediate answer.

diff --git a/agents/enable-ai-router.mdx b/agents/enable-ai-router.mdx
@@ -5,9 +5,9 @@ description: "Automatically route questions to the best-fit agent, so users don'
 doc-type: tutorial
 ---
 
-<Info>
-  The AI Router is part of the AI agents add-on. [View pricing](https://www.lightdash.com/pricing)
-</Info>
+import AiAddOnAvailability from '/snippets/ai-add-on-availability.mdx';
+
+<AiAddOnAvailability />
 
 When you have more than one AI agent in a project, users have to know which agent to ask. The **AI Router** removes that decision: type a question, and Lightdash picks the agent best suited to answer it.
 

diff --git a/agents/issues.mdx b/agents/issues.mdx
@@ -7,7 +7,7 @@ tag: "Beta"
 ---
 
 <Info>
-  <Badge icon="flask" color="purple" size="sm" shape="pill">Beta</Badge> Issues is available on **Enterprise** and **Cloud Pro** plans. It is **off by default** and must be enabled by an organization admin. [What Beta means](/support/feature-maturity-levels).
+  <Badge icon="flask" color="purple" size="sm" shape="pill">Beta</Badge> <Badge icon="sparkles" color="blue" size="sm" shape="pill">AI add-on</Badge> Cloud Pro and above, and off by default — an organization admin has to turn it on. [What Beta means](/support/feature-maturity-levels). [What each availability level means](/support/feature-availability).
 </Info>
 
 The Issues board is where data problems get tracked. Two things land on the same board:

diff --git a/agents/lightdash-mcp.mdx b/agents/lightdash-mcp.mdx
@@ -4,9 +4,9 @@ description: "Connect Claude, ChatGPT, and Codex to your Lightdash data over the
 doc-type: reference
 ---
 
-<Check>
-  Available to all Lightdash Cloud users.
-</Check>
+<Info>
+  <Badge icon="cloud-bolt" color="blue" size="sm" shape="pill">Cloud</Badge> Available on every Lightdash Cloud plan. Self-hosted instances need an [Enterprise license key](/self-host/enterprise-features#mcp) and `MCP_ENABLED=true`. [What each availability level means](/support/feature-availability).
+</Info>
 
 The [Model Context Protocol (MCP)](https://modelcontextprotocol.io/) lets external AI assistants — Claude, ChatGPT, OpenAI Codex, and custom agents — connect to Lightdash and query your data directly. Lightdash runs the MCP server and your assistant is the client, so it can explore your data models, search for metrics and dimensions, run queries, and surface data-driven insights through natural conversation. MCP uses secure OAuth authentication and respects all your existing access controls, so data stays protected.
 

diff --git a/agents/set-up-agents.mdx b/agents/set-up-agents.mdx
@@ -5,6 +5,10 @@ description: Enable AI features, create your first agent, scope its data, and co
 doc-type: tutorial
 ---
 
+import AiAddOnAvailability from '/snippets/ai-add-on-availability.mdx';
+
+<AiAddOnAvailability />
+
 <Frame>
   <iframe
     width="100%"

diff --git a/agents/thread-retention.mdx b/agents/thread-retention.mdx
@@ -5,11 +5,7 @@ doc-type: reference
 ---
 
 <Info>
-  Thread retention is an Enterprise feature, enabled per organization on
-  request — [contact us](/support) to turn it on. Self-hosted Enterprise
-  deployments set `LIGHTDASH_ENABLE_FEATURE_FLAGS=ai-thread-retention` — see
-  [feature flag environment
-  variables](/self-host/customize-deployment/environment-variables#feature-flags).
+  <Badge icon="sparkles" color="blue" size="sm" shape="pill">AI add-on</Badge> <Badge icon="building-plus" color="blue" size="sm" shape="pill">Enterprise</Badge> Enabled per organization on request — [contact us](/support) to turn it on. Self-hosted deployments set `LIGHTDASH_ENABLE_FEATURE_FLAGS=ai-thread-retention`, see [feature flag environment variables](/self-host/customize-deployment/environment-variables#feature-flags). [What each availability level means](/support/feature-availability).
 </Info>
 
 By default, Lightdash keeps AI agent threads forever. Organizations with

diff --git a/agents/use-ai-agents.mdx b/agents/use-ai-agents.mdx
@@ -3,6 +3,10 @@ title: Using AI agents
 description: Ask questions, use SQL mode, mention charts, and schedule deliveries from an agent conversation
 ---
 
+import AiAddOnAvailability from '/snippets/ai-add-on-availability.mdx';
+
+<AiAddOnAvailability />
+
 ## Core capabilities
 
 AI agents in Lightdash allow you to:

diff --git a/agents/verified-answers.mdx b/agents/verified-answers.mdx
@@ -4,6 +4,10 @@ description: Train your AI agent with high-quality examples for better, more con
 doc-type: tutorial
 ---
 
+import AiAddOnAvailability from '/snippets/ai-add-on-availability.mdx';
+
+<AiAddOnAvailability />
+
 Mark AI-generated charts and dashboards as verified answers to help your agent learn from the best examples and provide better responses to similar questions.
 
 ## How verified answers work

diff --git a/agents/visibility.mdx b/agents/visibility.mdx
@@ -4,6 +4,10 @@ sidebarTitle: "Visibility"
 description: "How admins see, control, and give feedback on what AI agents do with your data"
 ---
 
+import AiAddOnAvailability from '/snippets/ai-add-on-availability.mdx';
+
+<AiAddOnAvailability />
+
 ## Built for trust
 
 AI agents only earn a place in your workflow if admins can see what they're doing and step in when it matters. Lightdash agents are designed around three principles: every interaction is visible to admins, risky actions need a human decision before they happen, and wrong answers get found and fixed rather than just logged.

diff --git a/data-apps.mdx b/data-apps.mdx
@@ -5,7 +5,7 @@ tag: "Beta"
 ---
 
 <Info>
-  <Badge icon="flask" color="purple" size="sm" shape="pill">Beta</Badge> Data apps are free to use for now; pricing after general availability is to be confirmed. [What Beta means](/support/feature-maturity-levels).
+  <Badge icon="flask" color="purple" size="sm" shape="pill">Beta</Badge> <Badge icon="sparkles" color="blue" size="sm" shape="pill">AI add-on</Badge> Free to use for now; pricing after general availability is to be confirmed. [What Beta means](/support/feature-maturity-levels). [What each availability level means](/support/feature-availability).
 </Info>
 
 Data apps let you describe what you want in plain English and get back a working, interactive application built on top of your semantic layer. Behind the scenes, a coding agent generates a React app inside an isolated sandbox and Lightdash serves it back to you in a sandboxed iframe. Lightdash Cloud uses Claude; self-hosted operators can also configure OpenAI Codex.

diff --git a/docs.json b/docs.json
@@ -452,6 +452,7 @@
             "pages": [
               "support/generate-har-file",
               "support/ai-thread-debug-dump",
+              "support/feature-availability",
               "support/feature-maturity-levels",
               "support/support-access"
             ],

diff --git a/explore/chart-types/custom-project-charts.mdx b/explore/chart-types/custom-project-charts.mdx
@@ -6,6 +6,10 @@ doc-type: reference
 icon: "shape"
 ---
 
+<Info>
+  <Badge icon="sparkles" color="blue" size="sm" shape="pill">AI add-on</Badge> <Badge icon="building-plus" color="blue" size="sm" shape="pill">Enterprise</Badge> Requires the [data apps runtime](/self-host/enterprise-features/data-apps) to be enabled. [What each availability level means](/support/feature-availability).
+</Info>
+
 Project chart types are reusable chart types you build for your project. You describe the chart you want, Lightdash builds it, and anyone in the project can then use it like a built-in chart type. Pick **Custom** in the chart type menu, then choose the chart type in the **Custom chart type** picker.
 
 For a one-off chart written directly as a Vega-Lite spec, see [Custom Vega charts](/explore/chart-types/custom-vega-charts).
@@ -14,10 +18,6 @@ A project chart type is a chart type you build once and reuse. Describe the char
 
 The same chart type can power many saved charts, each with its own query and field mapping.
 
-<Info>
-  Project chart types are an enterprise feature and require the [data apps runtime](/self-host/enterprise-features/data-apps) to be enabled.
-</Info>
-
 ## Browsing chart types
 
 Every chart type in the project lives in the gallery. Go to **Browse** and pick **Chart types**.

diff --git a/explore/create-scheduled-deliveries.mdx b/explore/create-scheduled-deliveries.mdx
@@ -74,9 +74,9 @@ Under `Content > Message`, write the static message that goes out with every del
 
 ## AI agent
 
-<Info>
-  AI-enhanced messages require the [AI agents](/agents) add-on, available for Cloud Pro and Enterprise plans. [View pricing](https://www.lightdash.com/pricing)
-</Info>
+<Note>
+  <Badge icon="sparkles" color="blue" size="sm" shape="pill">AI add-on</Badge> AI-enhanced messages need the [AI agents](/agents) add-on. [What each availability level means](/support/feature-availability).
+</Note>
 
 Instead of a static message, Lightdash can write the delivery message from your data on **every send** — summarizing the results and calling out notable changes or trends, so recipients get the headline before they even open the attachment.
 

diff --git a/explore/homepage.mdx b/explore/homepage.mdx
@@ -6,7 +6,7 @@ description: "Build and publish project homepages with curated blocks and audien
 Homepage is the curation layer for a Lightdash project. It turns the most useful data, workflows, and team context into a deliberate starting point, combining AI, key metrics, curated content, personal shortcuts, data announcements, and resources outside Lightdash.
 
 <Info>
-  Homepage is available on Lightdash Enterprise. An organization admin must enable it for the organization before project homepages can be customized.
+  <Badge icon="building-plus" color="blue" size="sm" shape="pill">Enterprise</Badge> An organization admin has to turn Homepage on before project homepages can be customized. [What each availability level means](/support/feature-availability).
 </Info>
 
 <Frame>

diff --git a/semantic-layer/caching.mdx b/semantic-layer/caching.mdx
@@ -4,9 +4,7 @@ description: "Cut the number of queries Lightdash runs against your warehouse wi
 ---
 
 <Info>
-  **Availability:** Caching features are only available to:
-  - **Lightdash Cloud customers** (all plans)
-  - **On-premise customers with a valid License key**
+  <Badge icon="cloud-bolt" color="blue" size="sm" shape="pill">Cloud</Badge> Filter value caching is available on every Lightdash Cloud plan; chart and dashboard results caching needs Cloud Pro or above. Self-hosted instances need an [Enterprise license key](/self-host/enterprise-features). [What each availability level means](/support/feature-availability).
 </Info>
 
 Lightdash supports two types of caching:

diff --git a/snippets/ai-add-on-availability.mdx b/snippets/ai-add-on-availability.mdx
@@ -0,0 +1,3 @@
+<Info>
+  <Badge icon="sparkles" color="blue" size="sm" shape="pill">AI add-on</Badge> Available on every Lightdash Cloud plan. Self-hosted instances need an [Enterprise license key](/self-host/enterprise-features#ai-agents) and a configured model provider. [What each availability level means](/support/feature-availability).
+</Info>
diff --git a/snippets/embedding-availability.mdx b/snippets/embedding-availability.mdx
@@ -1,3 +1,3 @@
 <Info>
-Embedding is available to all Lightdash Cloud users and Enterprise On-Prem customers. [Get in touch](https://lightdash.typeform.com/to/BujU5wg5) to have this feature enabled in your account.
+  <Badge icon="cloud-bolt" color="blue" size="sm" shape="pill">Cloud</Badge> Available on every Lightdash Cloud plan and to Enterprise on-prem customers. [Get in touch](https://lightdash.typeform.com/to/BujU5wg5) to have it turned on for your account.
 </Info>
-Original file line number
+Diff line change
@@ Expand Up / @@ -7,7 +7,7 @@ tag: "Beta" @@
     ---
     <Info>
-      <Badge icon="flask" color="purple" size="sm" shape="pill">Beta</Badge> Autopilot is available on **Enterprise** and **Cloud Pro** plans only. [What Beta means](/support/feature-maturity-levels).
+      <Badge icon="flask" color="purple" size="sm" shape="pill">Beta</Badge> <Badge icon="sparkles" color="blue" size="sm" shape="pill">AI add-on</Badge> Cloud Pro and above, and self-hosted instances with an Enterprise license. [What Beta means](/support/feature-maturity-levels). [What each availability level means](/support/feature-availability).
     </Info>
     Autopilot is a scheduled agent that runs against a project on its own and keeps it healthy. Each run, it reviews recent activity, finds problems, fixes what it safely can, and flags the rest for review.
@@ Expand Down @@