# CloudEval AI Docs

> Documentation for evaluating cloud environments and infrastructure-as-code with CloudEval AI.

## Docs

- [Capabilities map](https://docs.cloudeval.ai/capabilities-map.md): See which CloudEval AI capabilities are current, in progress, planned, or limited.
- [Cloud and IaC support](https://docs.cloudeval.ai/cloud-support.md): Check which cloud and infrastructure formats CloudEval supports today.
- [Connections](https://docs.cloudeval.ai/concepts/connections.md): Understand how CloudEval AI ingests live cloud data or infrastructure code.
- [Diagrams and workspace views](https://docs.cloudeval.ai/concepts/diagrams.md): Understand architecture diagrams, dependency diagrams, code context, and shareable diagram views in CloudEval.
- [Projects](https://docs.cloudeval.ai/concepts/projects.md): Learn why the project is the main unit of analysis, reporting, and sharing in CloudEval AI.
- [Reports](https://docs.cloudeval.ai/concepts/reports.md): Understand the two main report families in CloudEval AI and what decisions each one supports.
- [Sharing and collaboration](https://docs.cloudeval.ai/concepts/sharing-and-collaboration.md): Learn how CloudEval AI handles private work, invited collaborators, and share links.
- [FAQ](https://docs.cloudeval.ai/faq.md): Quick answers to the questions most teams ask when evaluating CloudEval AI.
- [Availability and limits](https://docs.cloudeval.ai/feature-availability.md): Use this matrix to separate what is available today from what is limited or coming soon.
- [CloudEval AI](https://docs.cloudeval.ai/index.md): Turn cloud environments and infrastructure-as-code into clear cost, architecture, and reliability decisions before rollout.
- [How CloudEval works](https://docs.cloudeval.ai/overview/how-cloudeval-works.md): Learn the core CloudEval AI model so the rest of the product and docs make sense quickly.
- [Plans and billing](https://docs.cloudeval.ai/plans-and-pricing.md): Compare current CloudEval AI plans, credits, and how they are available today.
- [Connect an Azure environment](https://docs.cloudeval.ai/quickstart/connect-an-azure-environment.md): Create a live Azure connection in CloudEval AI and turn it into a project you can evaluate.
- [Create a project from a GitHub URL](https://docs.cloudeval.ai/quickstart/create-a-project-from-a-github-url.md): Use the CloudEval UI to create a project directly from a GitHub-hosted ARM template URL and land in the new workspace.
- [Import an ARM or Bicep template](https://docs.cloudeval.ai/quickstart/import-an-arm-or-bicep-template.md): Create a CloudEval project from local ARM JSON or a GitHub-hosted template URL.
- [Quickstart](https://docs.cloudeval.ai/quickstart/index.md): Get from account setup to a first useful CloudEval AI report with the shortest validated path.
- [Run your first reports](https://docs.cloudeval.ai/quickstart/run-your-first-reports.md): Generate cost and architecture outputs from an existing CloudEval project.
- [Use the CLI](https://docs.cloudeval.ai/quickstart/use-the-cli.md): Install CloudEval CLI, authenticate, create a project, run reports, and ask a grounded question from the terminal.
- [Agent and automation rules](https://docs.cloudeval.ai/reference/agent-and-automation-rules.md): Build reliable CloudEval integrations with the CLI and machine-readable context files.
- [CLI command reference](https://docs.cloudeval.ai/reference/cli-command-reference.md): Reference the current CloudEval CLI command surface, grouped by task.
- [CLI overview](https://docs.cloudeval.ai/reference/cli-overview.md): Learn when to use the CloudEval CLI, how it behaves, and which surfaces are best for different tasks.
- [Headless diagram image downloads](https://docs.cloudeval.ai/reference/headless-diagram-image-downloads.md): Download architecture and dependency diagram images from CLI, CI, or MCP agents without scraping the browser UI.
- [llms.txt and llms-full.txt](https://docs.cloudeval.ai/reference/llms-and-agent-context.md): Understand CloudEval's public machine-readable context files and when to use each one.
- [MCP client setup](https://docs.cloudeval.ai/reference/mcp-client-setup.md): Connect CloudEval's MCP server to Codex, Cursor, Claude Code, VS Code, or another MCP-compatible client.
- [Platform model](https://docs.cloudeval.ai/reference/platform-model.md): Reference the main CloudEval AI objects and how they relate to each other.
- [Share links](https://docs.cloudeval.ai/reference/public-sharing.md): Understand what CloudEval share links show and what they do not.
- [Report types and statuses](https://docs.cloudeval.ai/reference/report-types-and-statuses.md): Reference the report families, summary fields, and status values that appear across CloudEval AI.
- [Terminal UI](https://docs.cloudeval.ai/reference/terminal-ui.md): Use CloudEval's terminal UI for interactive review and explicit CLI commands for automation.
- [Release notes](https://docs.cloudeval.ai/release-notes.md): Public release notes for the CloudEval AI documentation experience.
- [Support](https://docs.cloudeval.ai/support.md): Contact CloudEval support with the details needed to diagnose setup, sync, reporting, or billing issues quickly.
- [Connections and sync](https://docs.cloudeval.ai/troubleshooting/connections-and-sync.md): Diagnose failed Azure connection tests, empty subscription lists, and incomplete project syncs.
- [Reports and billing](https://docs.cloudeval.ai/troubleshooting/reports-and-billing.md): Resolve failed report runs, missing cost output, and credit-related blockers in CloudEval AI.
- [Sign-in and onboarding](https://docs.cloudeval.ai/troubleshooting/sign-in-and-onboarding.md): Fix the most common CloudEval AI access and onboarding issues before they block setup.
- [Automate evaluations with the CLI](https://docs.cloudeval.ai/workflows/automate-evaluations-with-the-cli.md): Use CloudEval CLI for repeatable import, evaluation, download, and deeplink workflows.
- [Evaluate a live Azure environment](https://docs.cloudeval.ai/workflows/evaluate-a-live-azure-environment.md): Run a full CloudEval AI review loop against a deployed Azure environment.
- [Review cost and architecture findings](https://docs.cloudeval.ai/workflows/review-cost-and-architecture-findings.md): Turn CloudEval AI report output into a practical review conversation and a prioritized next step list.
- [Review IaC before deployment](https://docs.cloudeval.ai/workflows/review-iac-before-deployment.md): Use CloudEval AI as a pre-deployment review layer for ARM and Bicep-based Azure infrastructure.
- [Share results with stakeholders](https://docs.cloudeval.ai/workflows/share-results-with-stakeholders.md): Publish CloudEval AI findings in a way that is readable, controlled, and appropriate for the audience.

## Optional

- [CloudEval app](https://cloudeval.ai)
- [GitHub](https://github.com/ganakailabs/cloudeval)