CloudEval AI is easiest to understand as an evaluation flow: add a source, create a project, run reports, then share the result with the right audience.Documentation Index
Fetch the complete documentation index at: https://docs.cloudeval.ai/llms.txt
Use this file to discover all available pages before exploring further.
The core loop
- Add a connection. A connection tells CloudEval where to read cloud data or infrastructure code from.
- Create a project. A project becomes the working space for files, topology, report history, and sharing.
- Run reports. Cost and architecture reports turn raw infrastructure into findings you can act on.
- Share or collaborate. Invite teammates, publish a read-only view, or embed the result elsewhere.
Connection types
CloudEval currently supports two practical starting points:- Live Azure environment: connect with service principal credentials and sync cloud data.
- Infrastructure as code: upload or reference an ARM template, including a parameters file when available.
Why projects matter
Projects are the main unit of work in the product. They are where CloudEval stores:- linked connections
- synced or imported infrastructure data
- report outputs and latest snapshots
- share settings and collaborator access
What reports add
Reports are where CloudEval starts paying for itself.- Cost reports surface spend estimates, opportunity summaries, and trend-oriented context.
- Architecture reports summarize overall quality, Well-Architected pillar scores, and high-severity issues.
Sharing model
CloudEval supports three broad ways to work with others:- Private: only people with project access can open it.
- Restricted collaboration: invite named users with editor or viewer access.
- Share links: publish a read-only link or embed view when the project is appropriate to share.
What not to assume
- A provider name in the app is not the same as full support.
- A visible UI option is not always a production-ready path.
- Share links are read-only, not an editing mode.