I know AI is peak hype right now. But it has definitely changed some of our dev workflows already. So we wanted to find a way to let our customers experiment with how they can use AI to make their cloud cost management work more productive.

The MCP Server acts as a connector between LLMs (right now only Claude, Cursor support it but ChatGPT and Google Gemini coming soon) and your cost and usage data on Vantage which supports 20+ cloud infra providers including AWS, Datadog, Mongo, etc. (You have to have a Vantage account to use it since it's using the Vantage API)

Video demo: https://www.youtube.com/watch?v=n0VP2NlUvRU

Repo: https://github.com/vantage-sh/vantage-mcp-server

It's really impressive how capable the latest-gen models are with an MCP server and an API. So far we have found it useful for:

Ad-Hoc questions: "What's our non-prod cloud spend per engineer if we have 25 engineers"

Action plans: "Find unallocated spend and look for clues how it should be tagged"

Multi-tool workflows: "Find recent cost spikes that look like they could have come from eng changes and look for GitHub PR's merged around the same time" (using it in combination with the GitHub MCP)

Thought I'd share, let me know if you have questions.