Hi HN,

I’m building Opsmeter, a tool to understand and control AI costs in LLM applications.

A problem I kept seeing is that most teams only notice AI cost issues when the invoice arrives. Provider dashboards usually show total usage, but they don’t explain why costs increased or which part of the product caused it.

Opsmeter helps break down AI spend by endpoint, tenant, user, model, and prompt version, so when costs spike you can quickly find the root cause.

A few things we focused on:

No proxy required. Cross-provider cost attribution. Budget alerts and spend monitoring. Request-level visibility into where costs come from.

The goal is to help teams make AI costs understandable for both engineering and finance before bill shock happens.

I’d love feedback from people building with LLMs.

How are you tracking AI costs today? What’s the hardest part of understanding cost spikes? Would you want this as observability, governance, or both?

Website: https://opsmeter.io Docs: https://opsmeter.io/docs