The no proxy approach makes sense for LLMs calls. The gap is non LLM calls.

Often times damage is done by non LLM calls -- tool calls like sending email, add records, files, placing order, etc. Budget enforcement at the LLM layer wont work for those.

built an open protocol + reference implementation, handles both any tool calls, LLM calls, or any other call: https://runcycles.io, open sourced under Apache 2.0

I built MarginDash so teams can stop over-budget AI requests before they’re sent.

  What it does:
  - Set budgets at company, customer, and feature level
  - SDK checks budget first and blocks requests that exceed limits
  - Your app still calls OpenAI/Anthropic/etc directly (no proxy/gateway)
  - Prompts and outputs go directly between your app and the AI API provider
  - MarginDash only receives usage metadata (token counts)
  - TypeScript and Python SDKs

Flow: Checks limit you set for customer/feature -> sends AI call if within that limit -> records cost

I’d love feedback on any missing enforcement scope you’d need in production

Docs: https://margindash.com/documentation