The read-only correlation of request → model → token → $ is smart — you get a shot at answering 'why did our bill spike?' without routing traffic through a proxy. It also claims workflow-level analysis (long prompts, retries, agent loops) and concrete recommendations like model routing and context trimming; useful, but the pitch leaves open how reliable the heuristics and team-attribution are across providers.
Platform/AI engineers, SREs, FinOps teams, engineering managers and CTOs at companies using multiple LLM providers
Proxying every LLM call to log tokens is the right kind of blunt instrument — you get per-developer, per-model cost telemetry immediately. Smart routing and the built-in semantic cache (claims 45–80% savings) are the most useful ideas here, but the default SQLite backend and admin/admin creds scream MVP rather than production-ready scale.
Local budget caps block requests before provider dashboards even update the bill.
Not a proxy — optimizes Anthropic and OpenAI caching without adding latency or seeing your data.
Prompt clustering with cost attribution when LangSmith already does observability.
Local-first agent observability without cloud signup — LangSmith alternative that keeps data on your machine.
Prompt compression API cuts token bills 40-60%, integrates in two lines.
