Every LLM call tagged by group, user, and model. Set spend limits by tier. Plan where your AI budget goes — and know when it drifts.
search costs 5× more than chat per call — 18k vs 3.7k avg tokens.
Most LLM observability tools sit between your app and your provider. Every call routes through their servers — adding latency, a new dependency, and a question your security team will eventually ask. We work differently.
Your requests travel through a third-party server before reaching the provider.
Latency added — every call takes an extra hop through their infrastructure
New failure mode — their outage becomes your production incident
Data exposure risk — your requests physically pass through their servers
Your requests go directly to the provider. Always. The SDK reads metadata after the call resolves.
Zero latency impact — the SDK reads token counts after your call resolves
No new dependency — if our service is down, your LLM calls are completely unaffected
Prompts never leave your stack — we log cost metadata only, not your conversations
Direct to provider — your API calls never touch our servers
Prompt content never logged — token counts and metadata only
Our downtime is never your downtime — zero coupling to your call path
Provider dashboards show you what you owe. LLM Cost Tracker shows you why — and what to do about it.
Anthropic Console, OpenAI Platform
Three capabilities. One SDK. No infrastructure to manage.
We noticed our Anthropic bill going up and had no idea which searches were expensive, which were cheap, or why. We dropped in LLM Cost Tracker to find out.
Within 10 minutes we spotted a 5× cost variance between two searches — $0.0123 vs $0.0608 — driven by token count differences we couldn't see before.
Brian
Founder, contractclues.com
The SDK reads token counts and metadata from the response object after your call resolves. It never sees your prompts, your users' messages, or your model outputs.
Start free. Upgrade when your usage grows.
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