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Open-source AI Cost Intelligence Platform — smart routing, semantic cache, waste detection, prompt optimization

2 starsPython

TokenMeter – Open-source observability layer for LLM token costs

by Mohit8880·Feb 18, 2026·1 point·3 comments
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AI Analysis

●●SolidSolve My ProblemNiche Gem
The Take

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.

Target Audience

ML/AI teams, backend developers and infra engineers who need real-time cost observability for LLM usage (engineering managers, SREs, and startup teams running experiments)

Post Description

Hi HN,

I built TokenMeter after seeing how unpredictable LLM costs become as usage scales.

Most teams optimize prompts but lack real-time cost observability.

This project tracks token usage and exposes cost insights to help AI teams stay financially aware.

Would appreciate feedback on architecture and missing observability features.

Thanks.

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