burnlens 0.1.2

See where your LLM money goes. Open-source proxy for AI API cost tracking, waste detection, and budget alerts.

BurnLens

See exactly what your LLM API calls cost — per feature, team, and customer.
Zero code changes. Everything local.

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Install

pip install burnlens
burnlens start
# Dashboard → http://127.0.0.1:8420/ui

Point your SDK at the proxy

# OpenAI — note the /v1 suffix
export OPENAI_BASE_URL=http://127.0.0.1:8420/proxy/openai/v1

# Anthropic
export ANTHROPIC_BASE_URL=http://127.0.0.1:8420/proxy/anthropic

# Google (one import instead of env var)
import burnlens.patch; burnlens.patch.patch_google()

Your existing SDK code works unchanged. BurnLens intercepts, logs, and forwards — nothing else.

Tag any request for attribution

X-BurnLens-Tag-Feature: chat
X-BurnLens-Tag-Team:    backend
X-BurnLens-Tag-Customer: acme-corp

Tags are stripped before reaching the AI provider. They never leave your machine.

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The problem

• OpenAI bills by model, not by feature. You find out at month end.
• Reasoning tokens on o1/o3 can cost 10× more than expected.
• One bad deploy can cost $47K before anyone notices.

BurnLens fixes this at the proxy layer — no instrumentation, no SDK wrapping, no vendor lock-in.

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What you get

• Cost timeline — daily spend trend across all providers
• Attribution — cost by model, feature, team, customer
• Waste alerts — context bloat, duplicate requests, model overkill
• Per-request detail — tokens, cost, and latency for every call

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Providers

Provider	Models
OpenAI	gpt-4o, gpt-4o-mini, o1, o3, o1-mini, and more
Anthropic	claude-3-5-sonnet, claude-3-haiku, claude-opus-4-6, and more
Google	gemini-1.5-pro, gemini-1.5-flash, gemini-2.0-flash, and more

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CLI

burnlens start         # proxy + dashboard
burnlens export        # CSV of last 7 days
burnlens report        # weekly cost summary
burnlens recommend     # cheaper model suggestions
burnlens budgets       # team spend vs limits

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Configuration

# burnlens.yaml (optional — sensible defaults without it)
budget_limit_usd: 500.00
budgets:
  teams:
    backend: 200.00
    research: 100.00

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Contributing

See CONTRIBUTING.md. Issues and PRs welcome.

License

MIT