GitHub - Rodiun/frugon: Free, local, open-source LLM cost analyzer — see where your LLM bill leaks, on your machine.

Your LLM bill is leaking — see exactly where, on your machine. Free, local, open-source LLM cost analyzer. Point Frugon at your LLM call logs and see — on your machine — how much you'd save by switching or routing models.

Your data never leaves your machine. Your keys go straight to your own providers. Nothing reaches us.

Install & run # one-shot (no install) uvx frugon analyze ./logs.jsonl

# permanent install pipx install frugon frugon analyze ./logs.jsonl

# for --measure (optional): samples real prompts through your own provider keys pip install 'frugon[measure]' frugon analyze ./logs.jsonl --measure No logs yet? See Getting your logs below, or run frugon analyze --demo to see it work on a bundled sample. Getting your logs frugon reads JSONL files in the OpenAI request/response format. There are two ways to produce them. Option A — frugon capture (proxy shim) frugon capture is a local HTTP proxy that sits between your app and your provider. Every call is forwarded unchanged to your real provider and saved as one JSONL line. # Start the shim (default port 8787, output file capture.jsonl) frugon capture --out ./logs.jsonl

# Then point your app's base URL at the shim instead of api.openai.com: OPENAI_BASE_URL=http://127.0.0.1:8787 your-app # bash / zsh $env:OPENAI_BASE_URL="http://127.0.0.1:8787"; your-app # PowerShell (Windows) # or in code: client = OpenAI(base_url="http://127.0.0.1:8787/v1") Options: --port, --out, --upstream (override the forwarding target), --verbose (print one line per captured call to verify it's recording), --proxy (opt in to route upstream calls through a proxy — by default frugon ignores any ambient HTTP_PROXY / HTTPS_PROXY, so your API key never passes through a third-party proxy).

The shim adds no latency overhead on localhost and makes no calls to any frugon endpoint. Option B — write JSONL directly If you already capture logs (e.g. via middleware or a provider SDK callback), write one JSON object per line with this shape: { "model": "gpt-4-turbo", "request": { "messages": [ {"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": "Summarise this document: ..."} ] }, "response": { "choices": [{"message": {"content": "Here is the summary: ..."}}] }, "usage": { "prompt_tokens": 312, "completion_tokens": 84 }, "timestamp": "2024-11-01T14:22:01Z" } usage.prompt_tokens / usage.completion_tokens — preferred when present; frugon falls back to its own tokenizer when absent. timestamp is optional but enables frugon to project costs over a real observed span. model is required; everything else degrades gracefully. 5-minute path from install to first analysis uv tool install frugon # or: pipx install frugon / pip install frugon frugon capture --out ./logs.jsonl & # start the proxy in the background # ... run your app, make some LLM calls ... frugon analyze ./logs.jsonl # see the cost breakdown and routing recommendation What it does

Cost analysis — fully local, no LLM calls, no network. Tokenizers + pricing + arithmetic on your machine. Quality visibility (--measure, optional) — samples your traffic through candidate models using your own API keys, sent directly to your own providers. Never to us. --measure needs pip install 'frugon[measure]' and a provider API key (OPENAI_API_KEY, etc.); calls go to your own provider, never to us. On --demo, sampling is pinned to a single OpenAI model so the try-out needs only OPENAI_API_KEY; on your own logs, --measure samples the actual recommendation.

Routing recommendation — "move these X% of calls to a cheaper model and save ~$Y/mo; keep the hard Z% where they are." Comes with an explicit quality caveat so you know what you're trading. Run frugon models to see the model names available for --candidates (optionally frugon models gpt-4o to filter by substring). Share the result — add --report savings.html (or .md) to write a clean, shareable report you can drop into a PR, a Slack thread, or a budget review. Fast on real logs — everything runs locally and is comfortable well past 100k records. The bundled ~56,100-call demo (frugon analyze --demo) prices in a few seconds. Very large logs (>200k records) may take a little longer; Frugon shows a live progress bar and a one-line heads-up so you can see it working. There's no hard limit.

Example output $ frugon analyze --demo --candidates claude-sonnet-4-5,gpt-4.1,claude-haiku-4-5,gemini-2.5-flash,deepseek-v4-flash

┌─ frugon · cost analysis ────────────────────────────────────────────────────┐ │ │ │ Analyzed 56,100 calls · baseline gpt-5.5 (your current model) │ │ Current spend $549.46 / mo │ │ │ │ Route 36,100 easy calls (64.4%) → deepseek-v4-flash within │ │ tolerance │ │ Keep 10,000 hard calls (17.8%) → gpt-5.5 │ │ Keep 10,000 already on deepseek-v4-flash (17.8%) already optimal │ │ — no action │ │ │ │ New spend $343.91 / mo │ │

│ │ SAVING $205.55 / mo · 37.4% lower │ │ │ └─────────────────────────────────────────────────────────────────────────────┘ Candidates considered claude-sonnet-4-5 $452.23 / mo 17.7% lower Strong considered gpt-4.1 $405.89 / mo 26.1% lower Capable considered claude-haiku-4-5 $377.82 / mo 31.2% lower Capable considered gemini-2.5-flash $356.35 / mo 35.1% lower Strong considered deepseek-v4-flash $343.91 / mo 37.4% lower Strong recommended Each candidate is shown under the same quality-preserving split (easy calls to the candidate, hard calls kept on baseline); the biggest saving is the headline recommendation, and when savings tie at the precision shown the higher quality tier wins. Run --measure --judge to score each candidate's quality.

Accounting 36,100 routed + 10,000 kept (gpt-5.5) + 10,000 already on cheaper deepseek-v4-flash = 56,100 analyzed Upper bound a full swap to deepseek-v4-flash saves ~98.1% — run with --verbose for detail Quality tier gpt-5.5: Elite → deepseek-v4-flash: Strong (LMArena) Prices synced 2026-07-02 Quality synced 2026-07-02

⚠ Quality is not verified — 'within tolerance' is an offline estimate; run --measure to confirm it on your real outputs before you switch.

Your data never leaves your machine. Your keys go to your own providers. → Route every call automatically and hold the saving: https://frugon.rodiun.io

Recommendations use a curated set of current top models across providers, drawn from OpenRouter usage rankings. Prices synced 2026-07-02 from the LiteLLM registry. Run `frugon update` for the full live roster. This is bundled sample data — run `frugon analyze <your-logs>` for a recommendation on your own logs.

Your numbers depend on your logs and your locally synced pricing/quality data. Run frugon analyze --demo --candidates claude-sonnet-4-5,gpt-4.1,claude-haiku-4-5,gemini-2.5-flash,deepseek-v4-flash to see the same output on your machine. Quality tiers for reasoning models reflect the model at its default/typical reasoning effort — effort changes how many tokens a call spends thinking, not its per-token rate, so it never affects the price shown above. How it's different A provider's billing dashboard tells you what you already spent, and a raw token counter prices a single call — Frugon prices your real logs against every model, locally, and tells you which calls to move and which to keep. Realistic savings Based on RouteLLM's published research (LMSYS):

Traffic mix Typical saving

General mixed workload 30 – 50%

Easy / repetitive (high MT-Bench similarity) up to ~85%

Hard reasoning / MMLU-heavy ~30%

Your actual number comes from your logs. Frugon never inflates — it shows what the math says for your data. Is this you?

Agent builders — your GPT-4o agents are expensive; most easy hops don't need them. AI dev teams — monthly LLM bill is real; routing pays for itself in days. RAG & support — retrieval + rerank is cheap; the final answer call doesn't have to be Opus. Data-ETL pipelines — batch extraction is 100% repeatable; mini models handle it fine. Indie hackers — every dollar saved is a dollar of runway.

Keep the savings This is a one-time snapshot. Want it to keep routing automatically and hold the savings? → frugon.rodiun.io Star the repo if this saved you money. Contributing Bug reports and pull requests are welcome — see CONTRIBUTING.md. Frugon is deliberately small: six commands (analyze, capture, models, update, pricing, quality), three capabilities (cost analysis, quality visibility, routing recommendation). Gateways, live routing proxies, web UIs, and multi-tenant accounts are out of scope by design.

Built by Rodiun. MIT licensed.