SilentFleetKK

SilentFleetKK / computecfo

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🏦 ComputeCFO — Your AI Financial Officer. Track, analyze, and optimize LLM API spending. Budget controls, ROI analysis, cost prediction.

14
1
100% credibility
Found Mar 29, 2026 at 14 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

A Python library that tracks usage costs for AI language models, sets budgets with warnings, and provides spending analysis and savings suggestions.

How It Works

1
💡 Discover ComputeCFO

You're surprised by your AI chat bills and learn about this friendly helper that watches every penny you spend on AI.

2
📦 Bring it into your project

You add this simple tool to your work with one easy step, and it's ready to go.

3
📝 Note your AI uses

After talking to AI, you tell the tool what you used, and it figures out the cost right away.

4
💳 Set spending limits

You pick daily and monthly budgets so it knows when to help you stay safe.

5
🚨 Get smart protections

Before each AI chat, it checks your budget, warns if needed, and even picks cheaper options to save money.

6
📊 See your spending story

You check easy reports with charts, trends, and tips on how to spend smarter.

🏆 Control your AI costs

Now you run AI projects confidently, avoid surprises, and get the most value from your money.

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Star Growth

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AI-Generated Review

What is computecfo?

ComputeCFO is a Python library and standalone dashboard that acts as your AI financial officer, tracking LLM API spending across providers like Anthropic, OpenAI, and Google. It logs costs per call, analyzes breakdowns by model, module, or project, and offers budget controls, ROI analysis, cost prediction, and optimization suggestions to avoid surprise bills. Install via pip, integrate with a decorator for auto-tracking, or spin up a FastAPI-powered dashboard in one command for real-time charts and alerts.

Why is it gaining traction?

It stands out with zero core dependencies, seamless FastAPI endpoints for any app, and smart features like pre-call estimators, auto-model downgrades on budget hits, and anomaly detection via webhooks to Slack or Telegram. Developers hook it in three lines without rewriting code, getting instant ROI insights and projected spending that beat basic logging tools. Multi-project accounting and Graham-inspired model value scoring provide actionable intel no spreadsheet can match.

Who should use this?

AI engineers building chatbots or agents who burn through Claude/GPT credits without visibility. Startup teams managing LLM costs across SaaS features or research pipelines, needing per-project budgets and alerts. Indie devs optimizing spending before it hits $500 surprises.

Verdict

Worth a spin for Python LLM users—excellent docs and MIT license lower the bar despite 14 stars and 1.0% credibility score signaling early maturity. No tests visible, but seed data and examples make prototyping fast; scale cautiously in prod.

(198 words)

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