Cut 90%+ of Your LLM Costs—Without Losing Accuracy
If you’re using large language models (LLMs) for classification tasks, you’re likely overspending. TRACER is built to fix that.
👉 https://github.com/adrida/tracer/
Replace 90%+ of LLM Calls
Most LLM pipelines send every request to an API—even when the task is simple. TRACER takes a smarter approach.
It learns from your LLM’s outputs and trains a lightweight traditional ML model to handle the majority of cases. The LLM is only used for harder edge cases.
The result: replace 90%+ of LLM calls while keeping performance high.
Reliable, Not Risky
TRACER doesn’t rely on guesswork. It uses a calibrated “acceptor gate” to decide when the ML model is safe to trust.
You set the desired agreement level (e.g., 95%), and TRACER enforces it. This gives you:
Predictable performance
Controlled risk
Confidence in production
Self-Improving System
As more data flows through your system, TRACER keeps learning from new LLM outputs. Over time, the ML model improves and handles even more cases.
This creates a powerful loop: More usage → better model → fewer LLM calls → lower costs
Built for Real Use
TRACER fits easily into existing workflows:
Works with modern embedding pipelines
Supports multiple ML models
Includes tools to analyze performance
It’s ideal for classification-heavy use cases like support tickets, intent detection, and content moderation.
A Smarter Approach to LLMs
LLMs are powerful—but not every task needs them.
TRACER helps you combine LLMs with traditional ML in a smart way:
Use LLMs where needed
Use ML where it’s enough
Route efficiently between them
TRACER isn’t just an optimization—it’s a better way to scale AI systems.
