What is TRACER?
TRACER is an open-source routing layer that trains a lightweight machine-learning surrogate on your LLM's own production classification traces. It routes the predictable 90 percent of traffic to the surrogate (near-zero cost) and defers only the hard 10 percent back to the LLM. Available as a Python SDK (pip install tracer-llm) or as a one-click hosted endpoint.
How do I reduce LLM costs?
To reduce LLM costs in production, route only the requests that genuinely need an LLM. Most production LLM workloads are repetitive classification tasks (intent detection, content moderation, support triage, tool selection). TRACER trains a small ML surrogate on your existing LLM traces and routes the predictable 90% of traffic to that surrogate at near-zero cost, deferring only the hard 10% back to the LLM. Typical impact: 5,000× cheaper per call on the routed slice and 80× lower latency, with a parity gate guaranteeing quality stays above your threshold. No fine-tuning, no manual labeling required.
What is LLM routing?
LLM routing sends each request to the cheapest model that can answer it correctly. Most model routers pick which LLM to call (frontier vs smaller LLM). Tracer routes predictable requests out of the LLM stack entirely, into a lightweight ML surrogate trained on your own production traces. Routing is gated by measured agreement with your teacher LLM, so quality stays above your threshold. Available as tracer-llm on PyPI or as a hosted multi-tier routing endpoint.
How much does TRACER reduce LLM cost?
On the Banking77 benchmark with 10,000 daily classification calls, TRACER offloaded 92.2 percent of traffic to a lightweight ML surrogate at 0.961 teacher agreement, cutting per-day cost from $44.50 to $3.47, about $14,976 saved per year. Actual savings depend on your workload's predictability; the more repetitive the traffic, the larger the saving.
How is TRACER different from a model router or smaller LLM?
Most LLM cost tools keep the request inside the LLM cost structure: caching only works on exact repeats, prompt optimization shaves tokens, smaller LLMs are still orders of magnitude more expensive than CPU-class ML, and model routers only pick which LLM to call. TRACER routes predictable slices out of the LLM stack entirely, gated by measured agreement (parity) with your teacher LLM so quality never silently degrades.
How does TRACER guarantee quality on the routed traffic?
TRACER deploys a parity gate: the surrogate goes live only when its agreement with the teacher LLM exceeds your threshold (for example 0.95) on held-out calibration data. If a workload is too hard, TRACER refuses to route it and everything stays on the LLM. Every routing decision exposes the matched cluster, the per-model accuracy on that cluster, and the confidence bound, fully auditable.
What kinds of workloads does TRACER work for?
TRACER targets repetitive LLM classification workloads: intent classification, content moderation, compliance scanning, support triage, document extraction, eval pipelines, and per-step tool selection in agentic workflows. Anywhere the same kinds of decisions happen many times a day, TRACER finds the predictable slices.
How long does it take to deploy TRACER?
On the hosted version, the setup wizard is six steps: pick your task, point to your traces, choose embeddings, pick your model menu, set a quality target, and get a live HTTPS endpoint. The build runs in the background and takes minutes (not days) depending on dataset size. With the open-source SDK, the equivalent is pip install tracer-llm followed by tracer fit traces.jsonl --target 0.95 and tracer serve.
Is TRACER open source?
Yes. The TRACER routing core is MIT-licensed and available on GitHub at github.com/adrida/tracer and on PyPI as tracer-llm. The hosted version layers managed infrastructure (managed embeddings, hosted endpoint, monitoring, audit dashboard) on top of the same OSS core.
Do I need to label my training data?
No. Every classification call your LLM already makes is a labeled (input, output) pair already in your logs. Tracer fits the surrogate directly on these traces with no manual labeling. As traces accumulate the surrogate refits and coverage compounds: 43% on day 1, 98% on day 2, 100% by day 4 in the demo workload.
How do AI SDR, sales-AI, and GTM tools reduce LLM costs with Tracer?
AI SDR and GTM platforms hit the same frontier-LLM call millions of times: lead scoring, intent classification on inbound replies, account triage, outbound-personalization categorization. Tracer trains a lightweight classifier on the calls your stack is already paying for and answers the repeated ones for near-zero cost. Typical impact on the routed slice: 70 to 95 percent lower per-call cost, sub-10ms latency, and a parity gate that refuses to route when the workload is too hard. Your prompts and providers stay the same.
How do AI HR and recruitment platforms cut LLM inference cost?
Resume screening, candidate-job matching, and skill extraction are exactly the workflow shape Tracer is built for: one structured LLM decision, repeated across every applicant and every requisition. Tracer learns from your existing teacher-LLM traces, routes the predictable slice to a lightweight ML surrogate, and defers ambiguous cases back to the LLM. Live deployments in production at Obside (intent-news matching, 95% saved vs GPT-5) and getclaw (agent tool selection, ~50% end-to-end cut) show the pattern transfers across verticals.
How do agentic systems and AI agents reduce per-step LLM cost?
Inside an agent loop, tool selection, planner-executor routing, and safety classification are repeated decisions an LLM does not need to make every time. Tracer ships a native plugin for Hermes (and an OpenAI-compatible endpoint for any other harness) that routes these decisions through a lightweight classifier trained on your agent's own traces. In production at getclaw.sh, end-to-end agent cost dropped about 50 percent on measured traces with no quality degradation.
What is the best way to reduce LLM cost on high-volume moderation, compliance, and screening?
Content moderation, abuse detection, KYC scoring, and compliance scanning all share the same shape: a single LLM verdict, repeated thousands of times per day. Tracer trains a small classifier on your existing moderation traces, routes the obvious cases locally for free, and reserves the frontier LLM for ambiguous or out-of-distribution inputs. The parity gate guarantees the surrogate only ships when measured agreement with your teacher clears your threshold (e.g. 0.95), so quality stays above the bar.
Who is behind Tracer AI?
Tracer AI is built by Adam Rida and the DeepRecall team. Adam holds a PhD in machine learning and is a repeat founder. The Tracer routing method is documented in a research paper featured on Hugging Face (huggingface.co/papers/2604.14531, arXiv 2604.14531). Tracer is already running in production at Obside (French fintech, automated trading) and getclaw (agent infrastructure), with three additional deployments announcing soon.
How is Tracer different from fine-tuning, OpenAI distillation, or running a smaller LLM?
Fine-tuning and OpenAI's distillation keep the request inside the LLM cost structure: you still pay per-token rates on a model that needs a GPU. A smaller LLM is still orders of magnitude more expensive than a CPU-class classifier at scale. Tracer routes the predictable slice out of the LLM stack entirely, into a lightweight ML surrogate trained on your own traces. The parity gate measures surrogate-teacher agreement and refuses to route when the workload does not meet your quality threshold, so quality stays above the bar.
What does Tracer AI cost? Is there a markup on LLM calls?
Zero markup on LLM inference. Tracer sits on the inference path so we can measure savings, but provider tokens pass through at cost. You bring your own keys (OpenAI, Anthropic, Bedrock, self-hosted) and switch providers anytime. Tracer charges 20 percent of the frontier-LLM spend we measurably remove from your bill. No seat licenses. The open-source SDK (pip install tracer-llm) is MIT-licensed and free forever.
