How to Track and Cut LLM Token Costs

Most teams discover their LLM costs the same way: a finance ping about a bill that doubled. The provider dashboard shows a total, maybe a breakdown by API key, but not the thing you actually need — which workflow, agent, or feature spent the money, and whether that spend was worth it. Cost control starts with attribution.

Step 1 — Attribute every token

You can't cut what you can't see. Capture input tokens, output tokens, and cached tokens on every call, and tag each call with the model, the agent or workflow, and ideally the team or feature. Once spend is attributed this way, the expensive 10% of your traffic usually becomes obvious — and it's rarely what people guessed.

Step 2 — Find the waste

Common culprits: a frontier model doing work a small model handles fine; retry loops that double or triple call volume; prompts bloated with context that doesn't change the answer; and duplicate calls that caching would eliminate. Each is measurable once you have per-call attribution and can compare models on your real traffic.

Step 3 — Cut without hurting quality

Downgrade the workloads where a cheaper model scores the same on your outputs — validated against real requests, not benchmarks. Trim prompt context that doesn't move results. Add caching for repeated calls. Cap runaway retry loops. Done in that order, most teams cut 20–40% of spend with no measurable quality loss.

Step 4 — Make it continuous

Cost creep comes back the moment you stop watching. Set budgets per workflow, alert on spend anomalies, and re-run model comparisons periodically as providers change pricing. Obsivara automates this loop — per-model/agent/workflow spend, waste detection, model comparison on live traffic, and anomaly alerts — so cost stays controlled instead of drifting until the next surprise invoice.