Reduce LLM costs by measuring the complete cost of successful tasks, then removing unnecessary computation without losing required quality or safety. A cheaper individual model call can become more expensive if it triggers extra retries or review.
Find the expensive paths
- Group usage by workflow, model, prompt version, and customer or project.
- Include failed attempts, tool calls, retries, and evaluation overhead.
- Inspect long prompts, repeated documents, oversized outputs, and repeated requests.
- Compare cost per successful task alongside correctness, safety, and latency.
Test smaller changes first
Remove unused context, constrain outputs to the required task, and avoid retries that cannot change the outcome. Evaluate the revised prompt on a held-out task set. Shorter input is helpful only if the model still has the evidence it needs.
Cache when reuse is valid
Use exact caching for equivalent requests and consider semantic caching only when you can test mistaken matches. Keep authorization and data freshness in the cache design. Provider prompt caching is a separate mechanism; consult the provider for supported models and billing.
Evaluate routing
Route lower-risk, well-defined tasks to a cheaper candidate only after measuring its performance on representative examples. Keep escalation criteria and a fallback for failures. Record route decisions so a cost or quality regression can be investigated.
Report measured savings
Compare equivalent task groups and periods. Report sample sizes, traffic changes, and quality tradeoffs with the cost result. This guide supplies no universal saving percentage.
References and further reading
Maintained by DriftRail. AI assisted the October 2026 content and template revision. Examples are illustrative; they are not measured product benchmarks. Suggest a correction.