How to Reduce LLM API Costs
How can I reduce my LLM API costs?
Four levers move an LLM bill: switching to a less verbose model for the same job (up to 94% on the output side), the batch API for asynchronous jobs, and trimming redundant input context. Each saving below is computed from priced-model data, not a hand-typed estimate, and every guide states the tradeoff it costs you.
Levers, ranked by savings
| Lever | Savings range | Applies to | Tradeoff |
|---|---|---|---|
| Model verbosity → | 91–94% | Any output-token-heavy workload. Does nothing for jobs bottlenecked on input tokens (large retrieved context, long documents) since verbosity only scales the output side. | A less verbose model is not free to swap to — it is a different model, with a different accuracy profile on your specific task. |
| Batch API → | 50% | Only jobs that can tolerate asynchronous turnaround — batch jobs typically return within 24 hours, not synchronously. Live chat, agents, and anything user-facing in real time cannot use it. | Batch pricing is a different API shape, not a toggle on the one you are already calling: you submit a file of requests and poll for a completed job, which means retry logic, partial-failure handling, and a result-collation step you did not need on the synchronous endpoint. |
| Context trimming → | 9–47% | Input-heavy shapes: RAG, long-document workloads, and chatbots with deep conversation history. Does nothing for short-input, long-output jobs like content generation. | Trimming context is an accuracy risk disguised as a cost optimization: the tokens cheapest to cut (older conversation turns, lower-ranked retrieved chunks) are sometimes the ones a specific answer actually depends on, and you only find out when the answer degrades. |
Every range is derived at build time from priced-model or provider data — see each guide for the exact computation. Prompt caching is not listed yet: our cache-read assumption is a flat modelling constant, not sourced per provider, so we are not publishing a savings claim on it until that data exists.
Price it on your own workload
Every lever's guide ends with a worked example priced on a real workload shape from the cost calculator, and links into it with your parameters pre-filled.