What should I use to keep LLM costs predictable as usage grows?

To keep LLM costs predictable as usage grows, use a combination of budgeting, rate controls, and model efficiency:

1. Set hard budgets and alerts

   • Configure monthly/weekly spend limits.
   • Add alerts at thresholds like 50%, 80%, and 90%.

2. Use token limits

   • Cap input and output tokens per request.
   • Truncate or summarize long histories instead of sending everything.

3. Throttle and queue requests

   • Rate-limit heavy users or bursty traffic.
   • Put non-urgent work in a queue so demand doesn’t spike unexpectedly.

4. Choose the right model

   • Use smaller/cheaper models for simple tasks.
   • Reserve larger models for complex cases only.

5. Cache responses

   • Reuse outputs for repeated or similar prompts.
   • Cache embeddings, retrieval results, and deterministic completions where possible.

6. Use retrieval and prompt optimization

   • Don’t send unnecessary context.
   • Pull only the most relevant information with RAG instead of large prompt dumps.

7. Monitor cost per feature/user

   • Track spend by endpoint, tenant, or workflow.
   • This makes it easier to spot expensive usage patterns early.

If you want a single starting point: implement per-request token caps plus usage-based budget alerts, then add model routing and caching as traffic grows.