AI Cost Optimization Guides & Best Practices
Practical techniques with explicit assumptions for measuring and reducing LLM API spend.
Prompt caching essentials
Reuse eligible stable prompts and reference context at the provider's cached-input rate, then measure the actual hit rate and cost change.
- Place stable content (system prompt, tools, references) at the start.
- Keep variable user input at the end of the prompt.
- Enable caching on the static prefix and measure the hit rate.
Model routing by difficulty
Send simple tasks to a small, cheap model and reserve flagship models for genuine reasoning to slash blended cost.
- Classify each request by complexity before inference.
- Route classification and extraction to a small model.
- Escalate only hard tasks to the flagship model.
Output token control
Output costs several times more than input. Cap and shape responses to keep the most expensive tokens in check.
- Set explicit max_tokens on every call.
- Request concise or structured output where a machine consumes it.
- Prefer long context with short answers over verbose generation.
RAG context efficiency
Retrieval quietly inflates input tokens. Tune chunking and reranking to send fewer, sharper passages.
- Test several chunk sizes against a fixed evaluation set.
- Rerank candidates and keep only passages that meet the measured relevance threshold.
- Strip boilerplate and deduplicate before sending.
AI agent budgeting
Agents re-send growing context on every loop. Cap steps and compress history to keep runs affordable.
- Set a hard step limit per agent run.
- Summarize conversation history instead of carrying full transcripts.
- Use a cheap model for orchestration and tool selection.
Infrastructure cost hygiene
Beyond tokens, retries, embeddings, and idle capacity add up. Make cost observable across the stack.
- Reduce retries with robust error handling and validation.
- Version embeddings and re-embed incrementally.
- Right-size or autoscale self-hosted GPU capacity.