LLM成本优化2026：8个把Token开支砍掉60-90%的技巧

账单跟着用户数一起涨，没人知道哪个功能在烧钱。成本优化是门手艺，不是折扣。

LLM Cost Optimization: The Techniques That Actually Move the Number

LLM costs are the new cloud bill - they grow silently and surprise everyone at month-end. The good news: most teams leave 60-90% savings on the table. These eight techniques, ordered by impact, are the ones that work in 2026.

The Eight Techniques

1. Prompt caching. Providers discount reused prefixes by 50-90%. Keep system prompts stable and static content first. This is the biggest lever with the least effort.

2. Model routing. Not every request needs the flagship model. Route simple requests (classification, extraction, formatting) to small models (gpt-4o-mini-class, Haiku-class, or local) and reserve the big models for complex reasoning. Savings: 80-95% on routed traffic.

3. Context compression. Summarize chat history instead of replaying it. Tools that compress prior turns cut input tokens by 50-80% in long conversations.

4. Structured outputs over prose. Asking for JSON forces shorter, cheaper responses than asking for explanations - and it is easier to validate.

5. A gateway with cost tracking (LiteLLM/Portkey). You cannot optimize what you cannot measure. Per-feature, per-user cost dashboards reveal the 20% of features burning 80% of the budget.

6. Batch API where latency allows. OpenAI and others discount batch endpoints by 50%. Offline jobs (summaries, embeddings, data cleaning) should never hit the real-time API.

7. Local models for private/high-volume tasks. Self-hosted small models (via vLLM/Ollama) have near-zero marginal cost; keep the cloud for the hard 20%.

8. Budget alerts and hard caps. Set spend limits per key/feature so a runaway loop cannot silently burn a week of budget.

What a Realistic Roadmap Looks Like

Week 1: enable caching + cost tracking. Week 2: route simple traffic to small models. Week 3: compress contexts and move batch jobs to the batch API. Most teams see a 60-80% reduction by week 3 without touching product behavior.

FAQ

What is the biggest single saving? Prompt caching or model routing - both are 50-90% on their portion of traffic.

Will cheaper models hurt quality? For simple tasks, no; benchmark routing decisions on your own eval set.

Do I need an AI gateway to save money? Not strictly, but cost tracking is what lets you find the leaks.

Is local inference free? Marginal compute cost only - but you own the hardware and ops burden.