LLM Cost Optimization 2026: 8 Techniques That Cut Token Spend by 60-90%

Your LLM bill grew with your user count - and nobody knows which feature is eating the budget. Cost optimization is a discipline, not a discount.

## 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.