8 Proven Ways to Reduce LLM API Costs by 60–90%

LLM API costs can spiral fast — especially as you scale from prototype to production. The good news: most teams are paying 3–10x more than they need to. These 8 techniques, applied systematically, typically reduce LLM costs by 60–90% without sacrificing quality.

1. Model Routing: Use the Right Model for Each Task

The biggest cost lever: stop sending every request to your most expensive model. Most production workloads are heterogeneous — some queries are simple (classification, short Q&A), others need flagship capability (complex reasoning, coding). Route accordingly:

A simple routing classifier (itself running on a cheap model) can automatically categorize incoming queries. Typical result: 60–80% of requests route to cheap models, cutting average cost by 4–5x.

2. Prompt Caching: 75–90% Off Repeated Tokens

If your prompts contain a consistent prefix — system prompt, few-shot examples, RAG context — prompt caching is the single highest-ROI optimization available. You pay full price once, then 10–25% for subsequent requests hitting the cached portion.

3. Batch API: 50% Off Async Workloads

OpenAI and Anthropic both offer batch APIs with a 50% discount for async processing. If your pipeline doesn't need real-time responses — document processing, data enrichment, overnight classification jobs — batch is a free 50% saving. No code changes to your prompts, just a different submission mechanism.

4. Token Budgeting: Cut Prompt Bloat

Most prompts are 30–50% longer than they need to be. Audit and trim:

• Remove redundant instructions: “Please”, “thank you”, repeated context the model already has.
• Trim conversation history: Keep only the last 3–5 turns; older history adds tokens without improving quality.
• Compress RAG chunks: Summarize retrieved documents before passing them as context.
• Use structured formats: JSON and XML are more token-efficient than verbose prose instructions.

5. Output Length Control

Output tokens cost 3–5x more than input tokens on most models. Set explicit max_tokens limits and instruct the model to be concise. For structured outputs, use JSON mode to eliminate wrapper text.

6. Switch Providers for Your Workload

Different providers have different price leaders for different tasks. A quick benchmark across:

7. Response Caching at Application Level

For deterministic queries — FAQ answers, product descriptions, standard summaries — cache the LLM response at your application layer (Redis, CDN). If the same question gets asked 1,000 times/day, you pay for it once.

8. Embeddings + Small Models for Pre-filtering

Before sending a query to an expensive LLM, use embeddings to check:

• Is this query similar to a cached response? → Return cached answer.
• Is this a simple lookup? → Use keyword search instead of LLM.
• Is this out-of-scope? → Return a fallback without consuming tokens.

Calculate your potential savings: Token cost calculator → | Cost routing tool → | Cheapest LLM APIs →