10 Strategies to Reduce Your AI API Costs by 80%

The Cost Problem

LLM API costs can spiral quickly. A modest app serving 10K users can cost $500-2000/month. Here's how to bring that down.

1. Cache Aggressively

• Cache identical or similar queries with semantic caching.
• Use Redis or in-memory LRU for hot paths.
• Cache rate: 30-60% for most applications.

2. Use Smaller Models for Simple Tasks

• Route easy queries to GPT-4o-mini or Haiku ($10x cheaper).
• Reserve GPT-4o or Claude for complex reasoning.
• Use a classifier to determine routing.

3. Optimize Prompts

• Shorter system prompts = fewer input tokens.
• Remove redundant instructions.
• Use structured output to avoid verbose responses.

4. Batch Requests

• OpenAI and Anthropic offer batch APIs at 50% discount.
• Queue non-urgent requests and process in batches.

5. Limit Context Window

• Only send relevant context, not entire documents.
• Use RAG to retrieve just the needed chunks.
• Summarize long conversations before sending.

6. Monitor and Set Budgets

• Use usage dashboards to track spending.
• Set hard limits per user and per feature.
• Alert on unusual spikes.

7. Fine-Tune Instead of Prompt

• A fine-tuned small model can replace a large prompted model.
• Saves 5-10x on per-token costs at scale.

8. Use Streaming

• Streaming lets users cancel early, saving output tokens.
• Better UX too — users see partial results.

9. Self-Host for High Volume

• At >10M tokens/day, self-hosted open-source models are cheaper.
• Use vLLM or TGI for efficient serving.

10. Negotiate Enterprise Plans

• Most providers offer volume discounts.
• Commit to monthly spend for better rates.