How To Reduce LLM Token Costs by 70–90%
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A practical guide to reducing LLM API costs by 70–90% through architectural decisions rather than prompt tweaks. Key strategies include tiered model routing (cheap models for 90% of traffic, premium only for 1%), aggressive prompt compression, response caching for repeated queries, using embeddings in RAG systems to cut context size, limiting output tokens, batching non-real-time requests, and building multi-step AI pipelines. Includes a real pricing comparison of models like Gemini Flash Lite, DeepSeek V3, GPT mini, Claude Sonnet, and Claude Opus, plus a concrete example showing how 10M tokens/month drops from ~$140 to $20–$40 with optimized routing.
Table of contents
1. Stop Using One Model for Everything (Biggest Mistake)2. Use Cheap Models Aggressively (They Are Better Than You Think)3. Token Reduction = Instant Cost Savings4. Cache Everything (Massive Savings)5. Use Embeddings Instead of Full Prompts6. Avoid Over-Context (Silent Cost Killer)7. Use Small Language Models (SLMs) Where Possible8. Batch Processing Instead of Real-Time9. Output Optimization (Hidden Goldmine)10. Multi-Step AI Pipelines (The Pro Move)🧠 1. Cheap Tier (High Volume)⚡ 2. Mid Tier (Balanced)🧨 3. Premium Tier (Use Sparingly)Sort: