LLM Cost Optimization Strategies That Cut 60-90%
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LLM API costs can be reduced by 60–90% through five compounding architectural strategies: model routing (sending queries to the cheapest capable model, saving up to 84%), fine-tuning smaller models for narrow tasks (80–90% per-token savings), semantic and prefix caching (eliminating redundant inference calls, up to 73% cost reduction), prompt compression and RAG optimization (reducing token bloat via tools like LLMLingua), and real-time budget governance via LLM gateways. The post explains the main cost drivers in production — token pricing mechanics, context accumulation, and redundant inferences — and recommends implementing these strategies in sequence. Portkey's AI Gateway is presented as a platform that unifies routing, caching, observability, and budget enforcement across 3,000+ models, with a cited case study of a delivery platform saving $500K.
