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.
Table of contents
Common cost drivers in production LLM applicationsKey cost optimization strategiesHow to cut costs without breaking output quality, and how Portkey enables itBuilding your LLM cost optimization stackCommon questions about LLM cost optimizationSort: