Seven Architecture Techniques to Cut LLM Inference Costs in Production

Running large language models in production can become expensive quickly, as a single user request may trigger multiple model calls, large prompts, retries, or agent loops. Key cost-reduction strategies include routing requests to smaller models based on complexity, minimizing token usage by trimming conversation history and system instructions, and caching responses to avoid paying for repeated inference. For retrieval-augmented generation pipelines, filtering and reranking chunks before sending them to the LLM reduces input tokens without sacrificing answer quality. Agentic workflows should have explicit limits on iterations, tool calls, and retries to prevent runaway inference costs. Non-urgent workloads such as bulk summarization or document classification can also be batched and processed asynchronously to improve resource efficiency.