LLM Cost Optimization: 10 Tips to Reduce AI Inference & Token Costs

The nOps blog, in an article published on June 22, 2026, provides a comprehensive guide to optimizing costs associated with Large Language Models (LLMs), offering ten actionable tips for both API-based inference and self-hosted environments. The core premise of the article is that effective cost management is crucial for sustainable AI operations, especially as LLM usage scales. For API-based LLM usage, the primary cost driver identified is token consumption. The article highlights that both input and output tokens incur charges, with output tokens often being 3-5 times more expensive. To mitigate this, nOps strongly recommends aggressive prompt length optimization. This involves reducing the input token count without sacrificing the quality of the task. Practical methods include keyphrase extraction to replace full documents with concise summaries, pre-summarizing long texts before feeding them to the model, and removing verbose or redundant instructions from prompts. Such prompt compression techniques can yield substantial cost savings, with some reports indicating 70-94% reductions. Another significant strategy is model routing, which involves directing requests to the most cost-effective model capable of handling the task. The article suggests a tiered architecture where expensive, frontier models are reserved for complex tasks, while smaller, faster, and significantly cheaper models (e.g., GPT-4o-mini, Claude Haiku, Llama 3-8B) handle routine operations like extraction, classification, and FAQs. This approach can dramatically cut overall spend if a majority of requests can be managed by less resource-intensive models. Semantic caching is also presented as a powerful optimization technique. By storing and reusing responses for semantically similar queries, organizations can reduce redundant API calls and processing, thereby lowering inference costs. The article also touches upon self-hosted inference cost optimization, where GPU compute hours are the main expense. Recommendations here include efficient batching of requests, right-sizing GPUs to match workload demands, and implementing intelligent autoscaling strategies to ensure resources are only utilized when needed. Furthermore, the nOps post advises continuous monitoring of token consumption per request to identify high-cost workloads and prioritize optimization efforts. It also mentions leveraging GenAI cost optimization tools to automate many of these processes. By implementing these strategies, organizations can achieve substantial cost reductions in their LLM deployments, ensuring that their AI initiatives remain economically viable and scalable.