AI Agent Cost Optimisation — How to Cut Your LLM Bill by 60-80% Without Losing Quality

At development scale, API costs are negligible. At production scale, they become your largest operating expense. Here is the complete playbook — ordered by impact.

The economics of AI agent systems change fundamentally between development and production. During development, you might make a few hundred API calls per day. In production, a system serving a thousand users can make millions. The strategies that are irrelevant at small scale become essential at large scale — and the order in which you implement them determines how much you save.

Strategy 1: Model Routing — 60-80% Cost Reduction

The single most impactful optimisation available to any production AI system is routing different types of requests to different models based on their complexity. GPT-4o mini and Claude Haiku cost 10-20x less than their full-size counterparts and produce acceptable quality for simple tasks.

Add a fast, cheap classifier as the first step in every request pipeline. It categorises the incoming task: simple (FAQ, classification, template filling) → cheap model; complex (multi-step analysis, code generation, nuanced reasoning) → expensive model. In most production systems, 60-70% of requests fall into the simple category. Routing them to the cheap model alone reduces your total LLM cost by 60-70%.

Strategy 2: Semantic Caching — 20-40% Additional Reduction

Traditional caching matches exact strings. Semantic caching matches meaning — if a user asks "What is your return policy?" and a previous user asked "How do I get a refund?", the cached answer may be appropriate. Use a vector similarity threshold (typically 0.92+) to determine cache hits.

Redis with vector search capabilities (Redis Stack) supports this natively. For high-volume FAQ-type systems, semantic caching typically achieves 25-40% cache hit rates within the first few weeks of deployment. Every cache hit costs effectively zero.

Strategy 3: Context Window Management

Token costs are linear with context length. Every unnecessary token in your context costs money on every call. Audit your system prompts for redundancy — most can be reduced by 30-50% without losing effectiveness. Limit conversation history to the last 10 turns, not the entire history. Limit RAG context to the top 3 most relevant chunks, not 10. Summarise long documents before including them in prompts.

Strategy 4: Batch Processing

OpenAI and Anthropic both offer batch APIs that process requests at 50% of standard pricing with a 24-hour SLA. For any non-real-time workload — nightly reports, bulk data enrichment, scheduled content generation, offline analysis — batch processing halves your API costs automatically. The only cost is planning your workflow to separate real-time and non-real-time tasks.