How to Cut Your AI Agent API Costs by 80% — The Complete Optimisation Playbook

At development scale, LLM costs are negligible. At production scale, they become your largest operating expense. Here are the four strategies that reduce costs dramatically without reducing output quality.

Most guides to AI agent cost optimisation focus on small improvements: shortening prompts by 10%, switching from one model to a slightly cheaper one, reducing max tokens. These produce marginal gains. The strategies below produce 60-80% cost reduction — the kind of improvement that changes the unit economics of your AI product.

Strategy 1: Model Routing — The 60-80% Solution

The single most impactful cost optimisation available to any production AI system is routing different types of requests to different model tiers based on complexity. GPT-4o mini and Claude Haiku cost 10-20x less than their full-size counterparts and produce perfectly acceptable quality for tasks that do not require complex reasoning.

The pattern: add a fast, cheap classifier as the first step in every request pipeline. It categorises the incoming task as Simple (FAQ, classification, template filling, short generation) or Complex (multi-step analysis, code generation, nuanced synthesis, creative writing). Simple tasks route to the cheap model. Complex tasks route to the expensive one.

In most production AI systems, 60-70% of requests are simple. Routing them to a model that costs 15x less reduces total API spend by 60-70% immediately — with no reduction in output quality for those requests, because they did not require the expensive model in the first place. This is not a trade-off between cost and quality. It is recognising that you were paying for capabilities you were not using.

Strategy 2: Semantic Caching — 25-40% Additional Reduction

Traditional caching matches exact strings — if the same request comes in twice, return the cached response. This has limited applicability because most user inputs are not identical. Semantic caching matches meaning rather than exact strings. If a user asks "What is your return policy?" and another user previously asked "How do I get a refund?", the semantic similarity score between these questions may be high enough (above your configured threshold, typically 0.90-0.95) to return the cached answer for the second query.

Redis with vector search support (Redis Stack) implements this natively. The setup involves storing the embedding of each query alongside its response. On each new query, compute the embedding, search for similar previous queries, and if the similarity score exceeds your threshold, return the cached response without an LLM call. Cache hit rate for FAQ-heavy systems typically reaches 25-40% within a few weeks of deployment. Each cache hit is effectively free.

Strategy 3: Context Window Management

Token costs are linear — every token in your context costs money on every call. Systematic context management can reduce tokens per call by 30-50% with no quality impact:

Audit every system prompt for redundancy. Most can be reduced by 30-50% without losing effectiveness — remove examples that are not needed for the current task, consolidate repeated instructions, eliminate hedging language that the model will include in outputs regardless. Limit conversation history to the last 8-10 turns rather than the full history. Limit RAG-retrieved context to the top 3 most relevant chunks rather than 5-10. For long documents that need to be included in a prompt, summarise them first with a cheap model and include the summary rather than the full text.

Strategy 4: Batch Processing

Both OpenAI and Anthropic offer batch APIs that process requests at 50% of standard pricing, with responses delivered within 24 hours. For any non-real-time workload — nightly reports, bulk data enrichment, scheduled content generation, offline analysis, weekly summarisation — batch processing halves your API costs automatically.

The practical implementation: identify which of your workflows are genuinely time-sensitive (need a response in seconds) and which are not (results needed today, not necessarily in the next 10 seconds). Separate these two categories. Keep real-time workflows on the standard API. Move non-real-time workflows to the batch API. For most organisations, 30-50% of LLM calls are non-real-time and qualify for batch processing.