The sticker price of AI agents is the development cost. The real cost is operating them: every request consumes LLM tokens, every tool call consumes API credits, and every conversation consumes compute. For agents serving hundreds or thousands of users, these costs add up quickly. Here are the practical techniques that reduce operating costs 40-70% without sacrificing output quality.
Model Routing
Not every request needs the most powerful (and expensive) model. Implement model routing: route simple requests (classification, extraction) to a smaller, cheaper model, and complex requests (reasoning, planning, generation) to a larger model. The routing decision can be based on: task complexity, input length, required accuracy, or user tier. Our clients typically see 40-60% cost reduction from model routing alone, with no measurable quality degradation.
Response Caching
Many agent requests are repetitive: the same question asked in slightly different ways, the same classification applied to similar inputs. Implement semantic caching: store recent inputs and their outputs in a vector database, and for new requests, check if a semantically similar request has been handled before. If a cached response meets quality thresholds, return it instead of running the full agent pipeline. Cache hit rates of 20-40% are common, reducing inference costs proportionally.
Pro Tip
Semantic caching requires similarity thresholds — too low and you return incorrect cached results; too high and the cache never hits. Tune the threshold per use case.
Prompt Optimisation
Longer prompts cost more tokens. Optimise prompts: remove unnecessary instructions, use concise language, cache system prompts (they are the same for every request), and implement prompt compression for long conversations. Our prompt optimisation typically reduces token count 25-40% without changing behaviour. The savings compound: cheaper per request × same number of requests = proportional cost reduction.
Conclusion
Agent operating costs are controllable with the right techniques: model routing, semantic caching, and prompt optimisation. These three techniques typically reduce costs 40-70% without measurable quality degradation. Start with model routing — it has the highest impact and lowest implementation effort.
Key Takeaways
- Model routing: simple requests to cheap models, complex to expensive — 40-60% cost reduction
- Semantic caching: return cached results for similar requests — 20-40% cache hit rates
- Prompt optimisation: shorter prompts, cached system prompts, conversation compression — 25-40% token reduction
- Start with model routing — highest impact, lowest implementation effort
- These techniques compound — combined savings of 40-70% are typical