AI inference is the largest ongoing cost for AI products: every request consumes LLM tokens, every embedding consumes compute, and every retrieval consumes resources. For AI products serving thousands of users, inference costs can exceed infrastructure costs by 5-10x. The good news: inference costs are highly optimisable. The techniques in this article consistently reduce inference costs 40-70% without measurable quality degradation.
Technique 1: Model Routing
Not every request needs the most powerful model. Route simple requests (classification, extraction, summarisation) to smaller, cheaper models, and complex requests (reasoning, planning, generation) to larger models. 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. The key: build a routing classifier that is itself cheap and fast (<5ms latency, <1% of inference cost).
Technique 2: Semantic Caching
Many AI 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 inference 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.
Technique 3: 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.
Technique 4: Batch Inference
Process multiple requests in a single GPU forward pass. Batching 8-32 requests together uses GPU compute more efficiently than processing one at a time. The trade-off: slightly higher latency per request (waiting for the batch to fill), but significantly higher throughput per dollar. Batch inference is particularly effective for background processing (report generation, data enrichment, batch classification) where latency is less critical.
Conclusion
Inference costs are the largest ongoing cost for AI products, but they are highly optimisable. Model routing, semantic caching, prompt optimisation, and batch inference typically reduce costs 40-70%. Start with model routing — it has the highest impact and lowest implementation effort.
Key Takeaways
- Model routing: route simple tasks 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 — 25-40% token reduction
- Batch inference: process multiple requests per GPU pass — higher throughput per dollar
- Combined savings of 40-70% are typical without measurable quality degradation