AI Cost Optimisation
Cutting AI API costs on live systems without reducing the quality your users experience.
AI API costs can grow quickly when an integration is used at production volume — especially if it was built for a prototype where cost wasn't a constraint. The good news is that most cost reduction comes from a small number of changes: caching, batching, model right-sizing, and prompt compression. None of these require architectural changes to your application.
We start with a usage audit: which endpoints are called most, at what token volumes, with what cache hit rate. From there the reduction path is usually clear — identical inputs being sent to the API repeatedly without caching is the most common finding, and it's fixed in a day, not a sprint.
Model right-sizing is the second most common lever: tasks that use GPT-4 or Claude Opus because it was the default at integration time often perform identically with a smaller model, at a fraction of the cost. We test before we switch, always against a labelled sample.
Cost reduction levers
Response Caching
Identical inputs served from cache — the fastest cost reduction for high-repetition queries.
Model Right-Sizing
Smaller models tested against a labelled sample before switching — only moved when quality holds.
Request Batching
Multiple inputs processed in one API call where latency allows.
Prompt Compression
Token counts reduced without losing the context the model needs to perform well.