Cutting AI Costs Without Cutting Quality

AI costs scale with tokens processed, so the fastest way to cut spend is usually sending less text to the model rather than switching providers. Caching repeated queries, summarising long context before sending it, and routing simple requests to smaller models while reserving larger ones for complex tasks can cut costs substantially without hurting quality.

AI costs scale with the number of tokens processed, so the fastest way to cut spend is often to send less text to the model rather than switching to a cheaper one.

Caching repeated queries, summarising long documents before sending them as context, and routing simple requests to a smaller, cheaper model while reserving larger models for complex tasks can cut costs substantially.

We build cost monitoring into AI features from day one, since usage patterns in production often look very different from what teams expect during development.

A concrete example: a support assistant that pastes an entire product manual into every prompt, when only one section is actually relevant to a given question, is paying for thousands of unnecessary tokens on every single request, a cost that compounds fast at real usage volumes.

A common pitfall is optimising cost only by switching to a cheaper model across the board, which often degrades quality on the harder subset of requests that genuinely needed the more capable model, while barely saving money on the easy requests that were cheap either way.

Caching is particularly effective for common, repeated questions, since serving a cached answer costs nothing in model tokens, but it requires care to invalidate the cache correctly when underlying information changes.

We recommend reviewing token usage by request type after launch rather than only at the project planning stage, since the actual distribution of simple versus complex queries in production frequently differs from what was assumed during development.