LLM Cost Optimisation with Claude

We benchmark quality before and after every optimisation. A saving that quietly degrades output is not a saving, it is a deferred cost.

Claude is strongest at sustained reasoning over long documents, careful tool use, and a low rate of confident errors. For llm cost optimisation that matters because the failure modes of this kind of system tend to cluster exactly there.

The honest trade-off: for very high-volume classification or extraction, a smaller model is cheaper at indistinguishable quality. We say that up front because a stack chosen for fashion rather than fit becomes someone's migration project two years later. We start from the constraint, not the capability, what the system must never do, who signs off, and what happens when it is wrong.

You own the code, the models where they are open-weight, and the documentation to run it without us.