Token cost optimisation specialists for your dedicated team.

Where token cost optimisation fits in a long-term product team.

Routing by task is usually the largest available saving. Most systems send every request to their most capable model, while a large share of those requests, such as classification, routing and simple extraction, are handled just as well by a model costing a fraction as much.

Prompt caching is the second lever and is frequently unused. Systems with a large fixed preamble pay for those tokens on every call, and providers offering caching reduce that substantially for no quality change. It is close to free money for anyone with a long system prompt.

What your dedicated team does with token cost optimisation.

Cost work needs quality measurement beside it, or it becomes quality reduction with a cost chart. A cheaper model that is adequate on most cases and poor on the ones that matter is not a saving.

Running both together is why cost work belongs with the evaluation harness, agreed as part of how the monthly fee is built.

Common token cost optimisation use cases.

• Requests routed by difficulty Simple tasks handled by a cheaper model, which is usually the largest saving available.
• Fixed preambles cached Repeated context billed once.
• Quality measured alongside cost Evaluation run with every reduction, so savings are not quality loss.

Adding token cost optimisation skills to your team.

Cost optimisation is assigned inside AI capacity alongside the evaluation harness, because a saving without a quality measure is not a saving.

At Azendo, token cost optimisation work sits with a dedicated AI engineering team. You can add token cost optimisation capacity as one dedicated specialist or as part of a wider remote team, managed by a service delivery manager from our offices in Thailand. Companies hire dedicated developers this way under one agreement and one monthly fee.