Smarter AI Spending: Controlling Token Costs Without Sacrificing Performance

Token consumption has become one of the most overlooked cost drivers in the technology budget. As AI gets embedded into enterprise workflows, spend scales quietly and compounds fast. Most teams reach for a single large language model and apply it to every use case. The result is predictable. Runaway cost, unpredictable scaling, and solutions no one can defend to finance or leadership.

Improving treats this as a measurement problem. You cannot control spend you have not instrumented, and you cannot defend a model choice you have not tested. Cheaper only counts when you can prove it still performs.

In this webinar, we will walk through how we bring AI spend under control without giving up quality. It starts with evaluation-driven prompt development, which sets an objective quality bar for every prompt and model so "good enough at a fraction of the cost" becomes a number you can show, not a hope. That bar is what makes multi-model architecture safe. Once you can measure quality, you can match the right model to the right task and route between lightweight, specialized, and frontier models, paying for capability only where the task demands it. We test and compare those strategies in a governed devbox with DLP and data controls built in, so cost optimization never puts sensitive data or compliance at risk.

Drawing on real client engagements, our team will show how this approach has measurably reduced token usage, brought AI spend under control, and given organizations the confidence to expand AI adoption.

What You'll Take Away

• How token usage drives AI cost and where most organizations overspend

• How evaluation-driven prompt development sets a quality bar that justifies a cheaper model with data, not guesswork

• A framework for matching models to tasks and routing between them based on complexity and user need

• Practical patterns for multi-model pipelines that cut token consumption without degrading quality

• How to test cost strategies in a governed devbox with DLP and data controls in place

• Real-world examples of cost reduction and performance gains from Improving client engagements

• How to build executive-level visibility and governance into your AI spend