Ninety six percent less. Same output.
Cutting model spend from one hundred thousand to four thousand a year
A fleet of qualification agents was projected to burn a hundred thousand on managed APIs. We re orchestrated onto RunPod and AWS, swapped the high volume calls for CLI invocations, and dropped spend ninety six percent.
The client was running a fleet of agents through managed model APIs at production volume. Annual spend was projected at one hundred thousand and trending up on three different axes: per token price, per request overhead, and per model upgrade. Every one of those axes was outside the buyer's control. The CFO's question was simple and the answer was not.
We audited every agent in the fleet and categorized each one by latency tolerance, data sensitivity, and concurrency. The high volume, latency tolerant qualifiers were moved off managed APIs entirely. We wired them through a vendor CLI invoked from the application layer, with retries, caching, and warm process reuse. Long running agents went into RunPod GPU pods and AWS managed compute. Real time buyer facing agents stayed on managed APIs where the trade off was right.
Annual spend dropped from one hundred thousand to four thousand. Output volume held. The buyer is no longer exposed to per token price changes from a single vendor and the architecture survives any one model going away.