Ad tech · A global ad-tech platform
Ad tech: classifying every impression without a frontier-model bill
The situation
The platform scores and classifies ad opportunities as they arrive — brand-safety checks, content categorization, contextual matching — and generates copy variations for creative testing. Every one of those decisions is an LLM call, and in ad tech they arrive in a constant, unforgiving stream where latency budgets are measured in the blink of an eye and volumes never dip.
Running that stream end-to-end on a premium frontier model worked technically and failed commercially: the bill scaled with impressions, and most of the calls were routine classifications that a far smaller model answers just as well.
What moved to Run BiOS
The team moved the workload to Run BiOS and put BiOS Adaptive in front of it. Adaptive reads each request and routes it to the model that answers it best at the lowest cost — routine brand-safety and categorization calls land on small open models, and only the genuinely hard judgment calls escalate to a frontier model.
Nothing about the integration changed: one OpenAI-compatible endpoint, the same request shape, the same code path. What changed was which model answered — and what each answer cost.
The outcome
- AI spend reduced significantly — routine classification no longer pays frontier prices
- Throughput increased significantly under the same latency budget, because smaller models answer faster
- Frontier-model spend is now reserved for the decisions that actually need it
Before
Premium frontier model on every impression
With Run BiOS
Right-sized model per call, frontier only where it pays off
Illustrative, not measured.
Facing the same bill?
Estimate your workload on the rate card, or talk to us about the deployment pattern that fits.
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