A media company’s recommendation engine was running on infrastructure sized for its worst-case traffic spike, all day, every day — and cloud compute costs for AI inference had become one of the fastest-growing line items in the technology budget.
Cutting Model Inference Cost 40% with a Right-Sized Infrastructure Redesign
Service Provided
Capabilities Utilized
Infrastructure Optimization, Cost Engineering, Inference Architecture
Client challenge
The recommendation service ran on a fixed pool of high-end GPU instances provisioned for peak evening traffic, which meant the same expensive infrastructure sat mostly idle for the other 20 hours of the day. No one owned the cost question specifically — it had grown gradually enough that it took a dedicated review to notice how large it had become.
Our solution
We rearchitected the inference layer around autoscaling, request batching, and a tiered model strategy: a smaller, faster model handles the majority of low-stakes requests, with the full-size model reserved for cases where the lighter model’s confidence is low. Infrastructure now scales down automatically during off-peak hours instead of running at peak capacity around the clock.
We validated the tiered approach carefully against recommendation quality metrics before rollout, since the whole plan depended on proving the lighter model didn’t meaningfully hurt user engagement — it didn’t.
Results
Monthly inference infrastructure cost dropped 40% within the first billing cycle after rollout, with no measurable decline in recommendation click-through rate. The tiered model strategy alone accounted for roughly two-thirds of the savings, with autoscaling and batching contributing the remainder, and the finance team now has cost forecasting accuracy it previously lacked.
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