Serving Large Models Without Melting the Budget
Inference cost, not training cost, is what actually breaks most AI product budgets. Here is where the money really goes.
Most teams budget for the wrong phase of the model lifecycle. Training is a one-time, forecastable expense; inference is a recurring, traffic-shaped one, and it is the line item that quietly outgrows every projection once a feature actually gets used.
Where the cost actually accumulates
Batching efficiency, KV-cache memory pressure, and tail latency requirements interact in ways that are easy to underestimate during a proof of concept, when traffic is low and every request gets generous compute headroom.
- Under-batched traffic wastes GPU cycles on padding and idle time between requests.
- Long context windows multiply KV-cache memory faster than most teams model for.
- Aggressive latency SLOs force smaller batch sizes, trading throughput for responsiveness.
A practical next step
Before reaching for a bigger GPU budget, instrument request-level cost per token and segment it by traffic pattern. Most savings come from routing, not raw compute — cheaper models for simple requests, and reserving the expensive path for the traffic that actually needs it.