Two very different phases, one infrastructure budget
A model's life has two distinct infrastructure phases, and they're often planned for as if they were one continuous problem. Training is a finite, intensive project with a clear endpoint. Serving is an ongoing operational cost that runs for as long as the model is in production, which for a successful product can be years. Treating both phases with the same infrastructure assumptions is one of the more common ways AI infrastructure budgets go sideways.
What changes once a model goes live
During training, the priority is throughput over a fixed, known workload run by a small team of engineers who control exactly how it's scheduled. Once a model is serving real traffic, the priority shifts to latency, concurrency, uptime, and cost per request, driven by usage patterns nobody fully controls. The infrastructure that was ideal for getting the model built is rarely the infrastructure best suited to running it well for the next several years at whatever scale the product actually reaches.
Serving costs accumulate in a way training costs don't
Training is a cost you pay once, or periodically when you retrain. Serving is a cost you pay continuously, scaling with usage, for the entire time a product is live. That difference alone changes the infrastructure math: small inefficiencies in serving compound over the life of a product in a way that similar inefficiencies during a one-time training run simply don't. It's a large part of why inference-specific optimization pays for itself differently than training optimization does, on a longer horizon but a much larger total base.
Planning for the phase you'll actually spend the most time in
Most organizations will spend far more total infrastructure budget on serving a successful model than they spent training it. Recognizing that early, and planning inference infrastructure as its own decision rather than an extension of whatever hardware was used for training, is one of the more overlooked ways to control AI infrastructure costs over a product's lifetime. It's also the reason inference deserves purpose-built architecture of its own, not hand-me-down hardware from the training phase.
Related reading: What Makes Transformer Inference Different from Training.