What Is Model Serving? Running a Trained Model in Production
A trained model sitting on disk isn't useful to anyone yet — model serving is the infrastructure that turns it into something applications can actually call.
From a trained file to a live system
Model serving is the infrastructure and software that takes a trained model and makes it available to handle real, live requests — loading it into memory, exposing it through an API, and managing the resources needed to actually run inference reliably at whatever scale is needed.
The problems serving infrastructure has to solve
Beyond simply running the model, serving infrastructure has to handle many simultaneous requests efficiently (often via inference batching), keep response times acceptable under real load, and scale capacity up or down as demand changes throughout the day.
Why this is a genuinely separate discipline
Training a model and serving it in production are different engineering problems with different priorities — training optimizes for eventual model quality over a long run, while serving optimizes for consistent low latency and cost efficiency on every single request, which is why teams often specialize in one or the other.
Frequently Asked Questions
What is model serving?
The infrastructure and software that makes a trained model available to handle real, live requests — loading it, exposing it via an API, and managing the resources needed to run inference reliably at scale.
Is model serving the same skill set as training a model?
Not really — training optimizes for eventual model quality, while serving optimizes for consistent low latency and cost efficiency under live traffic, which is why they're often handled by different specialized teams.