Model Serving
Infrastructure for deploying AI models to handle production inference requests.
Definition
Model serving is the infrastructure and practice of deploying trained AI models to handle inference requests in production environments. This includes managing model loading, request routing, scaling, latency optimization, and monitoring.
Effective model serving balances performance, cost, and reliability. Options range from managed API services to self-hosted solutions, with choices depending on scale, latency requirements, and control needs.
Why It Matters
Training a model is only half the challenge—serving it reliably at scale is equally critical. Model serving infrastructure determines whether AI applications are fast, reliable, and cost-effective.
For engineering teams, model serving decisions significantly impact operational costs and user experience.
Examples in Practice
A recommendation engine uses model serving infrastructure that handles 10,000 requests per second with 50ms latency targets.
A company migrates from API-based serving to self-hosted infrastructure, reducing costs by 70% at their scale.
Model serving automation enables deploying updated models without downtime through blue-green deployment strategies.