What Is Model Serving?
Model serving is the process of deploying trained AI models into production systems where they can receive inputs and return predictions in real time. It encompasses the entire infrastructure stack needed to make a model accessible as a reliable service, including API endpoints, load balancing, hardware optimization, batching strategies, and monitoring. Effective model serving bridges the gap between data science experimentation and business value.
Modern serving frameworks like vLLM, TGI (Text Generation Inference), and TensorRT optimize model execution through techniques such as continuous batching, KV-cache management, PagedAttention, and hardware-specific kernel optimizations. These optimizations can improve throughput by 10-50x compared to naive serving approaches.
Key Considerations
Production model serving must address several critical dimensions: latency requirements (real-time vs batch), throughput (requests per second), availability (uptime guarantees), cost efficiency (GPU utilization), and scalability (handling demand spikes). Auto-scaling, canary deployments, and A/B testing capabilities are essential for enterprises managing multiple model versions.
Serving Architectures
Enterprise deployments typically choose between cloud-hosted inference APIs, self-managed GPU clusters, or hybrid approaches. Self-hosted serving provides full control over data privacy and costs but requires infrastructure expertise. Cloud inference APIs offer simplicity but create vendor dependencies and ongoing costs. Many organizations use tiered architectures, routing simpler queries to smaller self-hosted models and complex queries to more powerful cloud-based models to optimize the cost-performance balance.