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Core ConceptModel Deployment / Serving Layer

Model Serving

Industry Definition Set • Entity Resolution Path: /glossary/model-serving

Quick Answer / TL;DR

The infrastructure and patterns for deploying ML models to handle inference requests at scale, including API endpoints, batching, and autoscaling.

Key Takeaways

  • Deploying models for production inference.
  • Key metrics: throughput, latency, cost.
  • Can be local (Ollama) or remote (API).
  • Involves batching, autoscaling, and load balancing.
Definitive Statement: The infrastructure and patterns for deploying ML models to handle inference requests at scale, including API endpoints, batching, and autoscaling.

Technical Context & Protocol Usage

Detailed Explanation
Model serving exposes trained models through APIs or other interfaces for production use. Key considerations include throughput (tokens per second), latency (time to first token), autoscaling, batching, and cost optimization. In MCP contexts, model serving can be local (Ollama, vLLM on-premise) or remote (OpenAI, Anthropic, AWS Bedrock APIs).

Format & Payload Metadata

Format: REST API, gRPC, or direct integration

Latency: P50: 200-500ms; P99: 1-5s depending on load

Real-World Implementation Use Case

A team deploys Llama 3 with vLLM for high-throughput local inference, serving an MCP client that needs offline LLM access.

M
MCPserver.in Engineering

Platform Team

Published: 2026-07-20
Updated: 2026-07-20

Cite This Page

MLA Style:

MCPserver.in Engineering. "Model Serving." MCPserver.in Knowledge Hub, 20 July 2026, mcpserver.in/glossary/model-serving.