MCP: Model Control Plane

backend service that hosts and orchestrates AI models and the agent capabilities

the management layer that controls models and routing (model selection, versions, telemetry, access, policy, deployments)

• it’s about operational control, policy, orchestration and governance

Github's MCP:

• used by GitHub Copilot CLI
• hosts models, routes requests, and manages model selection and versioning
• runs multistep plans, tool integrations, and agent workflows
• enforces access control, data handling rules, and auditing
• lets organizations plug in custom models, tools, or connectors
• collects usage, logs, and performance metrics

Other Example Products:

• Seldon Core - Open-source ML deployment platform with a control plane for managing model serving
• Google Vertex AI - GCP’s unified ML platform with model deployment management
• Azure Machine Learning - Microsoft’s MLOps platform with model management capabilities

MCP: Model Context Protocol

a structured envelope for what you send to / receive from a model (messages, resources, provenance, controls)

• it’s about input/output data shape, provenance and runtime constraints

The terms “MCP” refer to two different concepts in AI and cloud/Kubernetes contexts:

Key Differences Summary: Model Control Plane vs Model Context Protocol