Running MCP On-Premise
Running MCP on-premise means self-hosting your Model Context Protocol infrastructure — the gateway and the upstream MCP servers — inside your own network or private cloud, so prompts, context, tool arguments and results never leave your perimeter. It is the alternative to routing your AI tooling through a third-party SaaS control plane.
Why on-premise matters for MCP
MCP sits at a uniquely sensitive point. Tool arguments and resource contents flow through it, and those often contain exactly the data an organization is most careful about: customer records, credentials, source code, internal documents. Whoever operates the MCP control plane can, in principle, see that traffic. For many organizations, handing that to an outside SaaS vendor is a non-starter — so they keep the infrastructure inside their own walls.
Drivers for self-hosting
- Data residency — regulations may require that data stay in a specific country or region. Self-hosting puts you in control of exactly where every byte lives.
- No data egress — sensitive prompts and results are processed entirely on infrastructure you control, with nothing sent to an external provider.
- Air-gap friendliness — an on-prem gateway can run in a segmented or fully disconnected network, fronting internal-only MCP servers.
- Regulatory pressure — finance, healthcare, government and other regulated sectors often mandate self-hosting and full auditability. See MCP for Regulated Industries.
- Vendor independence — no dependency on a SaaS provider's uptime, pricing or data-handling changes.
What you run yourself
Two layers move in-house. First, the upstream MCP servers — the connectors to your databases, ticketing, document stores and internal APIs — run on your infrastructure. Second, an MCP gateway fronts them: a single governed endpoint that applies identity, authorization and auditing before any call reaches a server. The gateway is what makes a fleet of on-prem servers manageable rather than a sprawl of individual endpoints. See What is an MCP Gateway?.
SaaS vs. self-hosted: the trade-off
SaaS MCP tooling is fast to start and someone else handles operations — but your traffic transits their control plane, and you inherit their data-handling and residency posture. Self-hosting inverts that: you own the operational burden (deployment, upgrades, availability) in exchange for full control over data, identity integration and audit. For a laptop-scale experiment, SaaS is fine. For an organization moving regulated or proprietary data through AI tools, on-premise is usually the defensible choice.
Making on-prem practical
The historical objection to self-hosting is operational cost. Modern gateways answer this by shipping as a single container image that boots with one command, uses an embedded database by default, and scales to multiple replicas when you need high availability. That closes most of the gap: you get SaaS-like ease of setup with on-prem control. Kubernetes users can go further — see Deploying MCP in Kubernetes.
How Kravn fits
Kravn is designed to run entirely on your own infrastructure — Docker or Helm — with no data egress. It boots in one command with a first-run setup wizard, uses embedded SQLite by default or connects to PostgreSQL, MySQL/MariaDB or SQL Server, and integrates with your identity stack. Prompts, context and tool results stay inside your perimeter. See the installation guide and What is Kravn for the full picture.