Model Context Protocol (MCP) servers are becoming more and more popular. And as adoption grows, an important architectural question starts popping up: Where should you actually run your MCP servers?
There are many options, like a hosted MCP service run by a 3rd party, self-hosted in your own infrastructure, or you can keep it “simple” and run everything locally. There isn’t any correct answer to this, but I wanted to go through this and look at the pros and cons.
In this article, I break down each option to evaluate and go through the advantages and trade-offs. But remember: everything depends on what stage you are in, your team, how critical the security is, etc.
If you need a practical next step after choosing a model, see our guide to accessing, adding, and managing MCP servers in Obot and our MCP Gateway overview.
Click here to check out Obot’s Hosted MCP Platform for free, or visit GitHub to deploy our open-source gateway on your own infrastructure.
Hosted MCP
When it comes to “Hosted MCP” (sometimes called managed MCP), it means the solution is hosted by a third party. A hosted MCP server is essentially a cloud-managed service: you connect to this service with your AI clients, like Claude or Cursor, over standard web protocols such as HTTP and Server-Sent Events, and the provider handles the rest. They handle and manage the infrastructure, so you don’t have to worry about this. This means that you don’t have to manage servers, you don’t have to patch vulnerabilities, and you don’t have to think about uptime**:** this is what you pay that third party to do for you.
Advantages
The biggest advantage of using hosted MCP servers is speed. You can get started right away, and you don’t have to worry about infrastructure or configurations. This option is especially attractive for smaller teams, prototyping, experimentation, or teams without a dedicated DevOps resource. Rapid prototyping and shorter time-to-market are some of the clearest advantages of going hosted early on.
Hosted solutions also reduce operational overhead. Scaling, monitoring, and version rollouts, including security patches, are handled for you, so centralized updates simplify maintenance on your end. If your goal is to validate an idea quickly, hosted MCP is often the fastest path, and for teams that don’t want to run their own servers at all, it’s close to zero infrastructure overhead.
Hosted MCPs makes particularly good sense when it is a first party offering from a SaaS provider you already use, or similar. For example, if GitHub hosts a GitHub MCP, DigitalOcean hosts an MCP server, or Linear hosts a Linear MCP, you are not really introducing a new party into your architecture, you’re using the official integration layer from the vendor itself. In those cases, hosted is often the cleanest and simplest option, and it gives you standardized interoperability with whichever AI agents or AI assistants your team already uses, without custom integration work per tool.
Tradeoffs
The tradeoff is control: your data flows through someone else’s infrastructure. Even with strong guarantees and contracts, some organizations are simply not comfortable with that.
If the hosted MCP is operated by a separate third party (not the SaaS vendor itself), you are introducing another actor into your data path. That is not necessarily wrong, but it should be a conscious decision when thinking about things like security and who data is shared with.
You also depend on the vendor’s roadmap, uptime, and pricing model. For internal tools, this may be fine. But for regulated industries, or security-sensitive environments, it might not be. There’s also a network latency cost to hosted MCP servers, since requests travel to remote infrastructure rather than staying local.
Self-Hosted MCP
Self-hosting means running MCP servers in your own cloud or datacenter, typically behind an MCP gateway you also run, which becomes the single entry point your AI clients connect to instead of talking to each server directly. You control the infrastructure, you manage deployments, and you define security boundaries. Most self-hosted setups run on Docker or Kubernetes, and many teams put a gateway in front of their MCP servers specifically to centralize credential management, identity access control, and security policies across every connected AI client.
Advantages
Self-hosting means running MCP servers in your own cloud or datacenter, typically behind an MCP gateway you also run, which becomes the single entry point your AI clients connect to instead of talking to each server directly. You control the infrastructure, you manage deployments, and you define security boundaries. Most self-hosted setups run on Docker or Kubernetes, and many teams put a gateway in front of their MCP servers specifically to centralize credential management, identity access control, and security policies across every connected AI client.
Tradeoffs
The biggest benefit of self-hosted MCP servers is ownership. You can decide where the data lives, the network access, and you can enforce strict compliance requirements, including support for frameworks like SOC 2 and GDPR, since self-hosting keeps data inside infrastructure you already control.
Self-hosting is often preferred when security and compliance are critical, when you need deep integration with your internal systems, and if you have a DevOps resource you can use. This also reduces external data processing concerns, since nothing needs to leave your own network boundary. This option also gives you a lot of architectural flexibility: you can design scaling, isolation, logging, and security policies exactly the way your organization needs.
Self-hosted MCP servers also work well as centralized endpoints for accessing internal data and systems. Instead of letting multiple AI tools integrate directly with backend servers, MCP becomes a controlled gateway, giving you complete audit trails that track every tool call and authentication event, which is useful both for security review and for general policy enforcement across different teams sharing the same infrastructure.
Local MCP
A local MCP runs directly on a developer’s machine. This means that there is no shared infrastructure, no centralized deployment, etc. Everything is just local tooling.
Advantages
You can do quick experiments this way. Spinning up a server is almost instant, and you can connect to tools without waiting for approvals or configurations from an admin. Local MCP is great for prototyping, tool development, personal workflows, and early experimentations. It removes almost all friction since it’s just locally on your computer, and it can genuinely achieve lower latency than a remote or hosted server, since there’s no network hop involved at all.
Local setups make the most sense when the MCP needs access to local resources. This can be resources like the local filesystem, file operations on developer machines, or other workstation-specific data. In those cases, running locally is not just convenient, it is necessary.
Tradeoffs
Local setups do not scale across teams, and the configurations for your team can become inconsistent. What works on one developer’s computer may not work for everyone else.
Security will also not be the same for all team members. Using local MCP servers can be powerful, and it certainly has its place. But it’s not recommended for an organizational strategy.
You can try to think that if you have a solution that requires access to your local filesystem, or local resources on your computer, go for local. Otherwise, it would be recommended to use a self-hosted or hosted solution.
What Most Teams Actually Do
In reality, many teams move through these stages over time. It’s typically a try and fail thing. They start locally to experiment, then move to hosted MCP servers for simplicity. And sometimes, as MCP becomes more critical, they adopt self-hosted infrastructure for control and security, often centralizing everything behind a single MCP gateway once they have more than a couple of servers or AI clients to manage.
The real decision isn’t about infrastructure preference, it’s about what bottleneck you’re trying to remove. If your bottleneck is speed, hosted or local wins. If your bottleneck is compliance or control, self-hosted wins.
Choosing the Right Approach
If you’re just getting started, I would suggest that you try to optimize for learning. The whole system of MCPs can become very complex, and you don’t want to introduce unnecessary complexity before it’s needed.
Things can always be adjusted down the road, so as MCP adoption becomes a more central part of your organization, try to revisit your architecture and see if and how things can be optimized.
Using MCP servers is meant to simplify how things are integrated, using a standardized protocol instead of one-off custom connections. That’s why the hosting strategies for your MCP servers should simplify your organization, not slow it down by adding more complexity.
Many teams end up with a hybrid model: local MCP for developer workflows, hosted MCP for fast SaaS integrations, and self-hosted MCP for sensitive internal systems. The key is having one place to connect and govern them, with centralized control over access, authentication, and audit trails regardless of where each individual server actually runs.
FAQ
What’s the difference between hosted MCP and self-hosted MCP?
Hosted MCP means a third party runs the server infrastructure for you; you just connect your AI clients to it. Self-hosted MCP means you run the server yourself, in your own cloud or datacenter, giving you full control over data, security policies, and compliance in exchange for the operational work of running it.
Is local MCP secure enough for a team?
Local MCP works well for individual experimentation, but it doesn’t give you consistent security or configuration across a team, since each developer’s machine is its own environment. For anything beyond personal workflows or prototyping, hosted or self-hosted options give you centralized control instead.
Where are MCP servers hosted?
MCP servers can run in three places: on a third party’s infrastructure (hosted), in your own cloud or datacenter behind a gateway (self-hosted), or directly on a developer’s machine (local). Which one is right depends mainly on how much control, compliance, and team-wide consistency you need.
Is managed MCP the same as hosted MCP?
Yes. “Managed MCP” and “hosted MCP” both describe the same setup: a third party runs and maintains the MCP server infrastructure for you, rather than you deploying and operating it yourself.
Can I switch from local or hosted MCP to self-hosted later?
Yes, and it’s common. Most teams start locally or with hosted MCP servers to experiment quickly, then move to self-hosted infrastructure once MCP usage becomes critical enough to need tighter control, audit trails, or compliance guarantees.
Obot MCP Gateway gives you secure, production-ready MCP hosting without the operational overhead, so your tools work everywhere, not just locally. Try our Obot MCP Gateway or get in touch with one of our experts to discuss how Obot can help securely manage and scale your organization’s MCP strategy.
