MCP Hosting: The Infrastructure Layer for AI Skills

MCP Hosting: The Infrastructure Layer for AI Skills

Every conversation about AI skills eventually focuses on the exciting parts -- the clever prompts, the tool integrations, the workflow automations that save hours. Nobody wants to talk about hosting. But hosting is where skills become real.

A skill that exists only as a file on your laptop works when your laptop is on, when the right processes are running, and when your network connection is stable. That is fine for personal use. It is completely inadequate for any scenario involving teams, production workflows, or always-on availability.

The Model Context Protocol has emerged as the standard interface between AI agents and external capabilities. MCP servers are the runtime layer that skills connect to. And MCP hosting -- the infrastructure that keeps those servers available, secure, and performant -- is the foundation that determines whether skill ecosystems can scale beyond individual developers.

Key Takeaways

• MCP hosting means running always-on servers that AI skills connect to for external capabilities like database access, API integrations, and tool execution
• Three deployment models exist -- local, self-hosted cloud, and managed hosting -- each with distinct tradeoffs in cost, control, and reliability
• Agent37's managed hosting at $3.99/month represents the emerging "hosting as a service" model for AI skills
• Security is the primary concern -- MCP servers have broad access to external systems, making authentication, network isolation, and access control critical
• Infrastructure maturity determines ecosystem scale -- skill ecosystems cannot grow beyond hobbyist use without reliable hosting

What MCP Hosting Actually Means

The Model Context Protocol defines a standard way for AI agents to communicate with external tools and services. An MCP server is a process that exposes capabilities -- reading files, querying databases, calling APIs, executing code -- through a standardized interface that any MCP-compatible agent can consume.

MCP hosting is running those servers reliably. That means:

Availability. The server needs to be running when the agent needs it. A local MCP server that crashes when your machine sleeps is not a production capability.

Authentication. MCP servers often have access to sensitive systems -- databases, cloud APIs, source control. The hosting layer must manage credentials securely.

Network access. Many MCP servers need to reach external services. The hosting environment must provide appropriate network connectivity while maintaining security boundaries.

Process management. MCP servers can crash, leak memory, or hang. The hosting layer must detect failures and restart processes.

This is standard infrastructure work. It is not novel. But it is essential, and the AI skills community has been slow to treat it with the seriousness it deserves.

The Three Hosting Models

Local Hosting

The default approach. MCP servers run on your development machine, typically managed by configuration files that your AI agent reads at startup.

How it works: You define MCP server configurations in JSON files (.mcp.json in Claude Code, workspace files in OpenClaw). When the agent starts, it launches the specified servers as local processes.

Advantages:

• Zero cost
• Full control over configuration
• No network latency
• Data stays on your machine

Limitations:

• Stops when your machine stops
• No team sharing without each person running their own servers
• Resource contention with your other applications
• No high availability

Local hosting is appropriate for individual developers during active work sessions. It is not appropriate for team workflows, CI/CD integrations, or any scenario requiring always-on availability.

Self-Hosted Cloud

Running MCP servers on your own cloud infrastructure -- VPS instances, Kubernetes clusters, or serverless functions.

How it works: You deploy MCP servers to cloud compute (AWS, GCP, Azure, DigitalOcean, etc.) and configure your agents to connect to remote endpoints instead of local processes.

Advantages:

• Always-on availability
• Team access through shared endpoints
• Scalable compute resources
• Integration with existing cloud infrastructure

Limitations:

• Requires DevOps expertise
• Ongoing maintenance burden (updates, security patches, monitoring)
• Cloud costs accumulate
• You own the security responsibility

Self-hosted cloud is the right choice for organizations with existing infrastructure teams and specific compliance requirements. It provides maximum control at the cost of maximum operational burden.

Managed Hosting

A third party runs and maintains MCP servers for you. This is the model that Agent37 has pioneered for the OpenClaw ecosystem.

How it works: You sign up for a managed service, configure your skills to connect to their hosted MCP endpoints, and they handle availability, security, updates, and scaling.

Advantages:

• No infrastructure management
• Professional security and monitoring
• Automatic updates and patches
• Predictable pricing

Limitations:

• Less control over server configuration
• Data passes through a third party
• Vendor dependency
• May not support custom MCP server implementations

Agent37's Approach

Agent37 offers managed OpenClaw hosting at $3.99 per month. This price point is significant because it positions MCP hosting as a consumer-grade service rather than an enterprise infrastructure purchase.

The Agent37 model includes:

The $3.99 price point is deliberately accessible. It reflects a bet that the value of MCP hosting comes from ecosystem adoption, not per-user revenue. More users running always-on OpenClaw instances means more skill consumption, more skill creation, and a healthier ecosystem.

Compare this with self-hosting costs:

For individual users and small teams, managed hosting wins on pure economics. For organizations with specific compliance, customization, or data residency requirements, self-hosting remains necessary.

Security Considerations

MCP hosting security deserves dedicated attention because the attack surface is significant. An MCP server typically has:

• Credential access to external services (databases, APIs, cloud providers)
• File system access to read and modify project files
• Network access to reach external endpoints
• Execution capability to run code and commands

A compromised MCP server is not just a data breach. It is a capability breach -- the attacker gains the same abilities the agent had.

Local hosting has the smallest attack surface but weakest isolation. Self-hosted cloud allows network segmentation and IAM policies but requires you to implement them. Managed hosting outsources security to the provider -- better if you lack expertise, worse if you need specific controls.

The Claude Code permission model offers a useful reference for how agent-level security can complement hosting-level security. Skills should operate under least-privilege principles regardless of where they are hosted.

Regardless of hosting model, implement: credential rotation (never hardcode API keys), network segmentation (restrict MCP servers to required endpoints only), audit logging (every tool invocation and API call), input validation (agent inputs originate from user goals and can be adversarial), and process isolation (each MCP server in its own container).

Infrastructure and Ecosystem Scale

The current state of MCP and skill infrastructure is roughly equivalent to web hosting circa 2005 -- it works, but it requires too much expertise. The maturation path is predictable: Phase 1 (current) is developer-managed infrastructure. Phase 2 (emerging) is managed services like Agent37. Phase 3 (future) is platform-integrated hosting where installing a skill automatically provisions the required infrastructure.

Phase 3 is where skill ecosystems become truly accessible and the addressable market expands by orders of magnitude.

The Hosting Stack for Skill Creators

If you are building skills for distribution, your hosting choices affect your users. A skill that requires complex MCP server setup will have lower adoption than a skill that works with standard hosting.

Design skills with hosting portability in mind:

• Use standard MCP protocols rather than custom communication channels
• Minimize external dependencies -- each dependency is an infrastructure requirement for your users
• Document infrastructure requirements clearly -- what ports, what network access, what credentials
• Test across hosting models -- verify your skill works locally, on cloud VPS, and with managed providers
• Provide Docker configurations for users who self-host

The goal is to make your skill's infrastructure requirements as lightweight as possible. The lighter the infrastructure footprint, the wider the potential user base.

Frequently Asked Questions

Do I need MCP hosting if I only use skills locally?

Not necessarily. If you only use skills during active work sessions on your own machine, local MCP servers are sufficient. Hosting becomes necessary when you need always-on availability, team sharing, or integration with CI/CD pipelines.

Is Agent37's $3.99/month pricing sustainable?

At that price point, Agent37 is likely betting on volume and ecosystem network effects rather than per-user margins. This is a common platform strategy -- subsidize infrastructure to grow the ecosystem, then monetize through premium tiers or marketplace transactions.

Can I run MCP servers on serverless platforms?

Yes, but with caveats. MCP servers that maintain persistent connections or long-running processes do not map well to serverless execution models. Stateless MCP servers that handle individual requests work well on Lambda, Cloud Functions, or similar platforms.

How do I choose between self-hosting and managed hosting?

Self-host if you have specific compliance requirements, need custom MCP server implementations, or have existing infrastructure teams. Use managed hosting if you want to minimize operational burden, lack DevOps expertise, or are starting with AI skills and want the fastest path to production.

What happens if my MCP hosting provider goes down?

This is the standard vendor dependency risk. Mitigate it by ensuring your skill configurations are portable -- you should be able to switch from managed to self-hosted (or between managed providers) without rewriting your skills. Avoid provider-specific extensions unless the lock-in is justified.

Infrastructure Makes Ecosystems Possible

MCP hosting is not glamorous. It does not make for compelling demo videos or viral social posts. But it is the layer that determines whether AI skill ecosystems remain a niche hobby or become a mainstream capability.

The pattern is clear from every previous platform transition: ecosystems scale when infrastructure becomes invisible. The mobile app ecosystem did not explode because apps got better. It exploded because the infrastructure -- app stores, cloud hosting, push notifications, payment processing -- became reliable and accessible.

AI skills are on the same trajectory. The skills are already impressive. The infrastructure needs to catch up.

Explore production-ready AI skills at aiskill.market/browse or submit your own skill to the marketplace.