Local MCP Vs remote MCP: decision framework
The Model Context Protocol (MCP) is becoming a foundational technology for connecting AI models with tools and data sources. As organizations implement MCP servers, they face an important architectural decision: should they deploy locally or remotely?
At Stainless, we generate client SDKs in various languages for our customers' APIs. Recently, those same customers started asking us for something new: Model Context Protocol (MCP) servers that wrap their APIs and expose them as tools for large language models (LLMs).
This choice impacts security, performance, and how teams collaborate. Let's explore what MCP servers are and the key differences between local and remote deployments.
MCP servers act as bridges between AI models and external tools or APIs. They translate between the model's requests and the actual implementation of those tools. When a model wants to perform an action like "get weather data" or "create a database entry," the MCP server handles that communication.
Local MCP servers run on your own machine or private network. They have direct access to local resources like files and databases without sending data over the internet. This approach is similar to running a development server on your laptop.
Remote MCP servers run on external infrastructure accessed over a network. They can be hosted in cloud environments and serve multiple users or applications from a central location. This is comparable to a production API that many clients connect to.
The protocol originated with Anthropic's Claude but has evolved into a standard for model-to-tool interaction across various AI systems. It addresses limitations in traditional function-calling approaches by providing a consistent way for models to discover and use tools.
Key differences between local and remote MCP servers
The choice between local and remote MCP affects everything from data handling to performance. Here's how they compare:
Aspect | Local MCP | Remote MCP |
|---|---|---|
Hosting | Your machine or network | Cloud or external server |
Data flow | Stays within your environment | Transmitted over network |
Setup | Software installed locally | Deployed to remote infrastructure |
Access | Limited to local environment | Available to distributed users |
Control | Direct control of all components | Depends on hosting environment |
Security and data control
Local MCP servers keep sensitive data within your infrastructure. Nothing leaves your system unless specifically programmed to do so. This approach provides maximum control over how data is accessed and processed.
With remote MCP, data travels between your client and the server over a network. While encryption protects this data in transit, it does move between systems. Remote servers typically implement authentication layers to ensure only authorized users can access tools.
For handling credentials:
Local MCP: API keys usually stored in local environment variables or config files
Remote MCP: Credentials managed through secret management systems or server-side storage
Performance considerations
Local MCP servers avoid network latency since everything happens on the same machine. This results in faster responses, particularly for tools that access local resources like files or databases.
Remote servers introduce network delays but may offer more powerful infrastructure than a local machine. While a good internet connection minimizes this impact, operations that require multiple back-and-forth communications can feel slower.
Performance differences are most noticeable in:
Tools that process large files
Operations requiring many sequential steps
Applications sensitive to response time
Setup and maintenance
Setting up a local MCP server requires installing software on your machine and configuring it to access needed resources. This process varies by programming language and operating system but typically involves:
Installing dependencies
Setting up environment variables
Running the server locally
Configuring tool access
Remote MCP servers need to be deployed to hosting infrastructure like AWS, Azure, or Google Cloud. This involves:
Preparing the server code
Configuring network access
Setting up authentication
Deploying to the target environment
Maintenance responsibilities differ significantly. With local MCP, you handle all updates, monitoring, and troubleshooting yourself. Remote MCP can leverage cloud management tools but requires knowledge of deployment pipelines and infrastructure monitoring.
When to choose local MCP
Local MCP servers excel in specific situations where control, privacy, or simplicity are priorities.
Development and testing: During development, local MCP provides a fast feedback loop. You can quickly modify tools, test changes, and debug issues without remote deployment steps.
Sensitive data handling: When working with confidential information that shouldn't leave your system, local MCP keeps everything contained. This applies to:
Personal financial data
Health information
Proprietary business data
Information subject to strict compliance requirements
Offline operation: Local MCP works without internet access, making it suitable for environments with limited connectivity or air-gapped systems.
Simple personal tools: For individual use cases like accessing local files or running personal automation, local MCP avoids unnecessary complexity.
A developer at a financial institution might use local MCP to create tools that analyze customer transaction data without exposing that information to external systems. The MCP server runs on a secure internal network, accessing databases directly while keeping all processing within the organization's infrastructure.
When to choose remote MCP
Remote MCP servers shine in collaborative, scalable, and distributed scenarios.
Team collaboration: When multiple people need to use the same tools, remote MCP provides a central access point. Everyone connects to the same server rather than maintaining individual installations.
Production applications: Customer-facing applications benefit from the reliability and scalability of remote hosting. Cloud infrastructure offers high availability and can handle varying loads.
Resource-intensive operations: Some tools require more computing power than a local machine can provide. Remote servers can offer more CPU, memory, and specialized hardware.
Global access: Distributed teams or applications serving users in different regions need tools accessible from anywhere. Remote MCP enables this global reach.
A marketing team might use a remote MCP server to give their AI assistant access to customer data, content management, and analytics tools. Team members in different offices all connect to the same server, ensuring consistent access to up-to-date information and capabilities.
Hybrid approaches
Many organizations find that a hybrid approach works best. This involves using both local and remote MCP servers for different purposes.
Development to production flow: Developers build and test tools using local MCP, then deploy stable versions to a remote server for production use.
Tiered data access: Sensitive operations happen locally, while general-purpose tools run remotely. This keeps critical data secure while enabling collaboration on less sensitive functions.
Geographic distribution: Remote MCP servers in different regions serve local users, reducing latency while maintaining central management.
A software company might use this approach by having developers test new API integrations with local MCP servers. Once approved, these integrations deploy to a remote MCP server that the company's customers can access through their AI assistants.
Decision framework
To choose between local and remote MCP, consider these key factors:
Data sensitivity: How confidential is the data being processed?
Highly sensitive → Local MCP
Public or non-sensitive → Remote MCP
Collaboration needs: Who needs to access these tools?
Single user or small co-located team → Local MCP
Distributed team or multiple users → Remote MCP
Resource requirements: What computing power do the tools need?
Standard operations within local machine capabilities → Local MCP
Intensive processing or specialized hardware → Remote MCP
Connectivity: What network environment will this run in?
Limited, unreliable, or restricted connectivity → Local MCP
Stable internet access → Either approach works
Deployment expertise: What infrastructure skills are available?
Limited DevOps experience → Local MCP is simpler
Strong cloud expertise → Remote MCP leverages those skills
Implementation considerations
Whichever approach you choose, these practices will help ensure success:
For local MCP:
Use containerization (like Docker) to create consistent environments
Implement proper credential management rather than hardcoding secrets
Document setup steps clearly for team members
Consider version control for configuration files
For remote MCP:
Implement proper authentication and authorization
Set up monitoring and alerting for availability
Use infrastructure as code for repeatable deployments
Consider regional deployment for global users
Next steps and takeaways
The choice between local and remote MCP depends on your specific needs and constraints. Many teams start with local MCP for development and testing, then move to remote MCP as their tools mature and usage expands.
Key takeaways:
Local MCP provides maximum control and privacy but limits collaboration
Remote MCP enables team access and scalability but introduces network dependencies
Hybrid approaches combine both models to get the best of each
At Stainless, we've helped customers implement both local and remote MCP servers to expose their APIs as tools for AI models. Our platform generates high-quality MCP servers from OpenAPI specifications, making it easy to deploy either locally or remotely based on your needs.
Ready to implement your MCP strategy? Get started with Stainless to build high-quality MCP servers for your API.
FAQs about local and remote MCP
How do I secure a remote MCP server?
Remote MCP servers should use HTTPS, implement proper authentication, apply rate limiting, and follow the principle of least privilege for all tool operations.
Can I migrate from local to remote MCP later?
Yes, most MCP implementations can be migrated from local to remote with minimal changes to the core tool logic, though authentication and network configuration will need updates.
Do all LLM platforms support both local and remote MCP?
Support varies by platform - Claude Desktop works well with local MCP, while other platforms like Atlassian's Remote MCP Server integrate specifically with remote implementations.
