Model context protocol overview: An MCP Solution on AWS Compute and Storage

Our earlier post on Revolutionizing Finance with AI: Intro to the Model Context Protocol (MCP) explores how MCP offers a secure, standardized way for AI systems to interact with sensitive financial data, solving long-standing integration and governance challenges. When paired with the compute and storage capabilities outlined in this blog, especially on scalable platforms like AWS ECS and DynamoDB, MCP enables financial institutions to unlock intelligent, compliant, and highly efficient AI-driven solutions.

Here’s the real kicker: MCP is an open, integrated MCP—and when built on AWS, it unlocks a modular, scalable world where AI agents, data sources, tools, and pre-configured infrastructure unite like an orchestra playing in perfect harmony.

Building a robust Managed Cloud Platform (MCP) solution on Amazon Web Services (AWS) requires careful planning and strategic selection of services. This guide explores how to architect an MCP, emphasizing compute and storage components, to create a scalable, efficient, and cost-effective platform. We'll delve into native AWS services, compare options, and share best practices for designing a resilient MCP.

Building a Robust MCP Architecture on AWS

The heart of a successful MCP lies in smart design—balancing compute and storage across a client server architecture that scales effortlessly. You need more than tech; you need a blueprint that hosts applications, connects systems, and powers intelligent services.

Constructing an MCP involves integrating various cloud services to deliver flexible, reliable, and scalable solutions. The architecture typically comprises a compute layer that handles application logic and processing, and a data layer responsible for storing and managing data. Achieving operational excellence entails selecting appropriate services, designing for high availability, and optimizing for performance and cost.

A well-architected MCP ensures that user requirements—such as high throughput, low latency, security, and cost efficiency—are met consistently. To do this, leveraging native AWS services is optimal, as they are designed for integration, scalability, and security.

AWS services first: Leveraging native components

AWS offers a suite of managed services that simplify architecture and reduce operational overhead. These native components include compute services like EC2, Lambda, ECS, and EKS, and storage services like S3, DynamoDB, RDS, and ElastiCache. Using cloud-native services allows architects to build flexible architectures that scale automatically, offer high availability, and integrate seamlessly.

The key is to understand the role of each service and how they can work together to serve both application autonomy and data management needs. The following sections explore core compute options and storage solutions in more detail.

Compute layer: Choosing the right MCP server hosting

This is your platform’s nervous system—the part that runs your AI model, logic, and processing. The AWS compute suite offers choices for every flavor of control and automation.

The compute layer is the backbone of any MCP architecture, providing the execution environment for applications and services. Choosing the right hosting option depends on application requirements such as scalability, control, latency, and operational complexity.

Key Options:

• Amazon EC2: Need custom setups or to integrate with an external system? EC2 is the go-to. It’s like having your virtual server room in the cloud—perfect for legacy systems or intensive apps needing fine-tuned environments. Virtual servers offer full control over the environment. Suitable for legacy applications, custom configurations, or workloads requiring specific server setups. Learn how compute, storage, and AI tools work together in a client-server setup. This helps MCP clients and new AI applications.
  

• Amazon ECS on Fargate: Imagine deploying your app without worrying about the plumbing. ECS on Fargate abstracts the infrastructure, allowing you to connect AI, scale effortlessly, and focus on building logic and features. It's like having DevOps magic built-in. Managed container orchestration that abstracts server management, offering scalability, simplicity, and flexibility for containerized applications.
  

• Amazon EKS (Kubernetes): Need advanced orchestration or model context protocol MCP features? EKS brings Kubernetes power with AWS simplicity. For those who dream in YAML and scale across clouds, this is your playground. Managed Kubernetes service that provides maximum control, portability, and customization, suitable for complex microservices architectures requiring advanced orchestration.Our Pick: For most use cases, ECS on Fargate delivers the sweet spot—simplicity, scalability, and seamless AWS integration. It’s ideal for MCP clients aiming to deploy fast without the hassle.
  

Considerations:

• Use EC2 if full control over the environment is necessary or if legacy applications cannot be containerized.

• Use Lambda for lightweight, event-driven tasks, such as processing API requests or small background jobs.

• Use ECS on Fargate if you prefer containers but don’t want to manage infrastructure.

• Use EKS if you need Kubernetes features, multi-cloud portability, or complex orchestration.
  

AWS Lambda: Serverless for lightweight tasks

AWS Lambda is a serverless computing service that allows developers to run code without managing servers. It is ideal for lightweight, stateless functions that respond to events such as API calls, database changes, or file uploads.

Benefits:

• Automatic Scaling: Lambda functions scale transparently based on demand.

• Cost Efficiency: You pay only for the actual compute time consumed.

• Reduced Operational Overhead: No infrastructure to manage; focus on code development.

Use Cases in MCP:

• Processing API requests

• Data transformation or validation

• Event-driven workflows

• Background jobs like image processing or report generation

However, Lambda has limitations on execution time and concurrency, making it unsuitable for long-running or stateful applications. Combining Lambda with other compute options creates a flexible, layered architecture.

Amazon ECS on Fargate: Container Orchestration for Scalability

Amazon Elastic Container Service (ECS) on Fargate offers managed container orchestration that simplifies deploying and scaling containerized applications. With Fargate, you don’t manage the underlying EC2 instances; AWS provisions and manages the server infrastructure.

Advantages:

• Serverless Container Hosting: No EC2 management needed.

• Scalability: Easily scale containers based on demand.

• Cost-effectiveness: Pay per task or service run-time.

• Integration: Seamless integration with other AWS services.

Use Cases:

• Microservices architectures

• Modern cloud-native applications

• Batch processing or scheduled jobs

• APIs and backend services

ECS on Fargate strikes a balance between control and simplicity, ideal for organizations seeking containerization without operational complexity.

Amazon EKS (Kubernetes): Maximum Control and Portability

Amazon Elastic Kubernetes Service (EKS) provides a managed Kubernetes environment, giving developers full control over container orchestration with the power of Kubernetes.

Benefits:

• Advanced Orchestration: Automatic deployment, scaling, and management of containerized applications using Kubernetes features.

• Portability: Kubernetes is a widely adopted open-source platform, enabling easier migration or hybrid deployments across different cloud providers or on-premises infrastructure.

• Customization: Fine-grained control over container scheduling, networking, and scaling policies.

Use Cases:

• Large-scale microservices architectures require complex orchestration and customization.

• Multi-cloud or hybrid cloud deployments.

• Applications with specific networking or storage requirements that benefit from Kubernetes' flexibility.

Considerations:

• Greater operational complexity compared to ECS on Fargate.

• Requires Kubernetes expertise for management and troubleshooting.

Overall, EKS is suitable for organizations needing granular control and portability, especially when already invested in Kubernetes or needing features supported by the Kubernetes ecosystem.

Our Choice: Amazon ECS with Fargate

For many organizations, especially those looking for simplicity and scalability without managing infrastructure, Amazon ECS with Fargate is an ideal choice. It offers:

• Managed serverless containers, removing the need to provision or manage servers.

• Ease of use, with familiar AWS integrations and simple deployment procedures.

• Cost efficiency, as you pay only for the compute resources you consume.

• Scalability, supporting fast growth and variable workloads.

• Flexibility allows deploying microservices, APIs, and background jobs within a unified platform.

While EKS provides more control, ECS on Fargate strikes an excellent balance between management overhead and capability. It enables developers to focus on application development rather than infrastructure management, making it a popular choice for many MCP architectures.

Data Layer: Selecting the Optimal Storage Services

Data storage is a critical part of any MCP framework. Choosing the right storage solutions ensures performance, scalability, and data integrity. AWS offers various managed storage services tailored for different data types and application needs.

Key Storage Services:

• Amazon DynamoDB: NoSQL, schema-flexible, high-volume, low-latency database designed for serverless, globally distributed applications.

• Amazon RDS (Aurora): Relational database service supporting SQL-based workloads, ideal for structured data and transactional operations.

• Amazon S3: Object storage service for unstructured data such as files, media, backups, and logs.

• (Optional) Amazon ElastiCache: In-memory caching layer supporting Redis or Memcached, used for low-latency data access and caching frequently accessed data.

Amazon DynamoDB: Schema-Flexible and High-Volume Data

DynamoDB is a fully managed NoSQL database designed for high throughput and low latency. Its flexible schema allows rapid iteration and dynamic data models, making it perfect for scalable applications.

Use Cases:

• Mobile apps require fast app data access.

• Gaming leaderboards and real-time analytics.

• Event logging and IoT data ingestion.

Benefits:

• Automatic scaling to handle millions of requests per second.

• Serverless operation with no server management.

• Global tables for multi-region replication, increasing data availability.

Limitations:

• Less suited for complex joins or multi-table relational data.

• Requires careful indexing for query optimization.

Amazon RDS (Aurora): Structured Data and Transactions

Amazon RDS, especially Aurora, is designed for relational data, supporting a familiar SQL interface, ACID transactions, and complex joins.

Use Cases:

• E-commerce systems.

• Financial applications.

• Enterprise business apps with complex schemas.

Benefits:

• High performance with serverless and autoscaling options.

• Managed backups, replication, and failover.

• Compatibility with MySQL and PostgreSQL.

Considerations:

• Slightly more operational overhead compared to DynamoDB, but provides relational data guarantees.

Amazon S3: Object Storage for Files and Unstructured Data

Amazon S3 is a fully managed object storage service designed for storing and retrieving any amount of unstructured data, such as files, media, logs, and backups. It offers virtually unlimited scalability, high durability, and seamless integration with AWS analytics and machine learning tools.

• Automatic scaling to handle millions of requests per second.

• Serverless operation with no server management.

• Global tables for multi-region replication, increasing data availability.

Limitations:

• Less suited for complex joins or multi-table relational data.

• Requires careful indexing for query optimization.

(Optional) Amazon ElastiCache: Low-Latency Caching

ElastiCache supports Redis and Memcached, enabling low-latency data retrieval for frequently accessed data and session management.

Use Cases:

• Session stores.

• Leaderboards and real-time data dashboards.

• Caching database query results to reduce load.

Benefits:

• In-memory fast access.

• Easy to integrate with other AWS services.
  

Data Strategy: A Combined Approach

Successful MCP architectures often employ a mix of storage solutions to optimize performance, cost, and scalability:

• Use DynamoDB for high-volume, low-latency, schema-flexible data like user profiles, session states, or real-time analytics.

• Use RDS (Aurora) for structured, relational data requiring complex queries, transactions, and consistency, such as order management or financial records.

• Use S3 for unstructured data storage like large media files, backups, static content, and data lakes.

• Use ElastiCache for caching frequently accessed data, sessions, or real-time leaderboards to reduce latency and database load.

  
  

Combining these services allows a flexible, scalable, and cost-effective architecture tailored to differing data access patterns.

Conclusion: A Scalable and Cost-Effective Architecture

To recap: Leveraging MCP on AWS means orchestrating a symphony of compute, storage, and automation using tools that are powerful, secure, and scalable.

From core MCP services like ECS and Lambda to integrating advanced protocols like JSON RPC 2.0, building a cloud-native, AI-powered system has never been more attainable. Whether you're deploying AI models, connecting to an external system, or offering scalable services to your MCP clients, the building blocks are all here.

Designing an MCP solution on AWS involves selecting the right mix of native services for compute and storage, balancing control, scalability, and operational complexity. ECS on Fargate provides an ideal combination of container orchestration without infrastructure management, allowing developers to focus on application logic. Complementing this with appropriate storage solutions like DynamoDB, RDS, and S3 ensures data is managed efficiently according to its nature and access needs.

A cloud-native architecture leveraging AWS services offers the benefits of automatic scaling, high availability, security, and cost efficiency. By thoughtfully integrating compute and storage components, organizations can build MCP solutions that support current demands and future growth, all while maintaining operational simplicity.

A good MCP on AWS uses modern, managed services. These services are designed for your application's needs. They provide a strong, scalable, and affordable platform for digital transformation. In summary, a good MCP on AWS uses modern, managed services. These services are designed for your application's needs. They provide a strong, scalable, and affordable platform for digital transformation. They offer a strong, scalable, and affordable platform for digital transformation. In short, a good MCP on AWS uses modern, managed services. 

Carlos Barroso

Head of AI

Teracloud