AWS vs Azure MCP Servers: Which Should You Install?

If you're connecting an AI agent to cloud infrastructure, AWS and Azure both have an official, vendor-maintained MCP server — but they're built on almost opposite philosophies. One ships dozens of small, single-purpose servers; the other ships one server that does almost everything. Knowing which model you're actually installing changes how you set it up and what you should expect from it.

What's the difference between AWS MCP Servers and Azure MCP Server?

AWS Labs' collection is a monorepo of many independently versioned servers, each scoped to one AWS service or workflow — you install only the ones you need. Microsoft's Azure MCP Server is a single unified binary that already covers 40-plus Azure services out of the box, with no equivalent per-service install step. Neither approach is objectively better; they reflect how differently each company already ships developer tooling.

What does the AWS MCP Servers collection actually include?

The AWS MCP Servers repository, maintained by AWS Labs, is written in Python and licensed Apache-2.0 with roughly 9,000 GitHub stars. The collection is licensed under Apache-2.0, so its terms are clear for commercial use. Its README groups servers by function, including documentation, infrastructure as code, containers, serverless, and data, and each server is published separately, so a project only pulls in the dependencies it actually uses.

Which AWS Labs server should you start with?

The AWS Documentation MCP Server is the most common starting point: it lets an agent search and retrieve official AWS documentation, so it can check a service before using it. Add the AWS IaC MCP Server if you manage infrastructure as code, since its README includes CDK best-practices guidance.

What other AWS Labs servers are worth knowing about?

Beyond the documentation and IaC servers, the collection's README lists service-specific servers such as the Amazon EKS MCP Server for teams running Kubernetes on AWS (as distinct from the cloud-agnostic Kubernetes MCP Server covered below), the Amazon ECS MCP Server, and the Bedrock Knowledge Bases Retrieval MCP Server, which queries a Bedrock-managed knowledge base. Because each server is separate, you add only the ones a project uses.

What does the Azure MCP Server cover?

The Azure MCP Server, maintained directly by Microsoft at github.com/microsoft/mcp, reached general availability at version 1.0 and is written in C# under the MIT license. Instead of separate servers per service, it exposes tools across Azure Storage, Azure Cosmos DB, resource group management, and direct Azure CLI command execution, all through one install. It's also built to work alongside the GitHub Copilot for Azure extension in VS Code specifically, not just as a standalone MCP server.

Do you need GitHub Copilot for Azure to use it?

No — the Azure MCP Server works as a standard MCP server with any compatible client, including Claude Desktop and Claude Code. The Copilot for Azure extension is an additional, optional integration layer for VS Code specifically; it isn't a requirement for connecting the server to an agent through a standard mcp.json or claude_desktop_config.json entry.

What's the real setup-time difference between the two?

In practice, Azure MCP Server is a single config entry and you're done — one npx or binary install, pointed at a client that already has an authenticated Azure CLI session. AWS Labs' collection takes longer up front because you're choosing: install the documentation server plus the service-specific servers you need, and you've made several install decisions instead of one. That extra decision-making isn't overhead for its own sake, though — it's the tradeoff for not pulling in 50 servers' worth of permissions and dependencies when a project only ever touches three AWS services.

How do authentication and credentials differ between the two?

Azure MCP Server authenticates through your existing Azure CLI session — if az login already works in your terminal, the server inherits that session with no separate credential setup. AWS Labs' servers follow the standard AWS SDK credential chain (environment variables, shared credentials file, or an assumed IAM role), which means credential setup is really "however you already authenticate AWS SDK calls in that environment" rather than anything MCP-specific. Neither model is more secure by default; both inherit whatever access the underlying CLI or SDK session already has, which is exactly why read-only IAM roles and scoped Azure RBAC assignments matter more for an agent-connected session than for your own interactive terminal use.

What about GCP — is there an official Google Cloud MCP server?

Not one that's currently vetted and listed on DReview. Search demand for a GCP MCP server is real (a few hundred monthly searches in the US) but meaningfully smaller than AWS or Azure, and a listing only goes live here once it passes DReview's four-point review — a real maintained repo, an actual license, working install configs, and an accurate description. If you maintain or know of a production-ready Google Cloud MCP server, submitting it is the fastest way to close that gap.

Should you also look at Terraform, Kubernetes, or Docker MCP servers?

If your infrastructure spans more than one cloud, or you manage it as code rather than clicking through a console, the Terraform MCP Server (HashiCorp's official server for Terraform Registry and Cloud data) is cloud-agnostic by design — it works the same way regardless of which provider your .tf files target. If your workloads run in containers, the Kubernetes MCP Server manages cluster resources directly through your existing kubeconfig, and the Docker MCP Gateway runs other MCP servers as isolated containers instead of granting them direct host access — worth pairing with either cloud server if you're running untrusted or third-party MCP servers alongside your own.

Does it matter that these are vendor-maintained instead of community projects?

Yes, more than it would for most categories on this directory. AWS and Azure both change their APIs on a rolling basis — new services, deprecated parameters, renamed resources — and a community-maintained wrapper that falls behind doesn't just miss a feature, it produces tool calls that fail silently or target the wrong resource shape against your real account. Both servers covered here are maintained directly by the vendor's own team, not a third party reverse-engineering the API, which is the main reason they're the ones listed on DReview for this category rather than one of the several unofficial alternatives that exist for each cloud.

Which should you install first?

If your team is already deep in one provider, the answer is simple: install that provider's server and move on. If you're evaluating both, start with Azure MCP Server for the fastest single-install setup, or start with the AWS Documentation MCP Server plus one or two service-specific servers if you'd rather opt into capability incrementally instead of getting everything at once. Both are free, open-source, and officially maintained — there's no cost to trying both side by side on a real project before standardizing on one.

Every cloud and infrastructure server mentioned here — AWS, Azure, Terraform, Kubernetes, and Docker — is browsable together under the aws mcp server category alongside the rest of DReview's directory.

If your team already runs its own tooling on one cloud, that choice usually settles the question faster than any feature comparison. The cost of switching later is high, so the first install should match the platform your team will still be using in a year.