Azure Multicloud Interconnect for AWS A No-Nonsense Deep Dive into Hyperscaler-to-Hyperscaler Private Networking

The Multicloud Tax Nobody Talks About

For years, connecting an Azure workload to an AWS workload meant stitching together ExpressRoute, Direct Connect, a co-location cage in Equinix, BGP peering sessions, and a prayer. You'd provision circuits on both sides, coordinate with two different NOC teams, and then maintain a Frankenstein routing table that nobody on your team fully understood.

That's the multicloud tax: not the bandwidth cost, but the operational drag of maintaining cross-provider plumbing that has nothing to do with your actual product.

Azure Multicloud Interconnect (announced alongside AWS Interconnect-multicloud) is Microsoft and AWS finally admitting that the old way was, in AWS VP Robert Kennedy's own words, "clunky." The new model replaces the DIY assembly with a standardized Open API spec that both providers implement. You get MACsec encryption out of the box, four-nines availability SLA, and up to 100 Gbps per interconnect at GA.

This is not a marketing refresh of ExpressRoute. It's a different operational contract between two hyperscalers.

For teams building AI training pipelines that span clouds (data in Azure Data Lake, GPU clusters in AWS, or vice versa), this is the first time the network layer stops being the bottleneck in your architecture diagram.

What Actually Changes in Your Architecture

Let's be concrete. Before, a typical Azure↔AWS private path looked like this:

[Azure VNet] -- [ExpressRoute Gateway] -- [MSEE] -- [Carrier NNI] -- [Equinix Fabric] -- [AWS DX Location] -- [AWS DX Gateway] -- [AWS VPC]

Every hop is a ticket, a change window, and a vendor relationship. After Multicloud Interconnect, the mental model collapses to:

[Azure VNet] -- [Azure Multicloud Interconnect] == [AWS Interconnect-multicloud] -- [AWS VPC]

Key technical properties

The Azure Private Link integration is the sleeper feature. It means your AWS-side consumer can reach an Azure PaaS service (Storage, SQL, Key Vault) over a private endpoint without you having to expose a public IP or build a hub VNet just to broker the connection. That's a meaningful reduction in blast radius for security reviews.

A minimal Terraform sketch (conceptual)

# Azure side: provision the multicloud interconnect endpoint
resource "azurerm_multicloud_interconnect" "to_aws" {
  name                = "mci-to-aws-prod"
  resource_group_name = azurerm_resource_group.net.name
  location            = "eastus"
  bandwidth_gbps      = 100
  peer_provider       = "AWS"
  # MACsec is on by default; do not disable in prod
}

# AWS side: mirror resource via the Open API spec
resource "aws_interconnect_multicloud" "to_azure" {
  name           = "mci-to-azure-prod"
  bandwidth_gbps = 100
  peer_provider  = "AZURE"
  peer_id        = azurerm_multicloud_interconnect.to_aws.id
}

Note: resource names above are illustrative — check the current provider docs for exact schema, since this is a GA-day feature and naming conventions are still settling.

The important architectural shift is that you now have one control plane per cloud, not one control plane across clouds. You don't get a unified dashboard. You get a unified contract. That distinction matters when you're writing your runbooks.

If you want to see how a similar "standardize the interface, keep the internals separate" pattern plays out in a very different domain — recommendation retrieval — the write-up on unified model-based recommendation retrieval with SilverTorch is worth a read. Same design philosophy, different layer of the stack.

The Limits You Won't See in the Announcement

Before you rip out your ExpressRoute circuits, be honest about what this doesn't solve.

1. It's still two clouds. You get a fast pipe, not a merged IAM model. Azure Entra ID and AWS IAM remain separate universes. Cross-cloud identity federation is still your problem.

2. Egress costs are unchanged. AWS egress to Azure is still metered. The interconnect makes the path simpler, not the bill smaller. For AI training workloads that shuffle terabytes daily, run the math before you commit.

3. Single-region pairing at launch. The GA announcement emphasizes point-to-point interconnect. If you need a mesh across three regions and two clouds, you're composing multiple interconnects and you're back to doing your own topology design.

4. Open API ≠ open source. The spec is published, but the implementation is proprietary on both sides. "Open" here means "interoperable," not "inspectable."

5. Vendor lock-in shifts, it doesn't disappear. You're now betting on the Microsoft-AWS interoperability roadmap staying aligned. If one side deprioritizes the spec, you're holding a circuit that's technically up but strategically orphaned.

When this is the right call

• You have a genuinely cross-cloud AI or data pipeline with steady, high-volume traffic.
• Your security team has been blocking multicloud because of public-endpoint exposure.
• You're already committed to both hyperscalers for 3+ years.

When to wait

• Your cross-cloud traffic is bursty and low-volume (use public endpoints + Private Link, cheaper).
• You're still evaluating which cloud wins for your primary workload.
• You need multi-region mesh on day one.

For a deeper look at how "one interface, many implementations" architectures tend to evolve in production systems, the SilverTorch retrieval architecture breakdown is a useful parallel — the same trade-offs between abstraction and control show up there.

What to Actually Do Next

If you're an Azure-primary shop with a growing AWS footprint (or vice versa), the pragmatic sequence is:

1. Inventory your cross-cloud traffic. If it's under a few hundred GB/month, this is overkill. If it's TB/day, keep reading.
2. Prototype one interconnect between a non-prod Azure subscription and a non-prod AWS account. Validate the Open API provisioning flow end-to-end before you trust it.
3. Test Private Link integration specifically. This is the feature most likely to change your security posture, and the one most likely to have sharp edges at GA.
4. Model egress costs for 12 months at your projected growth. The interconnect doesn't change the meter.
5. Write the runbook for "what if AWS deprioritizes this." Have an ExpressRoute fallback documented, even if you never deploy it.

Where to go deeper

• Microsoft's own write-up on the launch: Introducing Azure Multicloud Interconnect for AWS — the primary source for this piece.
• Microsoft Learn module for hands-on provisioning.
• AWS's parallel announcement for the Interconnect-multicloud side.

The bigger story here isn't the 100 Gbps number. It's that two hyperscalers agreed on a shared API spec for a networking primitive. If that pattern holds — and Microsoft is explicitly signaling they want it to extend to carriers and metro networks — the next five years of multicloud architecture will look structurally different from the last five. Worth watching, worth prototyping, but not worth betting your whole network strategy on in month one.