A cloud engineer analyzes interconnected cloud network diagrams that highlight multicloud networking for AI.
Multicloud networking just became much more concrete. When Amazon and Google announced a joint service to let enterprises create private, high-speed links between AWS and Google Cloud in minutes instead of weeks, they did more than ship a new checkbox in the console.
They quietly raised the bar for what a serious multicloud strategy looks like in the AI era, where a single outage can freeze revenue and stall models mid-flight.
For anyone betting their future on cloud and AI infrastructure, this move is a signal that resilience and interoperability now belong at the center of the architecture, not on the last slide of a strategy deck.
What Amazon and Google actually launched
At the core of this announcement is a jointly engineered multicloud networking solution that ties together AWS Interconnect multicloud with Google Cloud Cross Cloud Interconnect. In practice, it lets customers stand up dedicated, private links between AWS and Google Cloud through familiar consoles or APIs, instead of waiting weeks for cross-connects, carrier coordination, and careful manual routing work.
The service hides a lot of traditional pain. Physical connectivity, routing policies, and BGP configuration between providers move into a managed layer that customers consume as a service. Bandwidth starts in the low gigabit range and scales up to tens or potentially hundreds of gigabits, with redundant, encrypted paths that are designed to keep traffic flowing even if part of the underlying network fails.
Why this suddenly feels urgent
This launch lands in the shadow of high-profile outages. A recent AWS incident in October disrupted thousands of sites and apps such as Snapchat and Reddit, with estimates of business losses in the hundreds of millions of dollars. For many leaders, that turned “what if the cloud fails” from a hypothetical into an unwelcome live fire drill.
AI raises the stakes further. Training and serving modern models relies on continuous movement of large datasets across storage, GPUs, and user-facing services, often spread across regions and sometimes across providers. In that world, multicloud networking for AI is not cosmetic. It is about making sure critical traffic has a fast, private, predictable path, even when public internet routes are congested or one provider is having a bad day.
How the joint service works
At a technical level, the joint service creates a private fast lane between AWS and Google Cloud regions, built as an extension of each provider’s connectivity portfolio. Instead of enterprises stitching together their own links, VPNs, and carrier contracts, they use a managed workflow that feels closer to peering two virtual networks than building a physical backbone.
Traffic runs over dedicated physical links between edge routers, protected with MACsec encryption and automated key rotation. Routing uses pre-tested designs that provide multiple redundant paths across facilities and devices, which both reduces failure risk and simplifies troubleshooting for network teams.
For security and platform teams, that means less time wrestling with ad hoc cross-cloud routing, and more time deciding which workloads should actually use this capacity.
Why fierce rivals would cooperate
Competitive tension between AWS and Google Cloud has not gone away. What has changed is the reality that many large enterprises already run serious workloads in more than one cloud, sometimes because of regulation, sometimes because of acquisitions, and sometimes for very pragmatic reasons like pricing or regional coverage.
Those customers now expect the big providers to help them connect everything cleanly.
By collaborating on multicloud networking for AI and other critical workloads, both vendors accept that they will often share the same customer footprint. The bet is that making multicloud easier will attract more high-value workloads overall, and that each provider will still win its share on the strength of its platforms and services.
Publishing an open interconnect specification also positions them as leaders for any broader ecosystem of compatible offerings.
What changes for the multicloud strategy
For years, many “multicloud” strategies boiled down to spreading workloads across providers without truly connecting them. That brought complexity without real resilience, because failover paths were slow, manual, or untested.
With managed multicloud networking in place, enterprises can start designing systems that treat AWS and Google Cloud as different zones of a single extended fabric. That enables patterns such as:
- Active-active applications split across providers, with private links carrying state and health signals.
- Cross-cloud disaster recovery that fails over in minutes instead of days.
- AI pipelines where data lives and is prepared in one cloud, and model training or inference happens in another that offers better accelerators or tools.
In this view, the intercloud network is no longer a fragile escape route. It is a planned part of the design.
Vendor risk, regulation, and AI pressure
This joint move also speaks directly to growing concerns about vendor concentration risk. Regulators in several markets are examining whether customers can realistically adopt multicloud, or whether hidden frictions keep them tied to a single provider.
High-quality, private connectivity between clouds is part of the answer, because it reduces both technical and operational barriers to moving or duplicating services.
For regulated sectors such as finance or healthcare, the ability to demonstrate a tested path to fail over critical workloads from one cloud to another over dedicated links can become a powerful part of operational resilience narratives.
When those workloads include AI systems that sit close to core decision-making, the importance of predictable multicloud networking for AI only grows.
A new mental model for cloud and AI infrastructure
The most productive way to interpret this shift is as a change in mental model. Instead of picturing the cloud as three isolated skyscrapers, it starts to look more like a connected city grid. Each provider still has its own skyline and specialties, but the streets between them are no longer improvised for each journey.
For teams that care about resilience, vendor risk, and AI scale, that is a meaningful turning point. The infrastructure to support a serious multicloud strategy is finally maturing. The next step is architectural and cultural.
The organizations that move fastest will be the ones that treat multicloud networking for AI not as a backup route, but as a foundation decision baked into how they design, deploy, and govern their systems.
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