AWS and Google Cloud just launched a jointly engineered multicloud network. Here’s what it really changes for AI, data, and enterprise IT—and how to use it well.
Most enterprises are already multicloud, even if they don’t admit it on the slide deck.
One vendor powers the data lake, another runs customer-facing apps, and a third hosts that “temporary” workload that’s now in year four. Meanwhile, AI teams are wiring models across all of it. The result: slow data paths, brittle VPNs, and 3 a.m. outages that no one can fully explain.
AWS and Google Cloud just made a move that directly targets that pain.
They’ve launched a jointly engineered multicloud networking service that gives private, high-speed connectivity between AWS and Google Cloud — provisioned from your existing consoles, in minutes, not weeks. Underneath the press release, this is a serious signal about where cloud, AI infrastructure, and enterprise IT are heading.
This matters because if you’re serious about AI, resilience, and cost control in 2026 planning, your network architecture is now as strategic as your choice of LLM.
In this post, you’ll get a clear view of what this AWS–Google Cloud partnership really does, why it’s happening now, and how to turn it into an advantage for your own multicloud strategy.
What AWS and Google Actually Announced
The new AWS–Google multicloud network service is a managed, private backbone between the two clouds with:
- Dedicated bandwidth from pre-built capacity pools
- Quad-redundant interconnects between facilities
- Standard MACsec encryption between AWS and Google edge routers
- Provisioning via each cloud’s console or APIs
The big idea: abstract away physical connectivity, addressing, and routing policies so your team stops babysitting cross-cloud VPNs and manual BGP configs.
Instead of:
- Ordering physical cross-connects
- Waiting weeks for provisioning
- Managing routers, tunnels, and complex failover
You click through a workflow or call an API in AWS or Google Cloud and get:
- Private connectivity
- Predictable bandwidth
- Managed redundancy and monitoring across both providers
Salesforce is already piloting the service to unify Data 360 with other workloads, especially for AI and analytics. Their use case is exactly where this new backbone shines: trusted, consolidated data for AI that lives across clouds.
The service is designed to give you “simplified global connectivity and enhanced operational effectiveness” across AWS and Google environments.
That sounds like marketing, but the direction is right: simplify the plumbing so you can focus on apps, data, and AI.
Why These Rivals Are Suddenly Playing Nice
This partnership isn’t about friendship. It’s about pressure.
Three forces are pushing AWS and Google Cloud into cooperation:
1. AI has made multicloud the default, not the exception
AI workloads are forcing enterprises to mix and match clouds:
- One provider’s GPUs or TPUs are better for training
- Another provider has the data gravity or compliance coverage
- A third has existing contracts or enterprise discounts
If moving data between those platforms is slow, unreliable, or public-internet only, AI projects stall. This new service is a direct answer: high-throughput, private links that keep models and data in sync across clouds.
2. Outages got too expensive to ignore
The October AWS outage was a wake-up call. Single-cloud concentration is now a board-level risk.
When one region blinks and:
- Customer portals go dark
- Payment flows fail
- Internal tools stall
…the cost isn’t theoretical anymore. Parametrix estimated that outage exposure could reach $650 million for US companies alone.
Enterprises want credible active-active or failover architectures across providers. That means:
- Fast DNS or traffic management
- Consistent security and identity
- And critically, network paths that don’t fall apart under load
The AWS–Google backbone, with quad-redundancy and joint monitoring, is meant to support those architectures without you building an ISP-grade network team in-house.
3. Customers are tired of vendor lock-in tactics
Most companies now push back when vendors quietly tax data movement or make interoperability painful.
Publishing an open API specification for this interconnect model is a strategic move. It:
- Signals “we’re serious about interoperability”
- Encourages other providers and carriers to adopt the same pattern
- Makes it easier for enterprises and partners to automate multicloud networking
Is it fully open in practice? Not yet. But it’s a clear shift away from the old “pick one cloud and stay there forever” playbook.
What This Changes for Multicloud Networking
Here’s the thing about multicloud networking: most organizations underestimate it until it bites them.
This new service changes three core aspects of how you design multicloud.
1. From hand-built tunnels to cloud-native constructs
Previously, cross-cloud meant:
- IPSec VPNs over the internet
- Direct Connect + Partner Interconnect type setups
- Manual coordination with carriers and data centers
That approach works, but it’s slow and fragile.
Now, you can treat connectivity as just another cloud resource:
- Defined in Terraform or your IaC tool
- Version-controlled with the rest of your infra
- Created and destroyed as environments spin up or down
This is how you start working smarter, not harder: infrastructure engineers focus on intent and policy; the clouds handle the wiring.
2. From “best effort” internet to predictable performance
If you’re running:
- Latency-sensitive microservices split across AWS and Google
- AI pipelines that shuttle large feature sets
- Hybrid data platforms consolidating analytics across clouds
Then jittery internet paths are your enemy.
Dedicated capacity pools and joint monitoring mean:
- More predictable throughput
- Better resilience to congestion
- A single architectural pattern you can design around
You still need proper load testing and performance SLOs, but you’re building on a more reliable base.
3. From hidden complexity to managed risk
Quad-redundant interconnects, MACsec, and continuous monitoring sound like implementation details, but they translate into fewer unknown failure modes.
Instead of:
- “Is it our VPN? The ISP? The router in that colo?”
You get a managed fabric with:
- Documented SLAs
- Clear escalation paths
- Shared responsibility between AWS and Google
That doesn’t remove your accountability, but it simplifies your risk model.
Practical Use Cases: How This Helps Real Teams
The value of this multicloud network shows up fast once you map it to concrete patterns.
1. Unified data layer for AI and analytics
Most AI projects struggle more with data plumbing than with model selection.
Common scenario:
- Transactional systems and operational data live in AWS
- Advanced analytics, BigQuery, or AI workloads live in Google Cloud
- Data engineers maintain brittle ETL or batch exports over public links
With a managed private backbone, you can:
- Run near-real-time replication between data platforms
- Centralize training datasets without saturating internet links
- Keep sensitive data off the public internet by design
The result: faster iteration on AI projects and fewer “data out of sync” surprises.
2. Cross-cloud resiliency for critical applications
If you’re building resilience against provider or regional outages, the usual pattern is:
- Active-active or active-passive deployments across at least two clouds
- Shared identity (OIDC/SAML), shared secrets management patterns
- Replicated databases or message queues
This service makes the network part much less painful:
- Health-checked, redundant paths between your stacks
- Lower RTO/RPO because data replication can be faster and more predictable
- A clearer story for auditors and execs on how you mitigate cloud concentration risk
3. Gradual cloud migrations instead of “big bang” cutovers
Replatforming from one provider to another is rarely a clean cut. It’s more like:
- 6–24 months of parallel environments
- Services gradually migrating
- Data stores running in both places for a while
A managed, high-speed link turns this into a controlled, phased migration:
- Keep stateful services in the source cloud temporarily
- Move stateless and edge services first
- Migrate data stores with continuous replication rather than long freeze windows
You cut downtime, reduce rollback risk, and keep your options open if plans change.
How to Prepare Your Organization to Use This Well
Just because AWS and Google simplified the network doesn’t mean multicloud magically becomes easy. You still need good architecture and governance.
Here’s a practical checklist to approach this intelligently, not reactively.
1. Clarify your multicloud strategy first
If your only reason for multicloud is “everyone else is doing it,” pause.
Decide which of these you actually care about:
- Resilience: Survive a regional or provider-level outage
- Capability: Use a specific AI, data, or managed service that only exists on one cloud
- Cost: Use price competition and rightsizing opportunities across providers
Your priority shapes how you’ll use this backbone. For example:
- Resilience focus → design for active-active or fast failover
- Capability focus → treat it as a high-speed data bridge for specialized services
2. Standardize identity, security, and networking patterns
You want consistency across clouds, not one-off snowflakes.
Aim for:
- A single identity source of truth (IdP) used across AWS and Google
- Standard guardrails (landing zones, org policies, SCPs, org policies in Google)
- A shared set of networking templates: CIDR plans, segmentation strategy, routing policies
Then, integrate the new backbone as a standard building block inside that framework rather than a side project.
3. Bake automation in from day one
If you implement this service with manual clicks, you’ll regret it later.
Use:
- Terraform, Pulumi, or your preferred IaC to define interconnects
- Pipelines to provision, test, and tear down environments
- Policy-as-code to control who can create or modify cross-cloud links
This keeps your environment auditable and reduces the “only one engineer understands this” risk.
4. Watch costs and data egress patterns closely
Private and high-speed doesn’t mean free.
As you adopt cross-cloud connectivity:
- Tag and track all cross-cloud traffic where possible
- Establish budgets or thresholds for data transfer
- Regularly review which workloads truly need cross-cloud data paths
There’s a fine line between smart multicloud and an expensive, over-connected spider web.
What This Signals About the Next Phase of Cloud
The AWS–Google multicloud network service is more than another product launch. It’s a signal.
- Hyperscalers know AI is multisourced: models, accelerators, and data live everywhere.
- Customers are pushing for open, interoperable infrastructure, not walled gardens.
- Network reliability is now a first-class requirement for AI and digital operations, not an afterthought.
If you’re leading architecture, security, or data teams, this is the right moment to:
- Revisit your multicloud strategy for 2026
- Map which AI and data workloads genuinely benefit from cross-cloud connectivity
- Identify where managed multicloud networking could replace brittle, homegrown setups
There’s a better way to approach multicloud than duct-taping VPNs and hoping they hold during peak traffic. This new backbone from AWS and Google is one of the clearer signs that the clouds finally agree.
The question for your organization is simple: will you keep fighting the plumbing, or start designing a network that matches the ambition of your AI and digital roadmap?