AWS and Google Cloud just made multicloud far less painful. Here’s what their new private cross-cloud network means for AI workloads, resilience, and team productivity.
Most companies don’t lose sleep over “cloud strategy.” They lose sleep over outages, slow data pipelines, and surprise cloud bills that blow up Q4.
That’s why the new joint multicloud networking service from AWS and Google Cloud is a bigger deal than it looks on the surface. Two direct competitors are effectively saying: “Your AI workloads span multiple clouds now. Let’s stop making that painful.”
For anyone building AI products, running data-heavy workloads, or trying to keep enterprise systems reliable, this move isn’t just infrastructure news. It directly affects how fast you can ship, how resilient your stack is, and ultimately how productive your teams can be.
This piece breaks down what AWS and Google actually launched, why it matters for AI, technology, work, and productivity, and how you can turn this into a competitive advantage instead of yet another “cool announcement” you ignore.
What AWS and Google Just Shipped – In Plain English
AWS and Google Cloud have launched a jointly engineered multicloud network service that gives you private, high-speed connectivity between the two clouds, provisioned in minutes instead of weeks.
Here’s the thing about multicloud networking: historically, it’s been the least automated part of an otherwise very modern stack. You had to:
- Coordinate physical cross-connects in data centers
- Configure routers, BGP sessions, and IP addressing plans
- Wait days or weeks for provisioning and testing
- Maintain custom scripts or appliances to keep it all running
The new service replaces most of that with managed, cloud-native connectivity:
- You request bandwidth from your AWS or Google Cloud console or API
- The system uses pre-built capacity pools between the two providers
- Traffic runs over private, dedicated links, not the public internet
- The underlying infrastructure is fully managed by AWS and Google
Technically, it’s built with quad-redundancy (multiple interconnect facilities and paths) and MACsec encryption between AWS and Google edge routers. Both sides continuously monitor the links, which is critical in a year when a single major AWS outage was estimated to cost US companies up to $650 million.
The intent, as Google Cloud’s Rob Enns put it, is to help customers move data and applications “with simplified global connectivity and enhanced operational effectiveness.” In other words: less wrestling with network plumbing, more focus on actual workloads.
Why This Matters Right Now For AI-Centric Workflows
The more your AI strategy matures, the more multicloud becomes a productivity issue – not a technical fad.
Most serious AI and analytics teams are already behaving like multicloud users, whether they planned to or not:
- Models trained on one cloud, deployed on another
- Data lake in one provider, CRM or ERP in a different one
- Teams buying SaaS tools that sit on different hyperscalers
When cross-cloud connectivity is slow or brittle, you pay the price in:
- Longer training and inference times because data has to be copied or synced
- Operational risk when an outage on one cloud stalls systems on another
- Team frustration as data engineers and MLOps folks babysit pipelines instead of improving them
A jointly engineered, monitored, private backbone between AWS and Google Cloud changes that dynamic:
- Throughput improves: Moving large training datasets or feature stores between clouds becomes a normal operation, not an all-weekend event.
- Latency drops: Hybrid architectures (e.g., data on GCP, inference endpoints on AWS) become more realistic.
- Reliability goes up: Quad-redundancy and shared monitoring reduce the odds that a single fiber cut takes out critical links.
Salesforce is already named as an early adopter, using the service to unify data for AI and analytics. Jim Ostrognai, SVP of Software Engineering, highlighted that integrating Salesforce Data 360 "requires robust, private connectivity" so customers can ground AI in trusted data wherever it lives.
That’s the pattern to watch: AI teams want to treat “where the data lives” as an implementation detail, not a permanent constraint.
What This Means For Your Cloud Strategy (If You Care About Productivity)
This service is a signal: multicloud is no longer about vendor hedging; it’s about matching the right workload to the right platform and keeping your people unblocked.
If you’re leading engineering, data, or IT, here’s how to think about it.
1. Stop pretending you’re single-cloud
Most organizations say, “We’re an AWS shop,” or “We’re on Google Cloud.” But look at your actual footprint:
- Your data warehouse might be on BigQuery.
- Your primary applications might run on AWS.
- Your security stack, observability tools, and AI services might cross both.
The reality is that your work and technology are already multicloud, even if your architecture diagrams haven’t caught up.
This new AWS–Google backbone makes it easier to admit that reality and design for it intentionally:
- Run latency-tolerant analytics where it’s cheapest or best supported.
- Keep APIs and user-facing workloads wherever your teams are fastest.
- Move datasets between clouds based on feature needs instead of brittle, one-off migrations.
2. Treat network automation as a force multiplier
One of the quiet productivity killers in enterprise IT is “waiting on the network.” Waiting for:
- A new VPN or Direct Connect circuit
- Firewall rule changes
- Cross-cloud route updates
By making bandwidth on-demand via API and console, AWS and Google are effectively turning cross-cloud links into software objects. That matters because you can now:
- Bake link provisioning into CI/CD pipelines
- Spin up temporary high-bandwidth paths for big model training runs
- Standardize patterns for new AI workloads instead of inventing custom network topologies every time
I’ve seen teams shave weeks off project timelines just by removing the “ticket to the network team” step. This service is one more step toward network as code, which is where serious productivity gains show up.
3. Use open APIs to avoid new lock-in
Both AWS and Google published an open API specification for the service architecture. That’s not altruism; it’s strategy. But you can still benefit.
Here’s why open APIs matter:
- Other clouds and network providers can adopt the same model
- Your automation doesn’t have to be tightly coupled to one vendor’s proprietary interface
- Over time, this can evolve into a de facto standard for multicloud connectivity
If you’re designing for the next 3–5 years (not just the next quarter), build your internal tooling against the open spec where possible. That keeps you flexible as Azure or other providers join similar initiatives.
Practical Use Cases: Turning Infrastructure Into Real Productivity
Good infrastructure is only valuable when it changes how people work day-to-day. Here are a few concrete ways this AWS–Google link can help your teams work smarter, not harder.
Use Case 1: AI training on one cloud, serving on another
A pattern I see often:
- Data science teams love Google’s AI and analytics stack
- Platform teams standardize on AWS for production services
With high-speed, private connectivity:
- You can train models in Google Cloud (using TPUs or Vertex AI, for example)
- Export the trained artifacts over the dedicated link
- Serve them on AWS close to your existing microservices, users, or compliance controls
Result: the data science team uses the tools they’re productive with, operations keep their preferred production environment, and you don’t have to over-architect fragile data pipelines.
Use Case 2: Centralized data for analytics, distributed operational systems
Many enterprises are pushing towards single views of the customer, finance, or operations, while their operational systems remain scattered.
With private multicloud networking:
- CRM data from one cloud
- Transaction data from another
- Third-party SaaS logs from multiple providers
…can all flow into a centralized analytics or data platform with predictable performance and security.
This is exactly the Salesforce Data 360 story: summarize customer context once, then feed it into AI systems and front-line applications regardless of which cloud they sit on.
Use Case 3: Building resilience against single-provider outages
Outages are no longer hypothetical. Between hyperscaler incidents and SaaS failures, the question is “how fast can you route around it?”
With managed cross-cloud connectivity:
- You can design active–active architectures across AWS and Google Cloud
- Keep warm standby services or read replicas in another cloud
- Fail over critical AI inference endpoints or customer-facing APIs with less drama
You still need solid architecture and testing, but the plumbing is no longer the main blocker.
How To Prepare Your Org To Actually Use This
Most teams won’t benefit from this service by default. They’ll benefit if they line up process, architecture, and ownership around it.
Here’s a practical checklist to bring to your next architecture or platform meeting.
1. Map your real multicloud footprint
Don’t start with what you intend. Start with what you already have:
- List major workloads and note which cloud they run on
- Identify where your data actually lives (data warehouses, lakes, object storage)
- Flag AI/ML pipelines that already cross clouds or vendors
You’ll almost certainly find surprise dependencies. Those are your highest-impact candidates for private connectivity.
2. Identify “choke points” where connectivity slows work
Ask teams directly:
- Where do you wait on data transfers today?
- Which AI or analytics jobs get rescheduled because of bandwidth or reliability?
- Where do you maintain fragile VPNs or manual copy jobs?
Translate those into target use cases for managed cross-cloud links: nightly syncs, training data refreshes, replication for DR, etc.
3. Standardize patterns, don’t build snowflakes
Once you decide to use the AWS–Google service:
- Define reference architectures for common patterns (training there, serving here; analytics here, operational apps there)
- Bake connectivity provisioning into Terraform modules or similar IaC
- Document the “blessed” way to move data so people stop building ad-hoc tunnels and scripts
This is where productivity gains compound. Every new project benefits from the same patterns instead of re-arguing connectivity from scratch.
4. Involve security early
Private, MACsec-encrypted, provider-managed links are a strong foundation, but they’re not a full security story.
Make sure you:
- Align on identity and access patterns across AWS and Google Cloud
- Standardize encryption at rest and in transit above the network layer
- Log and monitor cross-cloud traffic so your security team isn’t blind
If security signs off early, you avoid the classic “we built this great new pipeline, and now it’s stuck in review for three months” bottleneck.
Where This Fits In Your AI & Technology Roadmap
For our AI & Technology series, the theme is simple: use AI and modern infrastructure to work smarter, not harder.
This AWS–Google Cloud partnership is one of those enabling moves that quietly reshapes what’s possible:
- AI teams get faster access to the data and platforms they prefer.
- IT and network teams spend less time on low-level configuration.
- Leaders get more options to balance cost, performance, and resilience.
If you’re planning your 2026 roadmaps right now, this is a good moment to ask:
- Where are we constrained by where our data lives?
- Which AI or analytics initiatives are slowed down by cross-cloud friction?
- How much more productive could our teams be if they didn’t have to fight the network?
There’s a better way to approach multicloud than “pick one cloud and hope for the best.” This new service from AWS and Google Cloud is a strong nudge in that direction. Use it intentionally, and it becomes less about infrastructure for its own sake and more about faster experiments, more resilient systems, and AI that actually reaches production.