Amazon Web Services and Google Cloud announced a cross-cloud interconnect partnership in late 2025 that became generally available in early 2026. The integration connects AWS Direct Connect with Google Cloud's Cross-Cloud Interconnect, providing encrypted-by-default private networking between the two platforms. Latency on the interconnect runs between 2-5ms depending on region pairing, with bandwidth options up to 100 Gbps.
Two years ago, this partnership would have been unthinkable. AWS and Google Cloud competed aggressively for every workload, every enterprise deal, every dollar of cloud spend. Vendor lock-in was not just a side effect -- it was a strategy. Proprietary services, incompatible APIs, and punitive egress fees all served to make leaving expensive and painful.
Something changed. And understanding what changed -- and what has not -- is critical for any team making infrastructure decisions in 2026.
What the Partnership Actually Includes
Specifics matter more than press releases. Here is what the AWS-Google Cloud interconnect partnership provides:
Private cross-cloud networking. Traffic between AWS VPCs and Google Cloud VPCs traverses dedicated interconnect infrastructure, not the public internet. This eliminates the latency variability and security exposure of internet-based connections.
Encryption by default. All traffic on the interconnect is encrypted using MACsec (IEEE 802.1AE) at the physical layer and IPsec at the network layer. There is no option to disable encryption. This is a meaningful improvement over the previous approach, where organizations had to configure their own VPN tunnels between clouds, often with inconsistent encryption standards.
Simplified network configuration. Previously, connecting AWS and Google Cloud required third-party network providers (Megaport, Equinix Fabric, PacketFabric) as intermediaries. The new partnership allows direct configuration through each cloud's native console, CLI, or API. What used to take 2-4 weeks of procurement and configuration can now be provisioned in hours.
Unified billing visibility. Cross-cloud interconnect costs appear in both cloud providers' billing dashboards, with reconciliation tools to avoid double-counting. This is less exciting than it sounds -- the billing reconciliation is basic, and most organizations will still need a third-party tool like CloudHealth or Apptio for meaningful multicloud cost analysis.
Availability in 14 metro regions at launch, with plans to expand to 25+ metros by the end of 2026. Current availability covers major US metros (Virginia, Oregon, Ohio), European hubs (Frankfurt, London, Amsterdam), and Asian markets (Tokyo, Singapore, Mumbai).
What the partnership does not include: unified identity management, cross-cloud service mesh, shared Kubernetes control planes, or interoperable managed services. You can move data between clouds more easily. You cannot run a single application transparently across both clouds. That distinction matters enormously.
Why Competitors Are Collaborating
The strategic reasoning is straightforward. Flexera's 2025 State of the Cloud report found that 84% of enterprise respondents use multiple cloud providers. Gartner estimates that by 2027, 90% of large organizations will have adopted a multicloud approach. The question is not whether enterprises use multiple clouds -- they already do. The question is whether cloud providers make that experience tolerable or continue to make it painful.
AWS holds approximately 31% of the global cloud infrastructure market. Google Cloud holds approximately 12%. Microsoft Azure holds approximately 24%. For AWS, the partnership reduces a friction point that was driving some customers to consolidate on Azure (which has had cross-cloud networking features for longer). For Google Cloud, it provides a compelling reason for AWS-primary customers to add Google services for specific workloads -- particularly AI/ML, where Google's Vertex AI and TPU infrastructure are genuinely differentiated.
Both companies calculated that the revenue gained from easier multicloud adoption exceeds the revenue lost from reduced lock-in. That calculation only works because the partnership is limited to networking. Neither company is making its proprietary services interoperable. You can move data between AWS S3 and Google Cloud Storage more easily, but you still cannot seamlessly migrate a Lambda function to Cloud Functions or an Aurora database to Cloud SQL. The lock-in remains at the application and service layer. The collaboration is at the network layer.
Azure is expected to join the interconnect partnership later in 2026. Microsoft has been publicly supportive, and technical discussions are reportedly underway. When all three major providers participate, the interconnect becomes an industry standard rather than a bilateral agreement. That is when multicloud networking transitions from "novel" to "expected."
What "Multicloud" Actually Means in Practice
Marketing materials present multicloud as a single unified cloud that happens to span multiple providers. The reality is messier. Here is what multicloud looks like in actual production environments:
Pattern 1: Best-of-Breed Services
The most common legitimate multicloud pattern. You use AWS for compute and storage because your team knows it well and the service breadth is unmatched. You use Google Cloud for AI/ML workloads because Vertex AI, BigQuery ML, and TPU access are superior for your specific model training needs. Data flows between the two clouds through the interconnect.
This pattern works when the services are genuinely differentiated and the data flow between clouds is well-defined. It fails when teams try to use "best of breed" as a justification for every service, creating a sprawl of cloud dependencies that no one fully understands.
Pattern 2: Compliance and Data Residency
Regulatory requirements sometimes mandate that certain data stays in specific jurisdictions or on specific infrastructure. A multinational company might use AWS in the US, Google Cloud in the EU (for GDPR compliance with a different data controller), and a local provider in countries with data sovereignty laws. Multicloud is not a choice here -- it is a compliance requirement.
Pattern 3: Resilience and Disaster Recovery
Running critical workloads across two cloud providers ensures survival if one provider experiences a significant outage. AWS's us-east-1 outages in 2017, 2020, and 2021 demonstrated that even the most reliable cloud provider can go down. Having a warm standby in Google Cloud (or vice versa) provides genuine resilience that multi-region within a single provider cannot.
The honest answer is that this pattern is expensive. Maintaining application deployments across two clouds, with synchronized data, tested failover procedures, and ongoing operational knowledge across both platforms, typically costs 40-60% more than a single-cloud deployment. Most organizations talk about cross-cloud DR but do not actually implement it.
Pattern 4: Avoiding Vendor Lock-In (The Theory)
In theory, multicloud prevents vendor lock-in by ensuring you can move workloads between providers. In practice, this only works if you architect for portability from the start -- using Kubernetes instead of proprietary container services, Terraform instead of CloudFormation/Deployment Manager, PostgreSQL instead of Aurora/Cloud Spanner, and so on.
The tradeoff is significant. Portable architectures sacrifice the performance, cost, and operational advantages of managed services. A team using standard PostgreSQL on VMs instead of Aurora Serverless is paying more for worse performance in exchange for theoretical portability. Whether that tradeoff is worthwhile depends on how likely you are to actually migrate and how much the managed service advantages are worth.
The Cost Reality
Multicloud is not automatically cheaper. In most configurations, it is more expensive. Here is where the costs come from:
Egress fees. Moving data out of any major cloud provider costs money. AWS charges $0.09/GB for data transfer to the internet (with volume discounts). The cross-cloud interconnect reduces these costs compared to internet-based transfer, but data movement between clouds is still a significant cost center for data-intensive workloads. A pipeline moving 10TB of data per day between AWS and Google Cloud will generate meaningful monthly bills.
Operational complexity. Every cloud provider has its own IAM model, networking model, monitoring tools, deployment pipelines, and operational procedures. Supporting two clouds means two sets of expertise, two sets of runbooks, two sets of security configurations. Our experience across client engagements suggests that multicloud operational overhead adds 25-40% to infrastructure team costs compared to single-cloud operations.
Tooling costs. Single-cloud operations can use the provider's native tools. Multicloud operations require abstraction layers: Terraform or Pulumi for infrastructure, Kubernetes for compute orchestration, Datadog or Grafana Cloud for observability, HashiCorp Vault for secrets management. These tools have licensing costs and their own operational overhead.
Reduced volume discounts. Cloud providers offer committed use discounts (AWS Reserved Instances/Savings Plans, Google CUDs) that reward concentration. Splitting workloads across providers means smaller commitments with each, resulting in higher per-unit costs.
Here is the comparison in table form:
| Factor | Single Cloud | Multicloud | Hybrid (Cloud + On-Prem) |
|---|---|---|---|
| Infrastructure cost | Lowest (volume discounts, managed services) | 20-40% higher (split discounts, egress fees) | Variable (capex + opex) |
| Operational complexity | Low | High (2+ platforms to manage) | High (different operational models) |
| Vendor lock-in risk | High | Low-medium | Low |
| Resilience | Medium (multi-region) | High (multi-provider) | Medium-high |
| Talent requirements | Single platform expertise | Multi-platform expertise (scarce, expensive) | Cloud + infrastructure expertise |
| Compliance flexibility | Limited by provider's regions | High (choose provider per jurisdiction) | Highest (full control of on-prem) |
| Time to deploy | Fast (one platform) | Slow (coordinate across platforms) | Slowest (procurement cycles) |
| Best for | Most workloads, startups, SMBs | Regulated enterprises, specific compliance needs | Data-sensitive industries, legacy integration |
The table makes the tradeoff clear. Multicloud provides genuine benefits for resilience and compliance flexibility, but those benefits come at a real cost in money and complexity. For the majority of organizations, single-cloud with multi-region deployment provides sufficient resilience at lower cost and complexity.
When Multicloud Genuinely Makes Sense
After working with organizations ranging from 10-person startups to Fortune 500 enterprises, our team has identified the scenarios where multicloud investment is justified:
Regulatory mandate. If regulations require data residency in jurisdictions where your primary cloud provider does not operate, or if different regulators prefer different providers, multicloud is not optional. Financial services firms in multiple jurisdictions frequently face this.
Genuine best-of-breed needs with large-scale data flow. If you are training large ML models on Google Cloud TPUs but serving predictions from AWS (because the rest of your stack is there), the cross-cloud interconnect makes this pattern viable and cost-effective. The key qualifier is "large-scale data flow" -- if you are moving small amounts of data, a VPN tunnel was already sufficient.
Board-level risk mitigation. Some organizations, particularly in financial services and healthcare, have board mandates to avoid single-vendor dependency for critical infrastructure. The business case is not about cost optimization -- it is about existential risk management. If your board has this mandate, multicloud is the correct architecture regardless of cost.
Acquisition integration. When a company running on AWS acquires a company running on Google Cloud, multicloud is the immediate reality. The cross-cloud interconnect makes integration faster and more secure than the previous approach of migrating everything to one provider (which typically takes 12-24 months and frequently goes over budget).
When Multicloud Is a Mistake
Equally important: when not to do it.
"We might need it someday." Architecting for multicloud portability when you have no concrete plan to use multiple clouds is premature abstraction. You pay the complexity tax now for a benefit you may never realize. Build on one cloud. Use managed services. If you need to migrate in the future, the migration will be painful regardless of how "portable" you made your architecture.
Cost optimization. The idea that you can save money by running each workload on whichever cloud is cheapest is theoretically appealing and practically disastrous. The operational overhead of managing workloads across providers, the egress costs of data movement, and the loss of volume discounts almost always exceed the per-service savings. We have seen this calculation done dozens of times. It has never worked out favorably.
Resume-driven architecture. We will say it plainly: some multicloud decisions are driven by engineers who want experience with multiple platforms rather than by genuine business needs. This is understandable from a career perspective but expensive from a business perspective. If the honest reason for multicloud is "our team wants to learn Google Cloud," hire a contractor for a proof of concept instead of re-architecting production infrastructure.
Practical Advice for Teams Evaluating Multicloud
If the AWS-Google Cloud interconnect has prompted your organization to reconsider its cloud strategy, here is our recommended approach:
Step 1: Audit Your Actual Needs
Before making any architectural decisions, document the specific business requirements that multicloud would address. "Avoiding vendor lock-in" is not specific enough. "Ensuring our European customer data is processed in an EU-based jurisdiction where our primary provider's data governance meets our DPO's requirements" is specific. If you cannot articulate a specific, concrete need, you probably do not need multicloud.
Step 2: Calculate the Real Cost
Model the total cost of ownership for multicloud versus single-cloud. Include infrastructure costs, egress fees, tooling costs, additional headcount or training, and the opportunity cost of engineers spending time on infrastructure abstraction instead of product features. Use a 3-year horizon. Be honest about the numbers.
Step 3: Start with Networking, Not Compute
If the analysis supports multicloud, the AWS-Google Cloud interconnect is the right starting point. Establish the network layer first. Move data between clouds. Do not attempt to run application workloads across clouds until the network layer is proven and your team has operational experience with cross-cloud data flow.
Step 4: Pick One Cloud as Primary
Successful multicloud architectures have a primary cloud and secondary clouds for specific purposes. Trying to split workloads 50/50 between providers maximizes complexity without maximizing any benefit. Pick a primary. Use secondary providers for the specific use cases that justify the additional complexity.
Step 5: Invest in Abstraction Selectively
Use Terraform for infrastructure-as-code across providers. Use Kubernetes for container orchestration if you are already invested in it. Use a cross-cloud observability platform. But do not abstract at every layer. Using a cloud provider's managed database (Aurora, Cloud SQL) is usually better than running self-managed PostgreSQL for portability. Abstract where the portability benefit is real. Use managed services where the operational benefit is real.
What Comes Next
Azure joining the interconnect partnership will be the inflection point. When all three major providers offer direct, encrypted, low-latency interconnection, multicloud networking becomes commoditized. The competitive differentiation will shift entirely to services, developer experience, and pricing.
That shift benefits customers. When switching costs decrease, providers must compete on value rather than lock-in. We expect to see more competitive pricing, better managed services, and more portable data formats as the major providers adjust to a world where keeping customers requires making them want to stay rather than making it painful to leave.
The AWS-Google Cloud partnership is a tactical move by both companies. But its strategic implications are significant. The cloud industry is maturing from "pick your vendor and commit" to "use what works best for each workload." That maturation has been promised for a decade. With this partnership, it is finally becoming practical.
Whether your organization should act on it today depends on whether you have a genuine multicloud use case or are reacting to marketing momentum. Know the difference. Build accordingly.
Evaluating multicloud architecture or planning a cloud migration? CODERCOPS has designed and implemented cloud infrastructure across AWS, Google Cloud, and Azure for startups and enterprise clients. Talk to our infrastructure team.
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