Multicloud strategy: when using AWS and GCP is worth it

Is multicloud worth it?

Multicloud is worth it when something outside your engineering preferences forces it: a data residency rule, an acquired company on another provider, a board mandate against depending on one vendor, or a workload one cloud does clearly better with heavy data flow between them. Otherwise one cloud across several regions is cheaper and easier to run.

The question comes up more often now because the network part got easier. AWS Interconnect - multicloud went generally available with Google Cloud in April 2026 (AWS announcement), and AWS opened a preview with Microsoft Azure in August. A simpler network link is nice. It doesn’t change the rest of the decision, which is what this guide is about.

A private link between two clouds solves the networking problem and none of the others.

What the interconnect changes, and what it doesn’t

Before this product, a private link between AWS and Google Cloud meant ordering circuits through a network provider such as Equinix or Megaport, or running IPsec VPN tunnels over the public internet. Both worked. Both took real setup time and someone who understood BGP.

Google’s launch post describes the new model as a single “transport” resource you create in Google Cloud and accept in AWS, with the physical interconnects, VLAN attachments and Cloud Routers handled for you. According to the same post, links between the two clouds’ edge routers are MACsec-encrypted, connections are quad-redundant, and bandwidth was planned to scale up to 100 Gbps at general availability (Google Cloud blog). The spec behind it is published openly, which is how Azure and Oracle Cloud got added later.

What it does not give you is just as important. There is no shared identity model, no cross-cloud service mesh, no common Kubernetes control plane, and no interoperability between managed services. You can move data between S3 and Cloud Storage more easily. You still can’t lift a Lambda function into Cloud Run functions or point an app built on Aurora at Cloud SQL without rework. Lock-in lives at the service and application layer, and the interconnect works one layer below that.

Four multicloud patterns you’ll actually see

Vendor slides show multicloud as one big cloud that happens to span providers. Production setups look more like one of these.

Best-of-breed services

This is the most common legitimate pattern. Most of the stack runs on AWS because the team knows it, while a specific workload runs on Google Cloud because it is clearly better there. Model training on TPUs or analytics in BigQuery are the usual examples. Data flows between the two over a private link.

It works when the differentiation is real and the data flow is well defined. It goes wrong when “best of breed” becomes the justification for every service, and you end up with a sprawl of dependencies nobody fully understands. That is the same sprawl risk covered in the guide to multi-tenancy SaaS architecture patterns.

Compliance and data residency

Sometimes regulation decides for you. A company might need certain data processed in a jurisdiction where its main provider has no region, or a regulator might expect a particular setup. In that situation multicloud is a requirement, not a strategy.

Resilience and disaster recovery

Running critical workloads on two providers protects you from a provider-wide failure, which multi-region on one provider cannot fully do. AWS’s own post-event summaries list several major us-east-1 incidents, including the S3 disruption in February 2017, the Kinesis event in November 2020, a broad service event in December 2021 and the DynamoDB disruption in October 2025.

This is the most expensive pattern to do properly. You need deployments on both clouds, synchronized data, failover you actually test, and people who can operate both platforms at 2 AM. Plenty of organizations describe cross-cloud DR in their architecture docs and never build the tested version.

Portability against lock-in

In theory, multicloud keeps you free to move. In practice that only holds if you design for portability from day one: Kubernetes instead of proprietary container services, Terraform or OpenTofu instead of CloudFormation, plain PostgreSQL instead of Aurora or Spanner.

That design has a cost. Self-managed PostgreSQL on VMs gives up the operational work Aurora Serverless does for you, in exchange for an exit you may never use. Whether that’s a good trade depends on how likely a migration really is.

Where the extra cost comes from

Multicloud is rarely the cheaper option. The bill grows in four places.

Data transfer comes first. AWS includes 100 GB a month of free data transfer out to the internet, aggregated across services, and charges per GB beyond that on a tiered schedule (EC2 pricing). The interconnect has its own fee, which AWS says is based on the bandwidth you pick and the geographic scope of the connection, with one free local 500 Mbps connection per Region. A pipeline that moves terabytes a day between clouds will show up on the bill either way. Billing surprises in this area are common enough that the lessons from AWS’s Cost Explorer billing bug get their own post.

Operations are the second. Each provider has its own IAM model, networking model, monitoring and deployment tooling. Two clouds means two sets of runbooks, two security baselines and two kinds of expertise on the on-call rotation.

Tooling is the third. Native tools stop being enough, so you add abstraction: Terraform, OpenTofu, or Pulumi for infrastructure, Kubernetes for compute, a cross-cloud observability platform, and a secrets manager like Vault for credentials that now span both clouds. Each one has a license and its own upkeep.

Discounts are the last. AWS Savings Plans and Google committed-use discounts reward concentration. Splitting spend means smaller commitments with each provider and a higher unit price.

Factor	Single cloud	Multicloud	Hybrid (cloud + on-prem)
Infrastructure cost	Lowest (bigger discounts, managed services)	Higher (split discounts, data transfer fees)	Variable (capex plus opex)
Operational complexity	Low	High (two or more platforms)	High (different operating models)
Vendor lock-in risk	High	Low to medium	Low
Resilience	Medium (multi-region)	High (multi-provider)	Medium to high
Talent needs	One platform	Several platforms (harder to hire)	Cloud plus data center skills
Compliance flexibility	Limited to the provider’s regions	High (choose a provider per jurisdiction)	Highest (full control on-prem)
Time to deploy	Fast	Slower (coordination across platforms)	Slowest (procurement)
Best fit	Most workloads, startups, SMBs	Regulated enterprises, specific compliance needs	Data-sensitive industries, legacy systems

Multicloud wins on resilience and compliance flexibility and loses on almost everything else. For most organizations, one cloud deployed across regions gives enough resilience for far less effort.

When multicloud makes sense

A regulatory mandate is the clearest case. If you must keep data in a jurisdiction your primary provider doesn’t serve, or regulators in different markets expect different setups, you don’t get a choice. Financial services firms working across several countries hit this often.

Best-of-breed with heavy data flow is the second. Training models on Google Cloud TPUs while serving from AWS, where the rest of the stack lives, is a reasonable design, and a private interconnect makes it cleaner. The key words are “heavy data flow”. If you only move small amounts of data, a VPN tunnel was already fine.

Board-level risk policy is the third. Some boards, especially in finance and healthcare, require that critical systems not depend on one vendor. That’s a risk decision, not a cost one, and if the mandate exists the architecture follows it.

Acquisitions are the fourth. When an AWS company buys a Google Cloud company, you are multicloud on day one. A private link lets the two environments talk while you decide whether consolidation is worth the multi-month migration it usually turns into.

When multicloud is a mistake

“We might need it someday” is the classic. Designing for portability with no concrete plan to use a second cloud is premature abstraction. You pay the complexity tax now for a benefit that may never come, and a future migration will hurt regardless.

Chasing the cheapest price per workload is the other. Running each service wherever it costs least sounds clever. Once you add data transfer, staff time and lost discounts, the math almost never works out.

There is also resume-driven architecture, which I’d call out plainly. Sometimes the real reason is that the team wants Google Cloud experience. That’s a fair career goal and a poor reason to re-architect production. A time-boxed proof of concept, maybe with a contractor, gets the learning without the long-term bill.

How to evaluate multicloud for your team

If you don’t have someone in-house who can make this call, start with the guide to hiring a cloud architect for AWS and GCP work.

1. Write down the actual requirement

“Avoid vendor lock-in” is too vague to act on. “Process EU customer data in an EU jurisdiction under a provider whose data governance our DPO has approved” is specific. If you can’t write a sentence like the second one, you probably don’t need multicloud.

2. Model the full cost

Compare total cost of ownership over three years for both options. Include infrastructure, data transfer, tooling, extra headcount or training, and the product work engineers won’t do while they build abstraction layers. Use real quotes and the providers’ pricing pages, not rules of thumb.

3. Start with the network, not compute

If the numbers still favor multicloud, set up the private link and move data first. Run application workloads across clouds only after the data flows are stable and the team has operated them for a while.

4. Pick a primary cloud

Working multicloud setups have one primary provider and secondary ones for specific jobs. A 50/50 split doubles the complexity and doesn’t double any benefit.

5. Abstract selectively

Use Terraform or OpenTofu across providers. Use Kubernetes if you already run it. Use one observability platform for both. Don’t abstract every layer, though: a managed database such as Aurora or Cloud SQL is usually a better choice than self-managed PostgreSQL kept around for portability. Abstract where portability pays for itself and use managed services everywhere else.

The short version

Private networking between AWS, Google Cloud and now Azure is turning into a standard feature. That lowers one barrier and leaves the expensive ones, which are services, skills and operations, where they were. If you have a concrete reason from the list above, the tooling has never been better. If you don’t, stay on one cloud and spend the effort on your product.

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Evaluating multicloud architecture or planning a cloud migration? CODERCOPS designs and builds cloud infrastructure on AWS, Google Cloud, and Azure. Talk to our infrastructure team.