GCP to Azure Migration: Making the Cloud-to-Cloud Jump 

Moving between cloud platforms might seem like swapping one set of complexities for another. There is a bit of truth to that. But migrating from Google Cloud Platform to Microsoft Azure is a fairly common move.

Below, we’ll look at the differences between the platforms, and why you’d want to migrate.

We’ll also go through how to move your workloads from one cloud to another without breaking everything in the process.

While we can’t give you the exact instruction manual (that’s a bit much to ask of a blog post), we can give you an overview of what the transition might involve. From there, you can see if it’ll be worth doing or not.

Let’s walk through what a GCP to Azure migration actually involves.

Cloud-to-cloud migrations happen for different reasons than on-premise moves. You’re not chasing general cloud benefits – you already have those. Instead, it’s usually about strategic alignment or practical necessities. The main reasons are usually one of these:

Microsoft ecosystem integration is a big driver of many migrations. If your organisation runs on Microsoft 365, Teams, and Active Directory, having your infrastructure in Azure just makes sense. The integration is deeper, the identity management is simpler, and your Microsoft Enterprise Agreement probably makes the pricing attractive.

Enterprise relationships and support matter more than many technical teams like to admit. Azure’s enterprise support structure suits organisations that want a traditional vendor relationship. Microsoft’s enterprise DNA shows in how they handle contracts, things that need escalating, and strategic partnerships. Google Cloud’s Premium Support takes a more engineering‑driven, partly automated support model.

Geographical coverage can be a factor that sways decisions. Azure has regions in places GCP doesn’t, and sometimes that matters for compliance or performance reasons. If you need a presence in specific countries or have data residency requirements, Azure’s wide-reaching footprint might seal the deal. Azure has over 60 regions worldwide, compared to the 46 regions that GCP will have by 2026.

Skills availability is a practical concern that often tips the balance. Azure skills are more common in the enterprise market. Finding contractors, hiring permanent staff, or getting training is often easier for Azure than GCP. It’s not that GCP skills don’t exist – they’re just less common in many markets.

Cost consolidation makes sense when you’re already paying for Microsoft services. Enterprise Agreements can make Azure pricing competitive, especially when you factor in the overhead of managing multiple vendor relationships.

Before you migrate anything, you’ll need a decent grasp on how these platforms differ. They solve the same problems but often in quite different ways.

Resource organisation works differently. GCP uses projects as the primary organisational boundary, while Azure uses subscriptions and resource groups. This isn’t just a difference in terminology – it affects how you structure everything from billing to access control. A single GCP project might map to multiple Azure resource groups, or vice versa.

Identity and access management takes some mental adjustment. GCP’s service accounts and IAM bindings work differently from Azure’s managed identities and role assignments. Both achieve the same goals, but the implementation details matter when you’re migrating applications.

Networking concepts definitely need translation. GCP’s Virtual Private Cloud networks are global by default, while Azure Virtual Networks are regional. Subnets work differently. Load balancers have different features. It’s all networking, but the details will trip you up if you’re not prepared.

Billing and cost management follows different patterns. Both GCP and Azure now bill by the second for most compute services, though the specifics vary by service type. GCP’s sustained use discounts happen automatically, while Azure requires reserved instances or savings plans. How does that affect your actual pricing? Well, for teams with predictable workloads, Azure can end up cheaper—but if your workloads are bursty or experimental, GCP’s automatic model can feel simpler. Azure tends to reward larger, longer-term enterprise commitments; GCP favours agility and simplicity.

Security and compliance are broadly comparable across both platforms, but they take different routes to get there. Both encrypt data in transit and at rest, enforce strong identity controls, and meet major certifications like ISO 27001, SOC 2 and GDPR. Azure leans towards integration and visibility: its Entra ID and native tools such as Defender for Cloud and Sentinel make sense for organisations already in the Microsoft ecosystem. GCP, on the other hand, prioritises isolation and automation, with granular IAM controls, Binary Authorization and global security perimeters baked into the platform. In short: both are secure by design, but Azure fits enterprise governance better while GCP appeals to developer-led teams.

Most GCP services have Azure equivalents, but they’re not always one-to-one matches.

Compute services map reasonably well:

• Compute Engine → Azure Virtual Machines
• Google Kubernetes Engine → Azure Kubernetes Service
• Cloud Run → Azure Container Instances or Container Apps
• App Engine → Azure App Service
• Cloud Functions → Azure Functions

Storage services have clear parallels:

• Cloud Storage → Azure Blob Storage
• Filestore → Azure Files
• Persistent Disks → Managed Disks

Database services need careful consideration:

• Cloud SQL → Azure Database for MySQL/PostgreSQL
• Firestore → Cosmos DB (though the models differ)
• BigQuery → Azure Synapse Analytics (sometimes paired with Microsoft Fabric for full analytical capabilities)
• Cloud Spanner → Cosmos DB or Azure SQL depending on needs

Analytics and AI services require some rethinking:

• BigQuery → Synapse (not just a lift and shift)
• Dataflow → Azure Data Factory
• Vertex AI → Azure Machine Learning
• Pub/Sub → Service Bus or Event Hub

While services map conceptually, their actual features and implementations can be different. That Cloud Run application might work better as Container Apps than Container Instances. Your BigQuery workflows might need redesigning for Synapse.

Migrating between clouds needs just as much planning as coming from on-premise. Maybe more, since you can’t blame legacy infrastructure for any issues.

Start with assessment and discovery. Make an inventory of everything running in GCP. Not just the obvious compute instances, but all the services, storage buckets, databases, and networking configurations. Understand the dependencies between different services too.

Knowing where you are, you can then decide on how much of your infrastructure you’ll actually migrate (if not all of it). You can apply the 5 Rs framework here (even though we primarily think of it for on-premise migrations). Even for cloud‑to‑cloud migrations, it’s a useful way to evaluate how much transformation each workload needs:

• Rehost (lift and shift): Move your GCP VMs directly to Azure VMs with minimal changes. Quick but doesn’t take advantage of Azure-specific features.

• Replatform: Make small adjustments to make use of Azure services. Maybe move from GKE to AKS, or shift from Cloud SQL to Azure Database. You get some Azure benefits without major rework.

• Refactor: Completely rearchitect for Azure. Transform that containerised app to use Azure Container Apps, or rebuild using Azure-native services. Maximum benefit but needs more effort.

• Repurchase: Sometimes it’s smarter to switch to a SaaS solution. Why migrate a custom analytics platform when Power BI or Microsoft Fabric might do the job better?

• Retire: Be honest about what you actually need. That proof-of-concept that’s been running for two years? Maybe it’s time to let it go.

Our smart cloud migration guide explores these patterns in more detail if you want a deeper dive.

Hybrid approaches are increasingly common. Not everything needs to move at once, and some workloads might stay in GCP permanently. Azure Arc lets you manage GCP resources alongside Azure resources from a single control plane. For workloads that need to stay on-premise or in other clouds, Azure Stack HCI (part of Azure Local) provides Azure services that run anywhere. This hybrid model gives you flexibility during migration and beyond.

Run proof of concepts before committing to your approach. Pick a representative but non-critical workload: maybe a development environment or internal tool. Migration between clouds often reveals unexpected assumptions about how services work. So, it’s better to find these issues early with smaller practical trials.

Plan around data gravity: it matters more than you might expect. As we put it in our guide, it’s “the idea that large data sets naturally attract applications, services, and other data to them; much like how gravity pulls objects together in space.” That multi-terabyte BigQuery dataset isn’t moving quickly or cheaply. Plan your migration order around data dependencies. Sometimes it makes sense to migrate applications first and leave data connections crossing clouds temporarily.

The tooling ecosystem for cloud-to-cloud migration is more fragmented than for on-prem scenarios, though Azure Migrate continues to expand its capabilities. Here are some of the most useful Azure migration tools to help with your move:

Azure Migrate now integrates with various third-party replication plugins, making it more useful for cloud-to-cloud scenarios than before. While it started as an on-premise migration tool, it can help with discovery, assessment, and even orchestration of GCP workloads.

Third-party tools fill many gaps. Tools like Zerto, CloudEndure (now AWS Application Migration Service, but still useful), and various migration platforms can help move your workloads. Container-focused tools like Velero work well for Kubernetes migrations.

Infrastructure as Code might be the perfect migration tool for some circumstances. If you’re using Terraform, you might be able to refactor your configurations for Azure rather than migrating traditionally. This approach takes more upfront work but gives you a cleaner result.

Data transfer options vary by volume and urgency. For smaller datasets, direct transfer over the internet works fine. Larger datasets might benefit from Azure Data Box, even from another cloud provider. For ongoing synchronisation during migration, consider tools like Azure Data Factory or third-party replication tools.

If you’re moving from GCP to Azure, there’s a good chance you’re also thinking about moving from Google Workspace to Microsoft 365. It’s a natural pairing: why split your ecosystem between vendors?

The workspace migration is its own project, but it often happens alongside infrastructure moves. Email, documents, and calendar data need careful handling. User training becomes critical since everyone in the organisation feels this change, not just IT.

Microsoft provides migration tools that handle the basics – email, calendars, contacts, and Drive files can move to their Office 365 equivalents. But the devil’s in the details. Shared drives need reorganising for SharePoint, and Google Forms might need rebuilding in Microsoft Forms or Power Apps. Apps Script automations need recreating in Power Automate – and so on.

You’ll want to get the timing right here, too. Some organisations migrate workspace first to get users comfortable with Microsoft tools before the infrastructure moves. Others do it simultaneously for a clean break.

There’s no perfect answer, but coordinate the migrations carefully. Nothing frustrates users more than constantly changing tools and processes.

There are a few recurring issues that pop up in most cloud-to-cloud migrations. It’d be wise to prepare for these sorts of thing:

1. Service feature gaps cause the most headaches. That GCP service feature you rely on might not exist in the Azure equivalent. Or it might work differently enough to break your assumptions. Cloud Run’s concurrency model differs from Container Apps, for example. So make sure you plan for application changes.
2. Network architecture redesign is almost inevitable. GCP’s global VPCs don’t translate directly to Azure’s regional model. Your beautiful network design might need rethinking. Factor in time for rejiggling your network architecture and testing that everything works.
3. IAM translation gets complex quickly. Service account patterns from GCP don’t map cleanly to Azure managed identities. Permission models differ. You’ll likely need to redesign your identity and access patterns rather than trying to replicate them exactly.
4. Infrastructure as Code adjustments take more time than expected. Even if you’re using Terraform for both platforms, the providers are different. Resource definitions change. State files need managing carefully, too. Budget time for IaC refactoring if you’ve invested time into it.
5. Egress charges can sting if you’re not careful. Moving data out of GCP isn’t free. Plan your migration to minimise cross-cloud traffic during the transition. Although sometimes it’s worth accepting higher egress charges for a faster cutover.

As you’re probably aware by now, migrating from GCP to Azure is not a small project. But it’s far from impossible. The platforms solve similar problems in different ways, and a successful move just means understanding and adapting to those differences, rather than fighting them.

You need stakeholder buy-in, proper planning, and realistic timelines. The technical challenges are solvable – it’s usually the organisational and procedural aspects that cause delays. Give your team time to adjust and learn.

Have you thought about getting a helping hand? At Synextra, we’ve helped organisations move to Azure, helping with both the technical migration and the organisational change needed to make it all happen smoothly.

If you’re ready to find out how to make your move from GCP to Azure, contact us today.