Google Cloud, AWS, and Azure all offer migration guidance and services, but they organize the work differently. Google Cloud’s Migration Center is an assessment and planning hub connected to workload-specific migration tools. AWS groups guidance around discovery, business-case analysis, application mobility, and data mobility. Azure frames migration as a five-stage journey and points users to Azure Migrate and workload-specific guidance. None is a universal winner: the right fit depends on your source environment, target architecture, modernization goals, data and cutover needs, and operating model.
This comparison reflects official provider documentation available as of October 7, 2026. It compares documented workload paths and planning frameworks—not performance, like-for-like pricing, or independent test results.
How the three providers organize modernization
| Provider | Documented framework | What it helps you compare | What the framework alone does not establish |
|---|---|---|---|
| Google Cloud | Migration Center supports cost estimation, asset discovery and assessment, dependency mapping, planning, and technical-fit recommendations. Google describes migration strategies including rehost, replatform, and refactor. | A central planning entry point plus separate tools for VM migration, containerization, databases, data transfer, and mainframe or application modernization. | Migration Center is not a single tool that performs every migration. Exact compatibility depends on the specific tool and workload. |
| AWS | AWS Prescriptive Guidance groups migration tooling into discovery and planning, business-case analysis, application mobility, and data mobility. | A staged view of planning, economics, and movement of applications and data, with guidance that spans rehosting, refactoring, and modernization. | The reviewed framework does not establish a verified one-to-one feature match for every Google Cloud or Azure product. |
| Azure | Microsoft’s Azure Migration and Modernization Hub describes five stages: Plan, Prepare, Execute, Evaluate, and Decommission. | A migration journey that includes preparation and operating controls as well as execution, with links to Azure Migrate and workload-specific scenarios. | The five stages are a journey framework, not a like-for-like feature or performance comparison with another provider’s individual tools. |
These are provider-described frameworks and services, not independent evaluations. Use the frameworks to organize discovery, then verify the specific tool path for each workload.
What Google Cloud’s tools cover
Assessment and migration planning
Migration Center is the main Google Cloud entry point for inventory, assessment, dependency mapping, cost estimation, and migration planning. Its recommendations can help identify technical fit and organize work into migration waves. Google describes three broad strategies: rehost (move with limited architectural change), replatform (change the platform while retaining much of the application), and refactor (make more substantial application changes).
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Virtual machines and containers
Migrate to Virtual Machines moves virtual machines from sources such as on-premises VMware and other cloud environments to Compute Engine. That is a VM migration path; by itself, it is not application refactoring.
Migrate to Containers converts VM-based workloads to containers for Google Kubernetes Engine (GKE), GKE Autopilot, GKE Enterprise, or Cloud Run. Documented source environments include VMware, AWS, Azure, and Compute Engine VMs. Containerization changes the deployment target, but does not necessarily redesign application architecture or remove application-level dependencies.
Rank #2
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- What You Get: Anker USB-C Hub (5-in-1, 4K HDMI), welcome guide, 18-month warranty, and our friendly customer service.
Databases and data movement
Database Migration Service supports documented source and destination combinations involving PostgreSQL, MySQL, SQL Server, and Oracle. Datastream provides change data capture and replication for supported database sources and destinations such as BigQuery and Cloud Storage. A database name alone is not enough to confirm a valid route: check the current engine, version, source, destination, and service-specific constraints before designing a migration or cutover.
Storage Transfer Service supports transfers from other cloud providers, online resources, and local data sources. Google also documents Transfer Appliance, a hardware-assisted option for large transfers, and recommends it for transfers exceeding 20 TB and up to 1 petabyte. That range is Google’s recommendation for this product in documentation accessed in 2026; it is not a general threshold for choosing a cloud migration method.
Rank #3
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- What You Get: Anker USB-C Hub (7-in-1), welcome guide, 18-month warranty, and our friendly customer service.
Mainframe and application modernization
Google’s catalog includes a Mainframe Assessment Tool, Dual Run, and Mainframe Connector. On October 5, 2026, Google announced Google Cloud Modernize, a portfolio bringing together Migration Center, Google Cloud VMware Engine, mainframe modernization, and an EKS-to-GKE migration agent. The announcement also described Modernization Hub, a new in-console experience for source-code analysis and dependency mapping across Java, .NET, and mainframe applications.
In that October 5 announcement, Google described the EKS-to-GKE migration agent as Public Preview. Preview status can change, so confirm its current status and suitability before planning a production migration.
Rank #4
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How AWS and Azure fit into the comparison
AWS: organize the work by migration concern
AWS Prescriptive Guidance groups its migration tooling around discovery and planning, business-case analysis, application mobility, and data mobility. AWS says the tools support rehosting, refactoring, and modernization. This makes the framework useful for structuring a migration program, but it does not, on its own, identify an exact AWS service equivalent for each Google Cloud tool described above.
Azure: include preparation and retirement
Azure’s Migration and Modernization Hub lays out Plan, Prepare, Execute, Evaluate, and Decommission. It directs readers to Azure Migrate and workload-specific guidance for scenarios involving on-premises systems, AWS, and Google Cloud. It also points to landing-zone, governance, and architecture guidance—important because moving a workload is only part of establishing how it will be secured and operated.
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Choose a path by workload, not by provider slogan
- Inventory the source. Record the hypervisor or cloud, operating system, database engine and version, application dependencies, data volume, and existing operational constraints. Use each provider’s assessment guidance to identify what is known and what still needs validation.
- Decide how much to change. For a move with minimal application changes, evaluate a rehost path. If the target runtime changes—for example, from VMs to containers—evaluate replatforming and confirm the application can run there. If the goal requires changing application design or dependencies, plan for refactoring rather than treating a VM move as modernization.
- Validate the precise source-to-target route. Check the relevant tool’s current support for the actual source, destination, engine or runtime version, and region. Google documents cross-cloud paths for certain tools, including containerization; that does not mean every workload or configuration is supported.
- Design data movement and cutover together. Compare transfer method, replication or change-data-capture needs, validation, acceptable data loss, and the cutover window. A bulk transfer and ongoing database replication address different parts of the problem; establish how they will fit the workload’s continuity requirements.
- Plan the destination operating model. Include identity and access, governance, compliance, landing zones, monitoring, support ownership, and ongoing operations. Azure’s hub explicitly links migration with landing-zone and governance guidance; apply equivalent operating questions when assessing any destination.
- Build a workload-specific economic case. Include compute and storage, software licensing, data transfer, migration effort, refactoring, and operations. The provider documentation reviewed here does not establish that Google Cloud, AWS, or Azure is generally the cheapest option.
What to verify before committing
- Compatibility: exact source and target versions, configurations, and regional availability for the selected service.
- Migration outcome: whether the plan is to move a VM, convert it to containers, migrate or replicate a database, transfer files, or transform application code.
- Continuity: how data is synchronized, validated, and cut over, and what downtime or data-loss limits the business will accept.
- Operational readiness: landing zones, governance, identity, observability, compliance, and the team responsible after migration.
- Evidence behind claims: provider product descriptions can explain intended scope and status, but they do not substitute for workload-specific testing or a comparable cost analysis.
Google’s October 2026 announcement also made product-specific claims about M4N-series memory per vCPU and potential software-licensing savings for Oracle and other core-licensed databases. Those are vendor claims for a specific instance and licensing use case, not independent benchmarks or evidence that a provider is cheaper overall; they are not a sound basis for a general comparison.
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