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AWS, Microsoft Azure, and Google Cloud offer services that often solve similar problems, but their names are only a starting point—not proof that the services work the same way. Use comparison tables to find candidates, then compare scope, capacity, security, resilience, and management boundaries against your workload.
How to read a cross-cloud service map
Cloud-provider comparison tables are useful for discovery, not for declaring services interchangeable. Google describes its listings as “similar or comparable offerings,” while Microsoft warns that direct service-to-service mappings are not always clear and that some services exist on only one platform. Microsoft puts the underlying issue plainly: “Azure and Google Cloud built their capabilities independently over time, so they have significant implementation and design differences.” Google Cloud’s comparison was last updated December 3, 2024; treat it as a dated starting point and verify current documentation for your region and use case. Microsoft Learn’s guide for AWS professionals likewise frames mappings as comparisons, not guarantees of parity.
Start with the job you need done—such as running a VM, storing objects, or accelerating application traffic—then identify the features and operating conditions that matter. A familiar label can conceal differences in resource scope, security controls, capacity, quotas, or failover behavior.
Common service equivalents—and what to verify
| Capability | AWS | Azure | Google Cloud | What the names do not tell you |
|---|---|---|---|---|
| Virtual machines | Amazon EC2 | Azure Virtual Machines | Compute Engine | Compare actual CPU, memory, storage, processor architecture, and configuration. Instance-family labels do not establish a capacity match. |
| Object storage | Amazon S3 | Azure Blob Storage | Cloud Storage | Check authorization, lifecycle behavior, redundancy, retrieval, and failover availability. |
| Virtual networking | Amazon VPC | Azure Virtual Network (VNet) | VPC network | Network scope differs: Google Cloud VPC networks are global, while Azure VNets are regional. Map topology and address ranges before connecting networks. |
| Application delivery and global traffic | CloudFront or Global Accelerator, depending on the need | Front Door or cross-region Load Balancer, depending on the layer and scenario | Cloud CDN or other products, depending on the function | Separate CDN, HTTP/HTTPS delivery, TCP/UDP acceleration, DNS, and security requirements; a single “equivalent” may not cover them all. |
| Organization and resource hierarchy | Accounts and organizations | Management groups, subscriptions, and resource groups | Organizations, folders, and projects | These structures occupy different levels and serve different billing, quota, policy, and deployment functions. |
The table is illustrative, not exhaustive. AWS and Azure VM categories may be broadly similar, but Microsoft’s comparison notes that RAM, CPU, and storage capabilities differ. Compare the actual AWS and Azure configurations for the workload instead of translating a VM family name mechanically.
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Gotchas that can change a design
“Equivalent” may mean the same job, not the same implementation
Two products can address the same broad need while differing in feature set, architecture, or operational model. Define the required outcome first: for example, whether you need global HTTP delivery, layer-4 traffic acceleration, or both. Then confirm each candidate supports the required protocol, security controls, geographic coverage, and integrations. Some capabilities have no direct counterpart, and a single service on one platform may map to multiple choices on another.
VM categories do not normalize performance or capacity
“General purpose” or “compute optimized” is not a sizing specification. Compare the CPU architecture and count, memory, storage options, and any workload-specific requirements for the configurations you would actually deploy. Validate capacity and performance for your application rather than assuming that similarly named families will behave alike.
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Storage names hide access, retrieval, and failover differences
Object, block, and shared-file storage are distinct patterns, not three labels for the same thing. S3, Blob Storage, and Cloud Storage are object-storage examples; EBS and managed disks are block-storage examples; EFS, FSx, and Azure Files serve file-storage patterns. Select according to how the application reads, writes, shares, and recovers data.
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Check whether data is stored redundantly within a location or zone, copied to another region, and available during failover. Microsoft notes that Azure geo-redundant storage (GRS) and geo-zone-redundant storage (GZRS) keep secondary-region data inaccessible until a planned or unplanned failover occurs. A replication label alone does not tell you whether an application can read the secondary copy during an incident. Microsoft’s AWS-to-Azure service comparison discusses storage and access patterns; confirm the current behavior and configuration in the provider documentation for the specific service and account.
Network scope and traffic products do not line up one-to-one
Plan routes, IP ranges, DNS, and security controls before building cross-cloud connectivity. Microsoft’s networking comparison describes Google Cloud VPC networks as global and Azure VNets as regional. It also notes that AWS subnets reside in one availability zone, whereas Azure subnets can span multiple availability zones. Those scope differences affect address planning and topology; copying a network diagram without adapting it can create conflicts or connectivity gaps. Review Microsoft’s networking comparison alongside current provider guidance.
Traffic-management mappings depend on the layer and function. Azure’s guidance maps AWS Global Accelerator to different Azure choices depending on whether the scenario is layer-7 HTTP/HTTPS or layer-4 TCP/UDP. It also says VPC Lattice has no single Azure equivalent. The AWS-to-Azure service mapping is therefore a prompt to identify the exact requirement, not a recommendation to substitute one product name for another.
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Projects and subscriptions are only partly analogous
Azure organizes resources into resource groups, places those groups under subscriptions, and can group subscriptions under management groups. Google Cloud uses organizations, folders, and projects. A Google Cloud project is conceptually similar to an Azure subscription for some billing and quota concerns, but functionally resembles a resource group as a deployment container. The axis matters: a one-word mapping can put resources, policies, or billing boundaries in the wrong place. Microsoft’s guide for Google Cloud professionals explains the distinction.
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A practical comparison checklist
Before choosing a counterpart or planning a migration, compare the options against the workload rather than the service names:
- Function: What outcome and specific features does the application require?
- Scope: Is the resource regional, zonal, global, or tied to a particular organizational boundary?
- Capacity and performance: Which CPU, memory, storage, and network configuration meets the workload’s needs?
- Identity and security: How are users, services, network access, and temporary access authorized?
- Resilience: Where are copies stored, and when can the application reach them during failover?
- Governance and billing: Where do quotas, policies, resource organization, and charges apply?
- Migration and operations: What must change in deployment tooling, monitoring, application integration, and day-to-day procedures?
- Availability: Is the required service, tier, and configuration available in the intended region?
Confirm current status, regional availability, tiers, quotas, and pricing with each provider before committing to an architecture or migration. Comparable service names narrow the search; workload-specific configuration determines whether a candidate actually fits.
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