Azure, AWS, and GCP: A Multicloud Service Cheat Sheet

Multicloud architecture often comes down to translation: knowing which Azure service corresponds to the AWS or Google Cloud option you already understand. While each provider has unique strengths, most common architecture patterns rely on comparable building blocks for compute, storage, networking, identity, data, security, observability, and automation.

This cheat sheet gives cloud architects, engineers, and platform teams a fast way to compare equivalent services across Microsoft Azure, Amazon Web Services, and Google Cloud Platform. Use it as a practical reference when designing portable architectures, evaluating provider-specific options, migrating workloads, or communicating across teams that use different cloud ecosystems.

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Core Compute and Container Services

Compute is usually the first place teams look when comparing Azure, AWS, and Google Cloud because it defines how applications run: virtual machines, containers, serverless functions, batch jobs, and managed application platforms. The names differ, but the patterns are similar. Use virtual machines when you need OS-level control, managed Kubernetes for portable container orchestration, serverless containers for simpler deployment, and functions for short-lived event-driven workloads.

For traditional lift-and-shift migrations, Azure Virtual Machines, Amazon EC2, and Compute Engine are the closest equivalents. Each supports common instance families for general purpose, compute-optimized, memory-optimized, GPU, and storage-heavy workloads. Autoscaling is handled through Virtual Machine Scale Sets, EC2 Auto Scaling groups, and Managed Instance Groups. These services are best for applications that require custom agents, specific operating system tuning, legacy middleware, or direct control over networking and disk layout.

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For containerized platforms, the most direct comparison is Azure Kubernetes Service, Amazon EKS, and Google Kubernetes Engine. All three provide managed Kubernetes control planes, integrate with provider-native load balancers and identity systems, and support cluster autoscaling. GKE is often considered the most mature Kubernetes-native option because of Google’s history with Kubernetes, while AKS is attractive for Microsoft-centric environments and EKS fits naturally into AWS-heavy estates. If the team does not need to manage Kubernetes, compare Azure Container Apps, AWS Fargate, and Cloud Run for containerized workloads with less operational overhead.

For event-driven workloads, Azure Functions, AWS Lambda, and Cloud Functions provide managed execution without provisioning servers. They are commonly used for API backends, queue processing, file processing, scheduled tasks, and automation glue between managed services. For web applications, Azure App Service, AWS Elastic Beanstalk, AWS App Runner, and Google App Engine sit between raw infrastructure and fully serverless functions, giving teams a simpler deployment target for HTTP applications while still handling scaling, patching, and platform management.

Storage and Database Equivalents

Storage and database choices are some of the most frequently compared services in a multicloud architecture. The closest match depends on the access pattern: object storage for files and backups, block storage for virtual machines, file storage for shared mounts, relational databases for structured transactions, and NoSQL databases for flexible or high-scale workloads. The table below maps the common equivalents across Azure, AWS, and Google Cloud.

For object storage, Azure Blob Storage, Amazon S3, and Cloud Storage are the standard choices for application assets, data lakes, backups, logs, and static content. All three support lifecycle policies, encryption, versioning, access controls, and mulle redundancy options. S3 is often treated as the industry reference model, but the Azure and Google Cloud services provide similar core capabilities. When comparing them, focus on storage classes, retrieval fees, replication options, event integrations, and compatibility requirements such as S3 API support.

For virtual machine disks, Azure Managed Disks, Amazon EBS, and Google Persistent Disk or Hyperdisk provide durable block storage attached to compute instances. These services are suited to operating systems, transactional applications, and workloads needing low-latency reads and writes. Shared file workloads map to Azure Files, Amazon EFS, and Filestore, while higher-end enterprise file needs may use Azure NetApp Files, Amazon FSx for NetApp ONTAP, or Google Cloud NetApp Volumes.

On the database side, the cleanest relational comparison is Azure SQL Database, Amazon RDS, and Cloud SQL. They cover managed patching, backups, high availability, and common engines such as SQL Server, PostgreSQL, and MySQL. AWS also offers Amazon Aurora as a cloud-optimized MySQL and PostgreSQL-compatible option, while Google Cloud offers AlloyDB for PostgreSQL for performance-focused PostgreSQL workloads. Azure’s closest managed SQL Server experience is often Azure SQL Managed Instance, especially for migrations requiring broad SQL Server compatibility.

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NoSQL mappings require closer inspection because data models differ. Azure Cosmos DB supports mulle APIs and global distribution patterns, making it a common comparison point for several services. Amazon DynamoDB is the usual AWS match for key-value and document-style workloads at very high scale, while Amazon DocumentDB targets MongoDB-compatible use cases. In Google Cloud, Firestore fits application document data, and Bigtable fits wide-column, high-throughput workloads such as telemetry, time-series, and large analytical serving patterns.

Networking, CDN, and Connectivity Services

Networking is where naming differences can hide major design differences. Azure centers most architectures on Azure Virtual Network, AWS on Amazon VPC, and Google Cloud on Virtual Private Cloud. All three provide private IP address spaces, subnets, route tables, security controls, NAT, peering, private endpoints, VPN, dedicated connectivity, load balancing, DNS, and CDN options. The main comparison point is scope: AWS VPCs are regional, Azure VNets are regional, while Google Cloud VPCs are global resources with regional subnets.

For internet-facing applications, load balancing and edge delivery services are often combined. Azure offers Azure Load Balancer for layer 4 traffic, Application Gateway for layer 7 HTTP routing and web application firewall integration, and Azure Front Door for global HTTP acceleration, CDN, and edge routing. AWS maps these patterns to Elastic Load Balancing, including Application Load Balancer, Network Load Balancer, and Gateway Load Balancer, plus Amazon CloudFront for CDN and edge distribution. Google Cloud uses Cloud Load Balancing across global and regional load balancers, with Cloud CDN for caching static and dynamic content close to users.

Hybrid connectivity is another common multicloud comparison area. Azure’s main private circuit service is ExpressRoute, AWS uses Direct Connect, and Google Cloud provides Cloud Interconnect. For encrypted tunnels over the public internet, the comparable services are Azure VPN Gateway, AWS Site-to-Site VPN, and Cloud VPN. In larger estates, these are usually paired with centralized routing services such as Azure Virtual WAN, AWS Transit Gateway, or Google Cloud Network Connectivity Center to reduce point-to-point complexity.

When translating architectures across providers, avoid assuming that identically named services behave identically. For example, AWS security groups are stateful and attached to elastic network interfaces, Azure Network Security Groups apply to subnets or NICs, and Google Cloud firewall rules are defined at the VPC level using targets such as tags or service accounts. Similarly, global load balancing is a native pattern in Google Cloud, while Azure and AWS often combine regional load balancers with Front Door, Traffic Manager, CloudFront, Route 53, or Global Accelerator depending on failover and latency requirements.

Identity, Security, and Compliance Tools

Identity and security services are some of the hardest multicloud mappings because each provider uses a different control-plane model. Azure centers heavily on Microsoft Entra ID and Azure Policy, AWS uses IAM as the foundation for almost every permission decision, and Google Cloud uses Cloud IAM with strong ties to organizations, folders, projects, and service accounts. For architects, the most practical comparison starts with workforce identity, workload identity, secrets, encryption keys, posture management, and audit evidence.

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For human access, Microsoft Entra ID often becomes the central identity provider in enterprises already using Microsoft 365. It can federate into AWS IAM Identity Center and Google Cloud Identity, giving users one login path while still applying provider-specific roles inside each cloud. AWS IAM roles are typically scoped to accounts, while Google Cloud roles are commonly applied at organization, folder, project, or resource level. Azure uses subscriptions and management groups as the main hierarchy for RBAC and policy assignment.

For workload access, avoid long-lived credentials wherever possible. Azure managed identities let virtual machines, functions, and container workloads access Azure resources without storing secrets. AWS uses IAM roles for EC2, Lambda execution roles, ECS task roles, and EKS IAM Roles for Service Accounts. Google Cloud uses service accounts, Workload Identity Federation, and metadata-server-based credentials for compute services. In multicloud designs, workload federation is usually safer than copying access keys between clouds.

Practical selection guidance

• Use native IAM for authorization: even if SSO is centralized, keep permissions expressed in Azure RBAC, AWS IAM, and Cloud IAM so each platform can enforce least privilege directly.
• Separate secrets from keys: store passwords, tokens, and connection strings in Key Vault, Secrets Manager, or Secret Manager; manage encryption keys in Key Vault HSM, AWS KMS, or Cloud KMS.
• Standardize audit collection: export Azure Activity Log, CloudTrail, and Cloud Audit Logs into a central SIEM such as Microsoft Sentinel, Splunk, Chronicle, or Elastic.
• Apply guardrails early: use Azure Policy, AWS Service Control Policies, and Google Organization Policy to restrict public storage, unmanaged regions, weak encryption settings, and risky network exposure.

Compliance tooling is most useful when it is tied to actual control enforcement. AWS Config rules, Azure Policy compliance states, and Google Cloud asset and policy findings can show whether required controls are active across accounts, subscriptions, and projects. For regulated workloads, services such as Azure Confidential Computing, AWS Nitro Enclaves, Google Confidential Computing, AWS Artifact, Microsoft Purview, and Google Assured Workloads help document boundaries, encryption posture, and regional commitments.

Analytics, AI/ML, and Data Engineering Services

Analytics and machine learning services are some of the hardest to compare across Azure, AWS, and Google Cloud because each provider packages data ingestion, transformation, warehousing, governance, and AI differently. For a quick mapping, think in layers: data movement, data lakes, batch and stream processing, warehouses, business intelligence, model development, and prebuilt AI APIs. The closest equivalent is not always feature-for-feature identical, but the services below cover the same architectural roles in most cloud designs.

For warehouse-first analytics, BigQuery, Amazon Redshift, and Azure Synapse Analytics are the main comparison points. BigQuery is serverless by default and commonly chosen for fast SQL analytics with minimal cluster management. Redshift is often used in AWS-centered architectures where teams want tight integration with S3, Glue, IAM, and QuickSight. Azure Synapse fits well for organizations already using Microsoft analytics tooling, especially Power BI, Azure Data Lake Storage, and Microsoft Fabric.

For data engineering, the equivalent depends on whether teams prefer managed Spark, visual ETL, or streaming pipelines. Azure Data Factory, AWS Glue, and Cloud Data Fusion can all move and transform data, but they differ in authoring style and operational model. For Spark workloads, Azure Databricks, Amazon EMR, AWS Glue Spark, and Google Cloud Dataproc are common choices. For streaming, Azure Event Hubs, Amazon Kinesis, and Google Pub/Sub handle ingestion, while Azure Stream Analytics, Managed Service for Apache Flink, and Dataflow handle real-time processing.

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For AI and ML, the clearest mapping is Azure Machine Learning to Amazon SageMaker to Vertex AI. These platforms support books, training jobs, model registries, pipelines, deployment endpoints, and MLOps workflows. For generative AI, Azure OpenAI Service is strong for teams standardizing on OpenAI models with Azure governance, Amazon Bedrock offers a broad managed model marketplace with AWS security integration, and Vertex AI provides access to Gemini models and Google’s ML ecosystem. If the requirement is OCR, translation, speech, image labeling, or entity extraction, prebuilt AI APIs are usually faster to implement than training custom models.

DevOps, Monitoring, and Management Services

DevOps and operations tooling is where the three clouds often look similar at a category level but differ in workflow. Azure tends to integrate tightly with Azure DevOps, GitHub, and Azure Resource Manager. AWS centers many deployment and management tasks around Code* services, CloudFormation, Systems Manager, and CloudWatch. Google Cloud leans on Cloud Build, Cloud Deploy, Cloud Operations, and a strong Kubernetes-native experience through GKE. For multicloud teams, the most useful comparison is by pipeline, infrastructure provisioning, observability, configuration, and fleet governance.

For application delivery, Azure DevOps Pipelines remains a broad option for teams that want boards, repos, artifacts, test plans, and deployments in one suite. GitHub Actions is often the preferred choice when source code already lives in GitHub and workflows need to deploy across several clouds. AWS CodePipeline works well for AWS-centered release flows, especially when combined with CodeBuild, CodeDeploy, ECR, and CloudFormation. On Google Cloud, Cloud Build is commonly used for container builds and automated tests, while Cloud Deploy provides progressive delivery to GKE, Cloud Run, and other runtime targets.

For infrastructure provisioning, native templates are useful when a team is deeply invested in one cloud: Bicep for Azure, CloudFormation or CDK for AWS, and Google Cloud’s Terraform-focused tooling for GCP environments. In a multicloud architecture, Terraform is usually the common denominator because it gives teams one workflow for Azure Resource Manager, AWS APIs, and Google Cloud resources. Pulumi, Crossplane, and GitOps tools such as Argo CD or Flux may also fit when platform teams want infrastructure and Kubernetes configuration managed through versioned pipelines.

Operational service mapping

• Central observability: Azure Monitor, Amazon CloudWatch, and Google Cloud Operations each collect metrics, logs, alerts, dashboards, and service health signals.
• Application diagnostics: Application Insights, AWS X-Ray, and Cloud Trace help track request paths, latency, dependencies, and errors in distributed systems.
• Fleet administration: Azure Update Manager, AWS Systems Manager, and Google VM Manager handle patching, inventory, command execution, and OS configuration for virtual machines.
• Governance at scale: Azure Policy, AWS Config, and Google Organization Policy Service enforce standards such as approved regions, required tags or labels, encryption settings, and network restrictions.

The practical pattern is to choose one delivery toolchain as the standard, then connect it to each cloud’s native operational data. For example, a team might use GitHub Actions or Azure DevOps for deployments, Terraform for provisioning, Prometheus and OpenTelemetry for portable telemetry, and still feed platform-specific logs into Azure Monitor, CloudWatch, or Cloud Logging. This keeps day-to-day engineering workflows consistent while preserving access to the best native diagnostics and management features in each provider.

Frequently Asked Questions

Are Azure, AWS, and GCP services truly interchangeable?

Not always. Many services solve the same architecture problem, such as object storage, managed Kubernetes, or serverless functions, but they differ in limits, pricing models, integrations, regional availability, and operational behavior. Use service mappings as a starting point, then validate features like networking, IAM controls, backup options, SLAs, and compliance support before choosing an equivalent.

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What are the closest equivalents to AWS EC2, Azure Virtual Machines, and Google Compute Engine?

AWS EC2, Azure Virtual Machines, and Google Compute Engine are the main infrastructure-as-a-service VM offerings across the three clouds. They all support custom machine sizing, images, disks, networking, autoscaling, and load balancing. The practical differences usually come down to instance families, discount models, placement options, GPU availability, and how each provider handles images and managed disks.

How do I compare object storage across Azure Blob Storage, Amazon S3, and Google Cloud Storage?

Azure Blob Storage, Amazon S3, and Google Cloud Storage are the primary object storage services for files, backups, logs, data lakes, and static assets. Compare them by storage classes, lifecycle policies, versioning, encryption, replication, access controls, event integrations, and data transfer costs. If you are building a multicloud data lake, also check compatibility with tools that expect S3-style APIs, because support varies by platform and product.

Which managed Kubernetes services match across the three providers?

The main managed Kubernetes services are Azure Kubernetes Service, Amazon Elastic Kubernetes Service, and Google Kubernetes Engine. All three manage the Kubernetes control plane and integrate with each provider’s networking, identity, load balancing, logging, and container registry services. GKE is often seen as the most Kubernetes-native, AKS integrates tightly with Microsoft environments, and EKS is commonly chosen in AWS-heavy architectures.

How should I choose between similar analytics and AI services in a multicloud architecture?

Start with where your data already lives, because moving large datasets between clouds can add latency, cost, and governance complexity. For analytics warehouses, compare Azure Synapse Analytics, Amazon Redshift, and BigQuery based on query patterns, scaling model, ecosystem integrations, and pricing. For AI and machine learning, compare Azure AI Foundry and Azure Machine Learning, Amazon SageMaker and Bedrock, and Google Vertex AI based on model support, MLOps features, governance, and deployment options.

Bottom Line

Azure, AWS, and GCP offer broadly similar building blocks, but the best choice usually comes down to your team’s skills, existing contracts, regional needs, compliance requirements, and how each provider packages managed services. Use this cheat sheet as a starting point to translate architectures across clouds and identify the closest service matches before you design, migrate, or modernize.

Your next step is to shortlist the services that matter most for your workload—compute, data, identity, networking, and observability—then validate pricing, limits, SLAs, and integrations in the provider documentation. For multicloud environments, standardize where it helps, but choose each cloud’s native strengths when they reduce operational effort or improve reliability.

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Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.