How to Compare Equivalent Cloud Services Across AWS, Azure, and Google Cloud for a Multicloud Architecture

To compare equivalent cloud services across providers for a multicloud architecture, map each workload need to the corresponding service on AWS, Azure, and Google Cloud, then compare those services on the properties that affect a multicloud design: scope (global versus regional), identity and access model, networking, data consistency and replication, limits, SLA measurement, and pricing structure. Where differences are large, standardize on portable layers such as containers, open-source data engines, Terraform or Pulumi, and OpenTelemetry. Where a provider-specific service delivers a clear advantage worth the lock-in, use it deliberately and isolate the dependency.

Disclosure: Control Plane publishes this guide.

TL;DR

• Comparison dimensions that matter: scope, identity model, networking and egress, data consistency, service limits and quotas, SLA definitions, and pricing structure (billing units).
• Categories where “equivalent” services differ most: networking (Google Cloud VPC is global, AWS VPCs and Azure VNets are regional), NoSQL databases (consistency models diverge sharply), identity (IAM roles versus managed identities versus service accounts), and global load balancing (architecturally different on each provider).
• Portable layers to standardize on: container images for compute packaging, Kubernetes for orchestration, PostgreSQL for relational data, Redis-compatible caches, Kafka-compatible or AMQP 1.0 messaging, Terraform or Pulumi for infrastructure as code, and OpenTelemetry for observability.
• Where Control Plane fits: Control Plane deploys one workload definition across AWS, GCP, and Azure through Control Plane’s Global Virtual Cloud (GVC), a named set of locations across one or more clouds, and uses Control Plane’s Universal Cloud Identity to reach native services on AWS, GCP, and Azure, so you standardize compute, identity, and networking without giving up provider-specific managed services where they matter. The same Day 2 operations, including autoscaling, built-in observability, and security, apply in one region of one cloud or across several.

How do I compare equivalent cloud services across providers?

A name-for-name mapping is only the first step. Two services can share a category label and still break your multicloud design because of how they scope resources, handle identity, replicate data, or bill usage.

1. Inventory your workload needs. List every capability your applications require: compute type, database engine, message delivery guarantees, object storage, secrets, observability, and networking topology.
2. Map each need to the three providers ’ services. Use the Google Cloud service comparison and Azure for AWS professionals as starting references, with the AWS overview for AWS scope.
3. Compare on the dimensions that affect multicloud design. For each trio of services, evaluate scope, identity model, networking, data consistency, limits, SLA, and pricing structure.
4. Score portability risk. For each service, estimate how much you would need to change to run the workload on a second provider tomorrow. High-risk services are those with proprietary APIs, unique consistency models, or deep ecosystem coupling.
5. Decide: standardize or use native. Where portability risk is high and the service is not a clear differentiator for your business, standardize on a portable alternative. Where the native service delivers a measurable advantage, use it and isolate the integration behind an abstraction layer.

Equivalent services across AWS, Azure, and Google Cloud, and where they differ

Which dimensions matter most for multicloud?

Seven properties decide whether two equivalent services behave the same inside a multicloud design.

Scope: global versus regional resources

A global Google Cloud VPC and regional AWS VPCs and Azure VNets lead to different IP plans, firewall rules, peering, and disaster recovery designs.

Google Cloud’s global external Application Load Balancer provides a single anycast IP that routes traffic to backends in any region. AWS composes Global Accelerator, Route 53, and CloudFront for similar behavior, and Azure uses Front Door. For cross-provider failover, these differences decide whether traffic shifts by DNS, with TTL-bound delay, or by anycast routing.

Identity and access model

AWS IAM roles, Microsoft Entra managed identities, and Google Cloud service accounts are conceptually similar but structurally different. Each provider’s identity system is deeply integrated with its own resource hierarchy and permission model. A workload running on one provider that needs to access a service on another provider must federate identity across boundaries.

The safest pattern is workload identity federation, where a workload proves its identity to a foreign provider through a short-lived token exchange rather than embedding long-lived credentials. This is possible natively through mechanisms like AWS STS AssumeRoleWithWebIdentity, Azure federated identity credentials, and Google Workload Identity Federation, but each implementation differs.

Networking and egress

Egress pricing is one of the largest hidden costs in multicloud. All three providers generally charge for data leaving their network (Google Cloud’s Data Transfer Essentials currently waives this for qualifying same-organization multicloud traffic in supported regions), and cross-cloud transfer adds the source provider’s egress plus any interconnect, NAT, or load balancer processing fees; ingress is free on all three. Rates vary by destination, region, and volume tier.

Cross-cloud connectivity options include site-to-site VPN, dedicated interconnects (AWS Direct Connect, Azure ExpressRoute, Google Cloud Interconnect), direct cloud-to-cloud links such as Google Cross-Cloud Interconnect, and third-party network fabrics. Latency, bandwidth, and encryption requirements determine the right choice. IP address range planning must prevent overlaps across providers.

Data consistency and replication

Consistency differences change how application code behaves. DynamoDB’s optional strong consistency, Cosmos DB’s five consistency levels, and Firestore’s strong consistency are different guarantees. An application designed around Cosmos DB’s bounded staleness will not behave the same way on DynamoDB without architectural changes.

Replication topology also matters. Aurora’s shared-storage model, Azure Flexible Server’s streaming replication, and AlloyDB’s disaggregated storage each have different failover times, replication lag characteristics, and cross-region capabilities. If your multicloud design requires data availability across providers, you need to evaluate these properties carefully rather than assuming “managed PostgreSQL” is portable.

Service limits and quotas

Every provider imposes default quotas on resources such as vCPUs per region, API requests per second, concurrent Lambda/function executions, and storage accounts. These defaults differ and can be raised to different ceilings. A workload that runs comfortably within AWS quotas may hit a limit on Azure or Google Cloud.

Document the quotas that matter for each workload and verify they are achievable on every target provider before committing to a multicloud deployment.

SLAs and how they are measured

All three providers publish SLAs, but they define availability differently. Scope changes the number too: Amazon EC2 commits to 99.99% at the region level but 99.5% for a single instance, and Compute Engine commits to 99.99% for instances in multiple zones but 99.9% for most single instances. Definitions of downtime, error rates, and measurement windows differ by service. The remedies (service credits) and claim processes also vary.

Comparing SLAs requires reading the full SLA documents behind each headline percentage. A 99.99% SLA on one provider may cover different failure modes than a 99.99% SLA on another.

Pricing structure: what is billed

Compare what each provider bills for, since unit prices change frequently.

For compute, some providers bill per second, others per minute or per hour depending on the service. Serverless containers may bill per request, per vCPU-second, or per instance-hour. Understanding the billing unit for each service lets you model costs accurately and avoid surprises when traffic patterns differ from your estimates.

Where should you standardize instead of using native services?

Make the standardize-or-native decision separately for each layer of the stack.

Managed services vs. portability and cloud lock-in covers these trade-offs in more depth.

How Control Plane fits

Control Plane is the unbreakable platform for running production workloads. It runs serverless, standard, stateful, and cron workloads natively on AWS, GCP, Azure, and Oracle Cloud Infrastructure (OCI), VM workloads on locations joined through Control Plane’s Managed Kubernetes, and workloads on Kubernetes clusters you already run or your own machines, including on premises, active-active across regions and clouds.

Control Plane-managed compute runs in AWS, GCP, Azure, and Oracle Cloud regions. The same Day 2 operations apply whether a GVC covers one region of one cloud or locations across several clouds, so teams get high availability and lower compute costs without committing to a multicloud design.

A workload deployed to Control Plane’s Global Virtual Cloud (GVC) , a named set of locations across one or more clouds, runs in every location of that GVC from one definition, on AWS, GCP, and Azure. This means the compute layer is the same on every provider. Workloads run standard container images, so the standardization decision for compute packaging is built in.

Control Plane’s Universal Cloud Identity gives a workload access to services on AWS, GCP, and Azure through an IAM role or service-account grant instead of long-lived keys in your code, with short-lived, least-privilege credentials at runtime. One workload can use native services on AWS, GCP, and Azure without managing separate credential pipelines for each cloud.

The Template Catalog , Control Plane’s library of ready-to-install apps, includes open-source data services such as PostgreSQL, Redis, and CockroachDB that run the same way on every provider. For teams that want to standardize their data layer across clouds, this eliminates the need to reconcile differences between provider-managed database services.

Control Plane ships its own Terraform and Pulumi providers, so it complements the infrastructure as code you already run. Control Plane handles Day 2 operations for you, including autoscaling, built-in observability, and security. The 99.999% availability SLA applies to workloads running at least two replicas in at least two locations. Customers typically cut compute costs 30 to 50 percent compared with running directly on AWS, GCP, or Azure. Control Plane is PCI DSS Level 1, SOC 2 Type II, HIPAA, and GDPR compliant.

Provider-specific managed services you choose to keep, such as a data warehouse, still differ by provider. On Control Plane, workloads reach those services through Universal Cloud Identity, so you keep the native service and run portable compute around it. You can run the same app across AWS, GCP, and Azure while still consuming native services where they deliver the most value.

More strategies for reducing lock-in are in how to avoid cloud vendor lock-in and the best tools to deploy applications on AWS, GCP, and Azure.

FAQ

Short answers to common questions about comparing cloud services for multicloud.

How do I compare equivalent cloud services across providers when planning a multicloud architecture?

Build a service map for every workload requirement across AWS, Azure, and Google Cloud, then score each trio on scope, identity, networking, consistency, quotas, SLA terms, and billing units. Standardize on portable layers where differences are large, and use provider-native services deliberately where the benefit justifies the coupling.

What is the AWS equivalent of Azure Container Apps and Google Cloud Run?

Amazon ECS on AWS Fargate is the closest equivalent for running containers without managing servers. Cloud Run and Azure Container Apps scale to zero by default and, in their default billing modes, charge only while instances handle requests, metered in vCPU-seconds and memory-seconds plus a per-request fee. Fargate tasks remain running until stopped and bill per vCPU and memory per second while the task is active. The concurrency models and cold-start behaviors also differ.

Are AWS, Azure, and Google Cloud services really interchangeable?

They are functionally similar within categories but structurally different in ways that matter for multicloud. Consistency models, identity systems, networking scope, billing units, and API surfaces all vary. Treating them as interchangeable without examining these differences leads to broken assumptions in production.

Which cloud services are hardest to move between providers?

NoSQL databases (DynamoDB, Cosmos DB, Firestore) are among the hardest because their consistency models, query languages, and data models are fundamentally different. Serverless functions are also difficult to port because of trigger ecosystems and runtime differences. Identity requires remapping roles and trust relationships per provider; secret values move easily, but access policies and rotation hooks do not. Data warehouses also carry heavy lock-in through proprietary SQL dialects and storage, which open table formats such as Apache Iceberg reduce.

Is a Google Cloud VPC the same as an AWS VPC?

No. A Google Cloud VPC is a global resource that spans all regions, with subnets scoped to individual regions. An AWS VPC is a regional resource, and subnets are scoped to availability zones within that region. Azure VNets are also regional.

How do I compare cloud SLAs across providers?

Read the actual SLA documents rather than comparing headline percentages. Check how each provider defines availability (error rate thresholds, measurement windows), what failure modes are covered, what remedies are offered (typically service credits), and what the claim process requires. Two SLAs with the same percentage can count downtime over different windows and error thresholds.

Should I use managed databases in a multicloud architecture?

It depends on your portability requirements. If you standardize on PostgreSQL, you can use managed PostgreSQL services on any provider (RDS, Azure Database for PostgreSQL, Cloud SQL) with reasonable portability, since the SQL dialect and wire protocol are the same, though supported extensions and major-version timing differ. If you use a proprietary database service like DynamoDB or Cosmos DB, you gain provider-optimized performance but accept significant lock-in. For maximum portability, run an open-source engine from Control Plane’s Template Catalog, such as PostgreSQL, CockroachDB, or a multi-location PostgreSQL cluster, which deploys the same way on every provider.

How does Control Plane help run one app across AWS, Azure, and Google Cloud?

Control Plane lets you define a workload once and deploy it to a Global Virtual Cloud that spans locations on AWS, GCP, and Azure. Universal Cloud Identity lets that workload reach native services on each provider through an IAM role or service-account grant. Autoscaling, observability, and security are built in, backed by a 99.999% availability SLA for workloads running at least two replicas in at least two locations.

To standardize compute, identity, and networking across AWS, GCP, and Azure while keeping the native services that matter, talk to the Control Plane team or sign up free.

Originally published on the Control Plane blog.