AWS vs Azure vs GCP: Which Cloud Platform Should You Choose?

There's no single answer, because it depends on your workload type. For general-purpose compute, a 4 vCPU, 16 GB VM costs about $140 to $147 a month at on-demand rates in US regions (AWS m7i.xlarge, Azure D4s v5 and GCP n2-standard-4). List prices sit within about 5% of each other. Commitments change the picture: AWS and Azure offer savings of up to 72% through Savings Plans and Reservations, while Google Cloud's committed use discounts reach up to 55% (70% for memory-optimized machines).

Always model your specific workload before committing. Sticker prices for standard compute sit close together, so the real gaps come from commitment discounts, data transfer fees and storage tiers. Google Cloud's automatic sustained use discounts apply only to older machine families, so newer instances need a committed use discount to save.

Each provider leads in a different way. Google Cloud has the deepest AI research roots, with Gemini models, TPUs and BigQuery ML on its Gemini Enterprise Agent Platform (formerly Vertex AI). AWS offers the broadest managed catalog: SageMaker covers model training and deployment, and Amazon Bedrock gives you foundation models from several vendors behind one API. Microsoft Foundry (formerly Azure AI Foundry) brings OpenAI models and enterprise tooling into the Azure ecosystem.

If your team builds custom models and wants cutting-edge AI primitives, Google Cloud is a strong starting point. If you want managed, production-ready AI services that plug into your existing cloud setup, Amazon Bedrock and SageMaker on AWS or Microsoft Foundry on Azure are more practical choices.

Azure is the clear leader for hybrid cloud. Azure Arc lets you manage on-premises servers and Kubernetes clusters, including those in other clouds, from a single control plane. The tight integration with Microsoft Entra ID, Windows Server and Microsoft 365 makes it the default choice for enterprises already running Microsoft infrastructure.

AWS offers AWS Outposts for running AWS services on premises, which works well but requires dedicated hardware. Google Cloud covers hybrid Kubernetes through GKE Enterprise and Google Distributed Cloud, the successors to Anthos. For most enterprises evaluating hybrid setups, Azure's ecosystem advantage is hard to match unless you're running a non-Microsoft stack.

Azure has the widest geographic reach, with more than 70 datacenter regions across 33 countries, according to Microsoft. Google Cloud lists 43 regions and 130 zones, and AWS operates 39 regions with more than 120 Availability Zones. Providers count regions and zones differently, so check the locations you actually need before you compare totals.

For data residency requirements in specific countries, check each provider's region map before choosing. Norway is one example: Azure operates a region there, while AWS and Google Cloud have none, so Norwegian residency rules can point you toward Azure.

Yes. This is called a multi-cloud strategy, and it's increasingly common among mid-size and enterprise companies. The main reason is avoiding vendor lock-in: running workloads across two providers means you depend less on one provider's pricing, uptime or roadmap.

In practice, multi-cloud adds operational complexity. You'll need tooling to manage costs, security policies and deployments across both environments. It works best when each provider handles what it does best. For example, Google Cloud can run data pipelines and BigQuery analytics while AWS hosts application infrastructure and serverless workloads. Getting the architecture right from the start matters here, because the wrong split creates duplication costs and erases any savings.

It depends on the size and complexity of your infrastructure. A straightforward lift-and-shift of a small application can take a few weeks. A full migration of a production environment with databases, authentication systems and multiple services typically runs 2 to 4 months.

The database migration step is usually the trickiest part. Moving live data without downtime requires careful sequencing. AWS Database Migration Service (DMS) handles much of this, but it needs proper planning to avoid data loss or extended cutover windows. Our complete guide to AWS DMS covers how the process works in detail, including replication strategies and common failure points to avoid.

All three providers publish SLAs guaranteeing 99.9% to 99.99% uptime per service, but outages do happen. AWS has had notable incidents in us-east-1, Azure has had identity service outages, and GCP has experienced networking disruptions.

Trusting any single provider's uptime guarantee blindly is a risk, so architect for failure. That means deploying across multiple Availability Zones within a region, using health checks and auto-scaling and, for critical workloads, considering cross-region failover. A well-designed cloud setup on any of the three providers is significantly more resilient than on-premises infrastructure.

Start with your existing stack. If your team runs Windows Server and Microsoft 365, Azure removes significant friction. If you're building on open source and prioritizing flexibility, AWS has the widest service catalog and the largest talent pool. If data analytics and AI are core to your product, GCP's tooling is worth the learning curve.

Beyond technology fit, factor in the compliance certifications you need and which provider your engineers already know. Plan for cost management as you scale, too. The wrong choice at the architecture stage is expensive to undo.

If you'd like to see what this looks like in practice, the my-vpa case study shows how a company moved from fragmented identity infrastructure to a unified AWS setup, cutting IAM costs by 90% in the process. For a structured assessment of which provider fits your workload, Perfsys offers AWS consulting and migration services to help you decide with data from your own workload.