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

There's no single answer — it depends entirely on your workload type. For general-purpose compute, a standard 4-CPU, 16GB VM runs at roughly $88/month on AWS, $96/month on Azure, and $90/month on GCP at on-demand rates. Commitment discounts shift the picture significantly: savings of 40–72% are achievable through reserved instances and committed use contracts across all three providers.

As a rule of thumb: GCP tends to win on raw compute through Sustained Use Discounts, Azure often wins on storage, and AWS is competitive on serverless workloads like Lambda. Always model your specific workload before committing. The advertised rates rarely reflect your actual bill once data transfer fees and storage tiers are factored in.

Google Cloud leads on AI research depth, with Vertex AI, BigQuery ML, and TensorFlow originating from Google's own infrastructure. AWS offers the broadest managed ML service catalog, with SageMaker covering end-to-end model training and deployment. Azure has closed the gap fast through its deep partnership with OpenAI and Azure AI Studio.

If your team is building custom models and wants cutting-edge AI primitives, GCP is the strongest starting point. If you need managed, production-ready AI services that plug into your existing cloud setup, AWS SageMaker or Azure AI are more practical choices.

Azure is the clear leader for hybrid cloud. Azure Arc lets you manage on-premise servers, Kubernetes clusters, and multi-cloud resources from a single control plane. The tight integration with Active Directory, 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-premise, which works well but requires dedicated hardware. GCP covers hybrid Kubernetes management through 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 regions and over 400 data centers globally as of 2025. AWS operates 38 regions with over 100 Availability Zones across 27 countries — the oldest and most mature global network, with particularly strong coverage in North America and APAC. GCP runs 40 regions with at least 121 zones, a footprint that's growing fast but still trails the other two in total coverage.

For data residency requirements in specific countries, check each provider's region map before choosing. Azure is often the only option in markets like Norway and Germany where AWS doesn't yet have a local region.

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're not fully dependent 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, GCP for data pipelines and BigQuery analytics, AWS for application infrastructure and serverless. Getting the architecture right from the start matters here — the wrong split creates duplication costs rather than 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 2–4 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 99.9–99.99% uptime per service, but outages do happen. AWS had notable incidents in us-east-1, Azure has had Active Directory-related outages, and GCP has experienced networking disruptions.

The answer isn't to trust any single provider's uptime guarantee blindly — it's to 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-premise 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: what compliance certifications you need, which provider your engineers already know, and how you plan to manage costs as you scale. 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 real data rather than vendor marketing.