Top Cloud Providers Compared: AWS vs Azure vs GCP and When Each One Wins

Choosing a cloud provider is one of the most consequential infrastructure decisions an organisation makes, and it is rarely easy to reverse. Migrating 200 services from one cloud to another is a multi-year project that costs millions. The decision made during initial adoption shapes technology choices, hiring requirements, tooling investment, and cost structure for years.

The market has three dominant providers: Amazon Web Services, Microsoft Azure, and Google Cloud Platform. They collectively account for roughly two-thirds of global cloud revenue. Everyone else — Oracle, IBM, Alibaba Cloud, Tencent — competes for the remaining third.

Market Position and History

AWS launched commercially in 2006 and has never surrendered its lead. It currently holds approximately 31–33% of global cloud infrastructure spending, depending on the analyst. The head start translated into the deepest service catalogue (200+ services), the largest partner ecosystem, and the most AWS-certified engineers in the workforce.

Azure launched in 2010 and reached second place through aggressive enterprise sales and the obvious advantage of integration with Windows, Active Directory, Office 365, and SQL Server. Enterprise licensing agreements (EAs) with Microsoft often include Azure credits, making Azure the path of least resistance for companies already running a Microsoft-heavy stack. Azure holds approximately 22–24% of the market.

GCP launched in 2011 but did not invest heavily in enterprise sales until around 2018 when the current Google Cloud leadership took over. It holds approximately 10–12% of the market. GCP’s competitive strength comes from the internal tooling Google built at scale — Kubernetes originated at Google, BigQuery descended from internal systems for processing search indexes — rather than from sales volume.

Compute

Compute Offering Comparison
------------------------------
Provider  | VM Service         | Strengths
----------|--------------------|-----------------------------------------
AWS       | EC2                | 500+ instance types, spot instances,
          |                    | Graviton ARM CPUs (best perf/price)
Azure     | Virtual Machines   | Hybrid Benefit for Windows licences,
          |                    | spot VMs, burstable B-series
GCP       | Compute Engine     | Custom machine types, sustained-use
          |                    | discounts (automatic, no commitment),
          |                    | sole-tenant nodes

AWS EC2 wins on raw selection. If you need a very specific combination of CPU, memory, and network bandwidth, AWS almost certainly has an instance type optimised for it. AWS also has the best spot market — excess capacity sold at 70–90% discount — which is excellent for batch workloads.

GCP’s sustained-use discounts are worth understanding. If you run a VM for more than 25% of a month with no reservation, GCP automatically applies a discount. Run it all month and you get about 30% off without signing any commitment. AWS and Azure require reserved instance commitments to unlock equivalent discounts.

For Windows Server workloads, Azure’s Hybrid Benefit is a significant advantage. Organisations with existing Microsoft licences under Software Assurance can apply those licences to Azure VMs, reducing VM costs by 40–85% on Windows instances. This alone can make Azure the cheapest option for Windows-heavy workloads.

Managed Databases

All three providers offer managed relational databases, NoSQL stores, data warehouses, and specialised engines. The competition is closest here.

AWS: RDS supports MySQL, PostgreSQL, MariaDB, Oracle, and SQL Server. Aurora is AWS’s proprietary MySQL/PostgreSQL-compatible engine with 3–5x the performance of standard RDS. DynamoDB is the NoSQL option, with a serverless mode that scales to zero. Redshift handles data warehousing.

Azure: Azure SQL Database is the flagship managed MSSQL service. Cosmos DB is the globally distributed multi-model NoSQL database with single-digit millisecond guarantees anywhere in the world — arguably the most sophisticated multi-region database product among the three. Azure Synapse Analytics handles data warehousing and integrates deeply with Power BI.

GCP: Cloud Spanner is GCP’s most distinctive database product — a globally distributed relational database with ACID transactions at planetary scale. No other provider has anything directly comparable. BigQuery is the data warehouse, and it handles petabyte-scale queries through a serverless model where you pay per terabyte scanned.

Machine Learning and AI

GCP has the most mature ML infrastructure by most measures. Google’s Tensor Processing Units (TPUs) are custom silicon designed for ML training, available in GCP but nowhere else. Vertex AI is the unified ML platform. GCP’s founding advantage here is that Google built the research behind TensorFlow and many foundational ML techniques internally.

AWS SageMaker is the most widely used ML platform in production because of AWS’s customer base and breadth. It handles the full pipeline: data labelling, training, hyperparameter tuning, model deployment, and monitoring. AWS Bedrock adds managed access to large language models.

Azure has strong ML capabilities and the Microsoft OpenAI partnership gives Azure customers access to GPT-4 and other OpenAI models through Azure OpenAI Service. For enterprises that want to use LLMs in production with enterprise support contracts, Azure OpenAI has been the most common path.

Pricing and Cost Management

Direct price comparisons are difficult because instance types differ, egress pricing varies by region, and discounting mechanisms differ. Some practical observations:

AWS generally has the widest selection of pricing options: on-demand, reserved (1-year or 3-year), savings plans, and spot. The complexity of AWS pricing is itself a recurring complaint — engineers routinely underestimate bills.

GCP’s billing is simpler. Sustained-use discounts apply automatically without requiring reservation commitments. Per-second billing (shared with AWS Lambda and Azure) means short-lived workloads are billed more precisely.

Azure pricing is often lowest for Windows workloads when Hybrid Benefit applies, and Azure Reserved VM Instances follow the same 1-year and 3-year commitment structure as AWS.

When Each Provider Wins

Choose AWS when: You have no existing cloud footprint, you need the widest service selection, or you are building on open-source technology (Linux, Kubernetes, PostgreSQL) without strong vendor dependencies. AWS is the default choice for most greenfield cloud projects.

Choose Azure when: Your organisation runs Microsoft technologies (Active Directory, Office 365, SQL Server, .NET, Windows Server) and your licensing agreements include Azure credits. Azure is also strong for regulated industries — it has the most compliance certifications of any provider.

Choose GCP when: Your workload is data-heavy, ML-centric, or requires strong Kubernetes primitives. Companies running large-scale analytics pipelines, training custom ML models, or requiring globally consistent transactions (Cloud Spanner) often find GCP the strongest technical fit.

The multi-cloud reality: most large organisations end up using two or three providers for different purposes, even if one is dominant. Accepting that complexity — and investing in abstraction layers and cost management tooling — is part of operating at scale.