Almost every company that moves to the cloud ends up choosing among three providers: Amazon Web Services (AWS), Microsoft Azure and Google Cloud Platform (GCP). All three are mature, secure and capable of running anything from a small website to a global platform. Yet they differ in culture, pricing structure, strengths and learning curve.
This guide compares them in business terms and gives you a framework to decide. Market-share and pricing details change frequently, so verify current figures with each provider and industry analysts such as Synergy Research Group.
Key takeaways
- AWS: largest and broadest; the default for many startups and cloud-native companies.
- Azure: the natural fit for Microsoft-centric organisations (Windows Server, Microsoft 365, Active Directory, SQL Server).
- Google Cloud: a leader in data analytics, AI and machine learning, and Kubernetes.
- Skills, existing licences and the specific workload usually matter more than headline price.
Market position at a glance
AWS is generally reported as the market leader, followed by Azure and then Google Cloud, according to industry analyst studies. Azure has been closing the gap, driven by enterprise Microsoft relationships. Percentages vary by study and year, so treat any single number with caution and check recent reports.
AWS: the broad, mature option
Strengths
- Breadth and depth: the largest catalogue of services, from compute and databases to IoT, media and machine learning.
- Maturity and ecosystem: a huge community, partners, documentation, tutorials and talent pool.
- Global footprint: many regions and availability zones.
- Flexibility: multiple ways to run workloads (virtual machines, containers, serverless).
Watch-outs
- The sheer number of services and pricing options can be confusing.
- Costs can spiral without governance and tagging.
- Console and naming can be complex for beginners.
Good fit: startups, SaaS, e-commerce, media, and organisations wanting the widest choice.
Microsoft Azure: the enterprise and Microsoft fit
Strengths
- Microsoft integration: Active Directory (Entra ID), Microsoft 365, Windows Server, SQL Server and Dynamics work naturally with Azure.
- Hybrid strength: tools such as Azure Arc and Azure Stack help bridge on-premises and cloud.
- Licensing benefits: existing licences can reduce costs for some Windows and SQL workloads.
- Enterprise relationships: strong support, compliance offerings and procurement familiarity.
Watch-outs
- Portal and naming conventions can be inconsistent across services.
- Savings claims for Windows workloads come partly from Microsoft; verify with your own licences.
Good fit: enterprises and mid-sized firms already invested in Microsoft technology, regulated industries and hybrid scenarios.
Google Cloud: the data and AI specialist
Strengths
- Data analytics: BigQuery is widely admired for large-scale analytics.
- AI and machine learning: strong tooling and models for data science teams.
- Kubernetes: Google created Kubernetes, and Google Kubernetes Engine is highly regarded.
- Networking and developer experience: a clean, developer-friendly approach and sustained-use discounts.
Watch-outs
- Smaller service catalogue and partner ecosystem than AWS or Azure.
- Fewer regions, though coverage keeps growing.
- Enterprise sales and support experience historically less extensive.
Good fit: data-heavy products, AI-first startups, analytics and container-native teams.
Comparison table
| Factor | AWS | Azure | Google Cloud |
|---|---|---|---|
| Best known for | Breadth, maturity | Microsoft integration, hybrid | Data, AI, Kubernetes |
| Service catalogue | Largest | Very large | Smaller but focused |
| Learning curve | Steep (many options) | Gentle for Microsoft teams | Moderate, developer-friendly |
| Pricing model | Pay as you go, reserved and savings plans, spot | Pay as you go, reservations, hybrid benefit | Pay as you go, sustained-use and committed-use discounts |
| Strong in | Startups, SaaS, general workloads | Enterprise, Windows, regulated sectors | Analytics, ML, container platforms |
| Typical risk | Cost sprawl | Complexity across portals and licensing | Smaller ecosystem |
Pricing: how to compare honestly
Published comparisons disagree, because the answer depends on the exact instance type, region, operating system, licences, data transfer and commitment. To compare fairly:
- Sketch your architecture: compute, database, storage, networking, backups, monitoring.
- Price it in each provider's official calculator.
- Include data egress charges, support plans and licences.
- Model one-year or three-year commitments if your workload is steady.
- Add the cost of skills: training or hiring for the platform you choose.
- Run a small proof of concept to validate performance and cost before committing.
Whatever the provider, use budgets, alerts and tagging from day one to avoid bill shock. Our cloud migration guide covers cost control in detail.
Security and compliance
All three provide strong infrastructure security and a long list of certifications. Under the shared responsibility model, the provider secures the underlying platform, while you secure identities, configuration, data and applications. Most cloud incidents stem from misconfiguration and weak access control, not from the provider's data centres. Basics: MFA for all admins, least privilege, encryption, logging and regular reviews. See also our cybersecurity threats guide.
Beyond the big three
For simple workloads, smaller platforms such as DigitalOcean, Hetzner, Cloudflare or managed platforms such as Vercel and Render can be cheaper and simpler. Many of our own projects run on a managed platform or a custom VPS, and move to a hyperscaler only when requirements justify it.
How to choose: a 6-question framework
- What do you already use? Heavy Microsoft usage points to Azure.
- What is the workload? Data and ML point to Google Cloud; broad general-purpose needs point to AWS.
- What skills does your team have? Choose where you can hire and train easily.
- Where are your customers? Check regions near them and data-residency rules.
- What compliance do you need? Confirm certifications and available tooling.
- How much complexity can you manage? Simpler platforms reduce operational burden.
Avoid lock-in sensibly
- Use containers and open standards where it does not slow you down.
- Keep data exportable and document your architecture.
- Use infrastructure as code so environments can be recreated.
- Do not over-engineer for hypothetical portability; it costs more than most migrations would.
Equivalent services across the three clouds
Names differ, but the building blocks are similar. This cheat sheet helps when you read documentation from another provider:
| Capability | AWS | Azure | Google Cloud |
|---|---|---|---|
| Virtual servers | EC2 | Virtual Machines | Compute Engine |
| Containers (managed Kubernetes) | EKS | AKS | GKE |
| Serverless functions | Lambda | Functions | Cloud Functions / Cloud Run |
| Object storage | S3 | Blob Storage | Cloud Storage |
| Managed relational database | RDS / Aurora | Azure SQL / Database for PostgreSQL | Cloud SQL / AlloyDB |
| Data warehouse | Redshift | Synapse / Fabric | BigQuery |
| Identity and access | IAM | Entra ID and RBAC | Cloud IAM |
| Content delivery network | CloudFront | Front Door / CDN | Cloud CDN |
| Monitoring | CloudWatch | Monitor | Cloud Monitoring |
Scenario guide: which cloud for which business?
- A startup building a SaaS product with a small team: any of the three works; AWS offers the biggest pool of tutorials and talent, while a simpler platform such as Vercel, Render or a VPS may be cheaper until you scale.
- A company running Microsoft 365, Windows Server and SQL Server: Azure usually integrates most smoothly, including identity and licensing benefits.
- A data and analytics company or an AI team: Google Cloud's BigQuery and machine learning tooling are strong draws, although all three offer capable AI services.
- A regulated enterprise with hybrid needs: Azure and AWS both have strong compliance and hybrid offerings; your auditors and existing contracts may decide.
- A small business with a website and a few apps: a managed host or a small VPS is often simpler and cheaper than a hyperscaler.
Skills and hiring
The platform you can staff is often the platform you should use. Check local job markets for engineers and consultants with experience on each cloud, and look at certification paths. A smaller provider with a team that knows it well usually beats a bigger platform nobody understands.
Proof-of-concept checklist
- Choose one representative workload, such as a web app with a database.
- Build it on your preferred provider using infrastructure as code.
- Measure performance from your users' locations.
- Record the real monthly cost, including data transfer and support.
- Test backup, restore and a failure scenario.
- Time how long it took your team and what confused them.
- Decide with data, not slide decks.
Common mistakes when choosing a cloud
- Choosing on a single published price comparison without modelling your workload.
- Ignoring data egress and support plan costs.
- Over-engineering for multi-cloud before you need it.
- No cost governance: no budgets, alerts or tagging.
- Giving everyone administrator rights during the first weeks and never removing them.
Get an independent recommendation
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