Compare Cloud Providers: AWS vs Azure vs Google Cloud
The short version
Amazon Web Services (AWS), Microsoft Azure and Google Cloud Platform (GCP) all run reliable infrastructure and cover the same basics: virtual machines, containers, managed databases, storage, networking, serverless functions and AI services. The right choice usually depends less on raw features and more on your team's existing skills, your current vendors and what your customers expect.
Amazon Web Services
AWS is the oldest and broadest of the three. It has the most services and the largest ecosystem of tutorials, consultants, third-party tools and job candidates who already know it. If you need a niche service, AWS probably has one.
Strengths: breadth, maturity, a huge community and strong regional coverage. Weaknesses: the console and service catalog are overwhelming for newcomers, and pricing has many dimensions that are easy to misjudge. Costs for data leaving the network (egress) surprise many teams.
Good fit for: startups that want the default industry choice, teams hiring broadly, and workloads that need a wide mix of services.
Microsoft Azure
Azure's biggest advantage is its link to the Microsoft ecosystem. If your company already uses Microsoft 365, Entra ID (formerly Azure Active Directory), Windows Server, SQL Server or .NET, Azure integrates with all of them in ways the others cannot match. It is also strong in enterprise and regulated industries, and it hosts Azure OpenAI Service for teams that want OpenAI models under Microsoft's contract and compliance terms.
Strengths: identity and hybrid-cloud integration, enterprise licensing deals, strong .NET support. Weaknesses: naming and portal structure can be confusing, and some teams report that service quality varies between products.
Good fit for: enterprises already invested in Microsoft, hybrid setups with on-premises servers, and .NET-heavy teams.
Google Cloud
Google Cloud is often smaller in market share but popular with engineering-led teams. It has strong data and analytics tools, led by BigQuery, and Kubernetes originated at Google, so Google Kubernetes Engine is well regarded. Cloud Run, which runs containers without server management, is a favorite for simple deployments. Its AI offerings include Vertex AI and the Gemini models.
Strengths: data analytics, Kubernetes, clean developer experience, and generally simpler networking defaults. Weaknesses: a smaller partner and enterprise sales network, and fewer niche services than AWS.
Good fit for: data-heavy products, machine learning work and teams that want a straightforward container platform.
How to compare them for your project
Start with your team
The cloud your engineers already know will be cheaper to run than a theoretically better one they have to learn. Training time and mistakes are real costs.
Check pricing with real numbers
List prices do not tell you your bill. Use the official calculators, such as the Google Cloud pricing calculator or the Azure pricing calculator, and enter your expected compute, storage and traffic. AWS offers a similar calculator. Pay attention to:
- Egress fees for data leaving the provider
- Managed database and load balancer costs, which often exceed compute
- Discounts for committing to usage over one or three years
- Support plan costs
- Free tiers, which differ by provider and change often, so check the current terms
Look at compliance and regions
If you need data to stay in a specific country or you must meet standards like SOC 2, HIPAA or ISO 27001, confirm that the specific services you plan to use are covered in the region you need. Coverage is per service, not per provider.
Think about lock-in honestly
Using proprietary managed services saves time but makes leaving harder. Containers, standard databases like PostgreSQL and infrastructure-as-code tools such as Terraform keep your options open. Total portability is rarely worth chasing, but avoiding needless dependencies is.
Do you need a hyperscaler at all?
Not always. For small apps, a simpler platform may be faster and cheaper to run. Vercel and Netlify handle front-end and Next.js hosting with little setup. DigitalOcean, Hetzner and Fly.io offer straightforward servers and containers at lower complexity. Cloudflare provides a CDN, serverless functions, object storage and a database, with no egress fees on its R2 storage. Many teams run on one of these until they have a reason to move.
If you are building a Next.js product and want a head start on the deployment side, Shipkit, a Next.js starter kit by Lacy Morrow, is one place to look at how a production project is put together before you pick a host.
A quick decision guide
- Choose AWS if you want the widest selection and the easiest hiring.
- Choose Azure if your company runs on Microsoft tools and identity.
- Choose Google Cloud if analytics, containers or machine learning are central.
- Choose a simpler platform if your app is small and you would rather ship than administer infrastructure.
Before you commit
Run a small pilot. Deploy one real service on your top choice, set billing alerts on day one, and watch the first month's invoice closely. Billing alerts and budgets are available on all three providers and take minutes to configure. Most cloud horror stories start with an unmonitored account. Then review the result with your team. The best cloud is the one you can operate confidently and afford to keep running.