AWS vs Azure: How to Choose a Cloud for Your Product

AWS vs Azure is one of the first infrastructure decisions a product team makes, and one of the hardest to reverse. Both platforms can host a web app, a data pipeline or an AI feature. The real differences are in how well each one fits the tools you already use, how your team works and how you plan to pay.

This guide compares Amazon Web Services and Microsoft Azure on the points that actually change outcomes for startups and small businesses: market position, service equivalents, pricing models, licensing, skills and lock-in. It ends with a simple way to decide.

AWS vs Azure: where each one stands

AWS launched first and still leads. According to Synergy Research Group's Q4 2025 cloud market figures, Amazon, Microsoft and Google held 28%, 21% and 14% of the worldwide cloud infrastructure services market respectively, in a quarter worth $119.1 billion. Synergy Research also notes that Microsoft and Google have been growing faster than Amazon, with generative AI as a major driver.

Market share tells you both platforms are safe long-term bets. It does not tell you which one fits your product. A small SaaS company will never touch most of either catalog, so the comparison that matters is narrower.

Core services side by side

For the building blocks most products use, the two platforms map closely. Names differ; capabilities are broadly equivalent.

Both providers publish architecture guidance that covers the same ground: the AWS Well-Architected Framework and the Azure Well-Architected Framework each organize advice around reliability, security, cost, performance and operations. If your team follows either one, the habits transfer to the other platform.

Where AWS tends to fit better

• Breadth. AWS has the widest catalog of managed services, which matters if you expect to need niche capabilities later.
• Ecosystem. Many third-party tools, tutorials and open-source projects assume AWS first, so answers are easier to find.
• Hiring. AWS experience is common among cloud and DevOps engineers, which makes staffing and contractor reviews easier.
• Startups without a Microsoft estate. If your company runs on Google Workspace, Linux and open-source databases, there is less pulling you toward Azure.

Where Azure tends to fit better

• Microsoft-centric companies. If staff already sign in with Microsoft 365, Microsoft Entra ID gives you one identity system for people and applications.
• Existing Windows Server and SQL Server licenses. Azure Hybrid Benefit lets eligible licenses with Software Assurance or qualifying subscriptions be reused in Azure, which can change the cost comparison substantially.
• .NET applications. Azure's tooling, Visual Studio integration and App Service are built with .NET teams in mind.
• Enterprise buyers. Some B2B customers already run on Azure and prefer vendors in the same cloud and region for data residency and procurement reasons.

Pricing: why list prices mislead

Most AWS vs Azure price comparisons online compare list prices for a single instance, which says little about a real bill. List prices for comparable instances are usually close enough that the gap is not decisive. What moves the bill is how you buy and how you architect. Both providers offer pay-as-you-go pricing, discounts for one- or three-year commitments, and cheaper interruptible capacity for work that can be paused.

On AWS, for example, Savings Plans trade a committed hourly spend for one or three years in exchange for lower rates, which AWS says can save up to 72% on compute. Azure has its own reservations and savings plans with a similar logic. Either way, the discount only helps if you commit to capacity you will actually use.

The practical approach is to sketch your expected architecture — say, two app servers, a managed Postgres database, object storage and a CDN — and price it in both providers' official calculators with realistic traffic and data transfer. That takes an afternoon and beats any generic benchmark.

AWS vs Azure for AI features

Many teams now weigh AWS vs Azure partly on AI. Both give you hosted access to large language models, vector search options, GPU instances and the usual storage and queueing around them. The difference is mostly which model families are offered first-party and how access is governed.

• Azure is the route to OpenAI models inside a Microsoft-governed environment, which suits companies whose security and compliance teams already approve Microsoft services.
• AWS offers a range of models from several providers through Amazon Bedrock, alongside SageMaker for teams that train or fine-tune their own.
• Either way, the AI feature is usually a small part of the architecture. The application, data and identity decisions still dominate cost and risk.

If one specific model is central to your product, check that it is available in the region you need on the platform you are leaning toward, with the quotas you will need at launch. Model availability changes often, so confirm it in the provider's own documentation at the time you decide rather than relying on comparison articles.

Skills, portability and lock-in

In our experience the most reliable tiebreaker in any AWS vs Azure decision is the team. The cloud your engineers already know is often the right one, because unfamiliar platforms produce slow delivery and insecure defaults. If your team or agency has shipped production systems on one provider, that experience is worth more than a modest price difference.

Lock-in is real but manageable. Containers, Kubernetes, standard databases such as PostgreSQL and infrastructure-as-code tools keep the application layer portable. Proprietary serverless, queueing and database services are more productive but harder to move. A reasonable rule for early products: use managed services freely, but keep your data in standard formats and your business logic out of provider-specific glue where you can.

How to decide between AWS and Azure

1. 01List what you already run. Identity provider, office suite, operating systems, databases and languages. Heavy Microsoft usage points toward Azure.
2. 02Check your licenses. Existing Windows Server or SQL Server licenses can tilt the economics.
3. 03Ask your customers. Enterprise buyers sometimes require a specific cloud or region.
4. 04Count your skills. Choose the platform your engineers or delivery partner can operate well today.
5. 05Price a real architecture. Use both calculators with your expected workload and data transfer.
6. 06Decide on portability up front. Agree which provider-specific services are acceptable and which are not.

If the answers are evenly split, pick one and move on. The cost of debating AWS vs Azure for months is usually higher than the difference between them. For an early product, see also our guide to MVP vs prototype — the infrastructure for a prototype can be much simpler than for a production MVP.

Getting the cloud decision right the first time

Choosing a cloud is exactly the kind of call a CTO owns — if you do not have one, our explainer on what a chief technology officer does covers why. Our cloud and DevOps team helps founders pick a provider, design an architecture that stays affordable as usage grows, and set up deployment, monitoring and backups properly from the start. You own all code, infrastructure definitions and accounts from day one.

Tell us what you're building and which systems you already use, and a senior engineer will reply within one business day with an honest view of which cloud fits.