AWS and Azure get compared constantly, usually by people who have already decided the workload could run on either. That is normally true. What differs is cost shape, how much of the stack you run yourself, and which constraints each platform imposes.
This comparison works through the differences that actually change a decision, then says plainly which one to pick and when. Live starting prices and configuration counts for both are shown in the table below and come straight from the catalog.
Short answer. This is usually decided by what the organisation already runs. If identity, licensing and desktops are Microsoft, Azure removes friction that AWS will keep reintroducing. If there is no Microsoft estate to integrate with, AWS offers more services and a larger hiring pool.
AWS and Azure at a glance
| AWS | Azure | |
|---|---|---|
| Starting price | $25.00 | $25.00 |
| Configurations available | 25 | 5 |
| Best suited to | Teams that need one platform covering compute, storage, data and generative AI. | Organisations already invested in Microsoft tooling and identity. |
| Coverage | Global | Global |
| Notable services | EC2, S3, Amazon Bedrock, SageMaker | Azure OpenAI Service, Azure Machine Learning, Azure Virtual Machines, Azure Storage |
View AWS accountsView Azure accounts
Service breadth
AWS
The widest service catalogue of any provider, spanning compute, storage, databases, networking, analytics and machine learning.
Azure
A full-service catalogue with unusually deep hybrid and on-premises integration.
The practical question is not which catalogue is larger but whether the one you choose contains the specific services your project needs. A large catalogue you never touch is not an advantage.
How each one charges
AWS
Per-second and per-hour billing across dozens of instance families, with reserved and spot options that reward committed usage.
Azure
Per-second billing with reservations, plus licensing benefits that materially reduce cost if you already own Windows Server or SQL Server licences.
Beyond the instance rate, network egress is the cost that most often surprises people. On AWS, egress is billed per gigabyte and becomes a material line item on data-heavy workloads. On Azure, egress is billed per gigabyte, with the first allowance free each month.
If the workload moves a lot of data, read our breakdown of egress charges before comparing instance prices, because egress can dominate the bill.
The differences that matter
- Where each is strongest. AWS is chosen for unmatched service breadth and the deepest managed AI stack. Azure is chosen for hybrid deployments and integration with existing Microsoft identity and licensing.
- What you give up. With AWS, cost and complexity that grow quickly if nobody is watching the bill. With Azure, a portal and resource model that can feel heavier than the alternatives for a simple server.
Console and learning curve
AWS. A large console that rewards familiarity but is genuinely complex for newcomers, with IAM as a first-class concept from day one.
Azure. A portal that will feel familiar to anyone who administers Microsoft products, with Entra ID handling identity.
Outbound email
AWS. Outbound port 25 is restricted by default; production email is expected to go through Amazon SES.
Azure. Outbound port 25 is blocked on most subscription types and is not unblocked on request.
This is a hard constraint rather than a preference, so settle it before anything else if your workload sends mail. The guide to port 25 explains which providers permit it.
Where they run
AWS. Regions on every populated continent, with fine-grained control over where data lives.
Azure. The widest declared region count of the three hyperscalers, including many sovereign and government regions.
AI and machine learning
AWS. Bedrock for hosted foundation models and SageMaker for training and deployment, alongside GPU instance families.
Azure. Azure OpenAI Service for enterprise access to GPT-family models, plus Azure Machine Learning and AI Foundry for the wider lifecycle.
If AI work is the reason for the account, our guide to choosing a cloud for AI workloads covers the ground in more detail.
Which one to choose
Choose AWS when
Pick AWS when you need more than servers, and expect to reach for managed data, analytics or AI services in the same account.
Choose Azure when
Pick Azure when the organisation already runs on Microsoft identity, tooling or licensing.
Before you commit
- Confirm the specific services you need exist in the region you need them in. Availability varies by region on every provider.
- Estimate egress volume and price it on both platforms. It is the cost most often left out of a comparison.
- Check quotas. A new account starts with conservative limits, and raising them takes time on either platform.
- If outbound email matters, settle that question first, because it eliminates options rather than merely adding cost.
- Consider running both. Using each platform for what it is good at is more common than standardising on one.
Conclusion
This is usually decided by what the organisation already runs. If identity, licensing and desktops are Microsoft, Azure removes friction that AWS will keep reintroducing. If there is no Microsoft estate to integrate with, AWS offers more services and a larger hiring pool.
Both platforms are available here with published configurations and pricing: Buy AWS Accounts and Buy Azure Accounts. If you are still weighing them, the full comparison table puts every provider side by side, and support can talk through the specific workload.
Related cloud accounts
Accounts on the platforms discussed here, with every configuration and price published up front:
- Buy AWS Accounts — from $25.00. Over 200 services, from EC2 to Bedrock.
- Buy Azure Accounts — from $25.00. Azure OpenAI and deep Microsoft integration.
Further reading
- AWS vs DigitalOcean: Which Cloud Account Should You Buy?
- AWS vs IBM Cloud: Which Cloud Account Should You Buy?
- Cloud Free Tiers Compared: What You Actually Get
- Azure vs IBM Cloud: Which Cloud Account Should You Buy?
Official documentation
Provider behaviour, quotas and pricing are defined by the platform itself. These are the first-party references for the services discussed above:
Frequently asked questions
It depends on the workload shape rather than the list price. Compare the starting prices in the table above, then add expected egress, because that is where the two most often diverge.
Plain virtual machines and object storage move relatively easily. Managed services do not, so the difficulty depends on how much of each platform's proprietary surface you used.
AWS if you need more than servers, and expect to reach for managed data, analytics or AI services in the same account. Azure if the organisation already runs on Microsoft identity, tooling or licensing.
