Azure vs AWS: Key Differences and Which to Choose

Azure vs AWS is rarely a question about which platform is technically better. Both run global data center networks, both bill by the second, and both will host essentially any workload you bring them. The decision almost always comes down to three things: what your team already knows, which software licences you already pay for, and which AI models you need access to.

This guide compares Microsoft Azure and Amazon Web Services on compute, storage, identity, networking, containers, AI, pricing and hybrid cloud, then gives you a decision framework you can work through in about ten minutes. Where the two platforms are genuinely equivalent, we say so rather than manufacturing a winner.

How we compared: Service capabilities were checked against current Microsoft Learn and AWS documentation. Market share figures come from Synergy Research Group’s quarterly cloud infrastructure tracker. Pricing statements reflect published list rates and were last verified in Sept. 2026. We have not been paid by either vendor, and neither company reviewed this article.

Azure vs AWS at a Glance

Area	AWS	Microsoft Azure
Launched	2006	2010
Virtual machines	EC2 with Auto Scaling groups	Virtual Machines with Scale Sets
Object storage	Amazon S3	Azure Blob Storage
Block storage	Amazon EBS	Azure Managed Disks
Managed Kubernetes	Amazon EKS	Azure Kubernetes Service (AKS)
Serverless functions	AWS Lambda	Azure Functions
Identity	AWS IAM and IAM Identity Center	Microsoft Entra ID with RBAC
Monitoring	Amazon CloudWatch and CloudTrail	Azure Monitor with Log Analytics
Private networking	Amazon VPC	Azure Virtual Network
Flagship AI platform	Amazon Bedrock and SageMaker	Azure AI Foundry and Azure OpenAI Service
Hybrid and on-premises	AWS Outposts, Local Zones	Azure Arc, Azure Local
Main discount levers	Savings Plans, Reserved Instances, Spot	Reservations, Azure Savings Plan, Spot VMs, Hybrid Benefit
Strongest fit	Linux-native teams, startups, broadest service catalogue	Organisations already standardised on Microsoft 365 and Windows Server

What Is AWS?

Amazon Web Services is a cloud computing platform offering more than 200 services across compute, storage, databases, networking, analytics and machine learning. It launched in 2006, several years ahead of its competitors, and that head start still shows in the breadth of its service catalogue and the size of its community. Netflix and Airbnb are among its best-known production users.

Its practical advantage is maturity. For almost any problem you have, there is an AWS service, a Terraform provider, a Stack Overflow thread and several engineers on the market who have solved it before. If you want a deeper walkthrough of the underlying model, our guide to how Amazon Web Services actually works covers regions, availability zones and the pay-per-use billing model in more detail.

What Is Microsoft Azure?

Azure is Microsoft’s cloud platform for building, testing, deploying and managing applications across a global network of Microsoft-operated data centers. It launched commercially in 2010 and grew primarily through Microsoft’s existing enterprise relationships rather than through developer adoption.

That origin defines its strengths. Azure integrates tightly with Windows Server, SQL Server, Active Directory, Microsoft 365 and Visual Studio. If your organisation already runs on that stack, Azure is not a new vendor relationship so much as an extension of an existing one, with the licensing and support agreements to match.

Market Share: Where AWS and Azure Actually Stand

According to Synergy Research Group’s Q2 2026 cloud infrastructure report, worldwide spending on cloud infrastructure services reached $143 billion in the quarter, growing 43% year over year. AWS held 28% of that market, Microsoft 20% and Google Cloud 15%.

Two things are worth reading into those numbers. First, AWS still leads by a wide margin in absolute revenue, but its share has drifted downward over the past three years while Microsoft and Google have grown faster. Second, the share gap matters far less to your decision than it looks. Both platforms are large enough that service availability, regional coverage and long-term viability are settled questions. Choose on fit, not on market position.

Compute: EC2 vs Azure Virtual Machines

AWS provisions compute through EC2 (Elastic Compute Cloud). You select an instance family, launch instances, and use Auto Scaling groups to add or remove capacity based on demand. Azure provisions the equivalent through Virtual Machines, with Virtual Machine Scale Sets handling autoscaling.

Functionally these are close to interchangeable. Both offer general purpose, compute optimised, memory optimised and GPU instance families. Both bill per second for Linux workloads after a short minimum. Both offer custom ARM-based processors designed in-house, AWS Graviton and Azure Cobalt, which typically deliver better price to performance than equivalent x86 instances for workloads that can run on ARM.

One point of confusion worth clearing up: on both platforms, stopping an instance stops the compute charge. An Azure VM in the stopped and deallocated state does not accrue compute cost, and neither does a stopped EC2 instance. What continues to bill on both is attached storage, reserved public IP addresses and any provisioned throughput. The idea that Azure charges for idle VMs while AWS does not is simply incorrect.

The real compute difference is licensing. If you already own Windows Server or SQL Server licenses with Software Assurance, Azure Hybrid Benefit lets you apply them to Azure VMs and pay the Linux rate for the instance instead of the Windows rate. Microsoft’s own Azure Hybrid Benefit documentation sets out the eligibility rules, and they are stricter than most teams assume.. The real compute difference is licensing. If you already own Windows Server or SQL Server licences with Software Assurance, Azure Hybrid Benefit lets you apply them to Azure VMs and cut the compute rate substantially. There is no equivalent on AWS. For Windows-heavy estates this single mechanism can swing total cost of ownership by a wide margin.

Storage and Databases: S3 vs Azure Blob Storage

AWS offers Amazon S3 for object storage, EBS for block storage attached to instances, EFS for shared file systems and S3 Glacier tiers for archival. Azure offers Blob Storage for objects, Managed Disks for block, Azure Files for SMB and NFS shares, and Azure NetApp Files for high-performance enterprise file workloads.

Both support hot, cool and archive access tiers with automatic lifecycle policies, and both offer an automatic tiering option (S3 Intelligent-Tiering, Azure Blob lifecycle management) that moves infrequently accessed objects to cheaper tiers without you writing rules by hand.

On databases, the split is more interesting. AWS leads on purpose-built engines: RDS for managed relational databases, Aurora for MySQL and PostgreSQL-compatible workloads at scale, DynamoDB for key-value, Redshift for warehousing. Azure counters with Azure SQL Database, which is the most capable managed SQL Server experience available anywhere, plus Cosmos DB for globally distributed NoSQL and Microsoft Fabric for unified analytics.

If you are already running SQL Server on-premises, Azure SQL Database is a materially easier migration path than moving to RDS for SQL Server. If you are greenfield and Postgres-based, AWS Aurora has the deeper track record.

Identity and Security: AWS IAM vs Microsoft Entra ID

AWS controls access through IAM (Identity and Access Management), using users, groups, roles and JSON policy documents. IAM Identity Center handles single sign-on across multiple AWS accounts. Multi-factor authentication and temporary credentials through Security Token Service are standard.

Azure uses Microsoft Entra ID, which was renamed from Azure Active Directory in July 2023. Note that despite the old name, this is not the same product as on-premises Active Directory, though the two can be synchronised. Entra ID pairs with Azure role-based access control for resource permissions, and adds Conditional Access policies and Privileged Identity Management for just-in-time elevation.

Comparing feature counts here misses the point. Both platforms will pass a security audit. The decision-relevant fact is that Entra ID is also the identity layer behind Microsoft 365, Teams and Intune. If your organisation already uses those, adopting Azure means your cloud resources inherit the same directory, the same conditional access rules and the same device compliance posture you have already configured. On AWS you would federate against that directory instead, which works, but is a second system to maintain.

Conversely, if you have no Microsoft footprint, IAM’s policy model is well documented, widely understood and has a deeper ecosystem of third-party tooling for policy analysis and least-privilege enforcement.

Monitoring and Logging: CloudWatch vs Azure Monitor

AWS covers observability with CloudWatch for metrics, logs and alarms, X-Ray for distributed tracing, and CloudTrail as a separate service recording every API call for audit purposes. Azure consolidates more of this into Azure Monitor, which includes Log Analytics for log querying and Application Insights for application performance monitoring, with the Activity Log providing the audit trail.

The practical difference most engineers notice is the query experience. Azure Log Analytics uses Kusto Query Language, which is genuinely pleasant for ad-hoc log investigation and is consistent across Azure Monitor, Microsoft Sentinel and Microsoft Defender. CloudWatch Logs Insights has its own query syntax that is capable but less expressive. Neither will be the reason you choose a platform, but it will affect how much your on-call engineers enjoy their week.

Networking: Amazon VPC vs Azure Virtual Network

AWS builds private networking on Amazon VPC, with subnets, security groups, network ACLs, Transit Gateway for hub-and-spoke topologies, Direct Connect for dedicated private circuits, Route 53 for DNS and CloudFront for content delivery. Azure offers Virtual Network with network security groups, Azure Firewall, Virtual WAN, ExpressRoute for private connectivity, Azure DNS and Azure Front Door.

Feature parity here is close enough that the comparison rarely decides anything. What does deserve attention is egress cost. Both platforms charge for data leaving their network, typically in the range of $0.05 to $0.09 per GB for the first tier in North American and European regions, dropping as volume rises. For a media-heavy site serving 10 TB a month, that is several hundred dollars before you count a single hour of compute. Neither pricing calculator surfaces it by default.

One change worth knowing: since 2024 the major providers waive egress charges for customers migrating entirely off their platform, a shift driven by the European Union’s Data Act switching provisions. That weakens the old lock-in argument considerably. It does not apply to routine day-to-day egress, which both platforms still bill normally.

Containers and Serverless: EKS vs AKS, Lambda vs Functions

For Kubernetes, AWS offers EKS and Azure offers AKS. Both are managed control planes running upstream Kubernetes. The notable difference is billing: EKS bills a per-cluster control plane fee of roughly $0.10 per hour, which works out to about $73 per cluster per month before you run a single node. AKS offers a Free tier control plane at no charge, with no financially backed uptime SLA, and a paid Standard tier if you need one. A team running separate clusters for development, staging and production pays for three control planes on AWS and none on Azure. For teams running many small clusters across environments, that difference adds up.

Outside Kubernetes, AWS offers ECS with Fargate for serverless containers, and Azure offers Container Apps and Container Instances. For functions, AWS Lambda and Azure Functions are broadly equivalent, though Lambda has a longer track record at extreme scale and Azure Functions has better native integration with the rest of the Microsoft stack, including Logic Apps and Power Platform.

AI and Machine Learning: Bedrock vs Azure AI Foundry

This is where the two platforms have diverged most sharply, and where most older comparisons are now badly out of date.

AWS offers Amazon SageMaker for building, training and deploying custom models, and Amazon Bedrock as a managed gateway to foundation models from multiple vendors, including Anthropic, Meta, Mistral and Amazon’s own Nova family. AWS also designs its own training and inference silicon, Trainium and Inferentia, which can lower the cost of large-scale workloads.

Azure offers Azure Machine Learning for the custom-model path and Azure AI Foundry as its model platform, with Azure OpenAI Service providing enterprise-governed access to OpenAI’s models under Microsoft’s compliance and data-residency terms.

If your product depends on a specific model family, this decision is made for you. Teams building on OpenAI models with enterprise data-handling requirements go to Azure. Teams wanting a multi-vendor model catalogue with the ability to switch providers without rewriting their integration go to Bedrock. Treat the rest of the platform comparison as secondary once this constraint is established.

Worth noting that Google Cloud is a serious third option for data-and-AI-first teams, particularly where BigQuery and Vertex AI fit the workload. Our breakdown of Google Cloud’s advantages and downsides is a reasonable starting point if you want to widen the shortlist before committing.

Pricing Models: How AWS and Azure Actually Bill You

Headline per-hour rates between AWS and Azure are close enough that comparing them directly tells you almost nothing. What changes your bill is the commitment model you choose.

Mechanism	AWS	Azure
Pay as you go	On-Demand instances	Pay-as-you-go
1 or 3-year commitment	Savings Plans, Reserved Instances	Reservations, Azure Savings Plan for compute
Interruptible capacity	Spot Instances	Spot Virtual Machines
Existing licence reuse	Not available	Azure Hybrid Benefit
Non-production discount	Not available as a distinct tier	Dev/Test pricing for eligible subscriptions
Free entry tier	AWS Free Tier with 12-month and always-free services	Azure free account with starting credit plus free services

Three practical rules apply regardless of platform. Model your actual workload in the AWS Pricing Calculator and the Azure Pricing Calculator rather than trusting third-party comparison charts, which are almost always stale by the time you read them. Include egress, not just compute and storage. And if you are a Microsoft licensing customer, price Azure with Hybrid Benefit applied before you compare anything, because it frequently changes the answer.

Hybrid Cloud: Azure Arc vs AWS Outposts

If you are keeping workloads on-premises for latency, data residency or regulatory reasons, the two platforms take different approaches.

AWS Outposts ships physical AWS-managed racks into your data center, giving you AWS APIs locally. Local Zones and Wavelength extend AWS infrastructure closer to population centres and mobile networks respectively.

Azure Arc takes the opposite approach: rather than shipping hardware, it projects your existing servers, Kubernetes clusters and databases into Azure’s management plane so you govern them with the same policy, monitoring and security tooling regardless of where they run. Azure Local covers the on-premises hardware side.

For organisations with a substantial existing server estate that is not going away soon, Arc’s management-plane model is usually the lower-friction option. For workloads that need genuine AWS services running physically on site, Outposts is the more direct answer.

Azure vs AWS for WordPress and Web Hosting

Neither AWS nor Azure is a managed WordPress host, and it is worth being blunt about that because a lot of comparison content implies otherwise.

Both platforms give you infrastructure. You are responsible for the web server, PHP version, database tuning, caching layer, backups, security patching and CDN configuration. That is a meaningful operational commitment for a site that a managed WordPress host would run for a fraction of the effort, often on top of the very same AWS or Azure infrastructure.

If you do want to host WordPress directly, the lower-friction routes are Amazon Lightsail, which offers a pre-configured WordPress blueprint at a fixed monthly price, or Azure App Service for Linux, which has a WordPress offering in the Azure Marketplace. Both are a reasonable middle ground between shared hosting and building your own stack on raw VMs.

Going full infrastructure only makes sense at a certain scale: multi-region deployments, heavy WooCommerce traffic, strict compliance requirements, or a platform serving many sites under one governance model. If that describes your situation, the lessons in our write-up on delivering enterprise WordPress projects cover the architecture and governance decisions that matter more than the choice of cloud vendor itself.

Where Each Platform Frustrates People

AWS: The console is genuinely difficult to navigate once you are past a handful of services, and the naming conventions assume you already know what the products do. Support plans are billed as a percentage of spend, which means the answer to a simple question can be expensive. The sheer size of the service catalog means there are often three plausible ways to solve a problem and no clear guidance on which one you should pick.

Azure: Documentation quality is uneven, with older pages describing interfaces that no longer exist. Product renames are frequent enough that searching for a solution often surfaces instructions using terminology Microsoft has since retired, Azure AD to Entra ID being the obvious recent example. Resource groups and subscriptions add a layer of hierarchy that confuses teams coming from AWS.

Neither of these is a reason to rule a platform out. Both are real, and both show up in week two rather than week one.

How to Choose: A Decision Framework

Work through these in order. The first one that clearly applies to you is usually the answer.

1. Do you depend on a specific AI model family? OpenAI models under enterprise terms means Azure. A multi-vendor model catalogue means AWS Bedrock. This constraint overrides everything below it.
2. Do you already run Microsoft 365 and Windows Server with Software Assurance? If yes, Azure almost certainly wins on total cost and on identity integration. Price it with Hybrid Benefit applied before comparing.
3. Is your team Linux-native with existing AWS experience? Retraining an entire platform team is a real cost that rarely appears in a pricing comparison. Staying on AWS is usually cheaper than the theoretical savings of switching.
4. Do you need a service that only one platform offers? Check the specific service, not the overall catalogue size. AWS has more services in total, but the ones you actually need exist on both far more often than not.
5. Are you hiring? AWS has the larger talent pool and the more established certification path. Azure’s pool is growing fastest in enterprise and public-sector contexts.
6. None of the above apply? Pick either and move on. The opportunity cost of a six-month evaluation exceeds any difference in outcome. For a broader view of how cloud adoption affects operations and cost structure generally, our guide to simplifying cloud computing for business growth covers the organisational side of the decision.

A note on multi-cloud: running production workloads across both platforms simultaneously sounds like risk mitigation and usually functions as cost multiplication. You duplicate tooling, expertise, networking and security posture for a redundancy scenario that rarely materialises. Use a second provider deliberately for a specific capability, not as a default hedge.

Azure vs AWS: Pricing, Security and Migration Questions

Final Verdict on Azure vs AWS

There is no universal winner, and any comparison that declares one is selling something. AWS remains the broader and more mature platform with the deeper talent pool. Azure wins decisively inside organisations already standardised on Microsoft licensing, identity and tooling, where the integration and licensing advantages are large and concrete.

The mistake most teams make is spending months on the comparison itself. In practice, one of the six questions in the framework above will apply clearly to your situation, and once it does, the rest of the analysis is confirmation rather than decision-making. Identify your constraint, choose accordingly, and put the saved time into the architecture instead.