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AWS vs Azure: A Pragmatic Guide for Engineering Leaders
- Authors
- Name
- Christopher Clemmons
AWS vs Azure: picking the right cloud for your team and your roadmap
Choosing a primary cloud is not a beauty contest. It is a strategic decision that affects staffing, cost, security posture, delivery speed, and the shape of your architecture for years. This guide compares Amazon Web Services (AWS) and Microsoft Azure in the areas that matter to engineering managers and senior developers: core primitives, identity and security, networking, containers and serverless, data platforms, analytics and AI, governance, hybrid options, cost, and developer experience.
I keep this practical. The goal is not to crown a universal winner. The goal is to help you pick the platform that fits your business constraints and your team’s strengths.
TL;DR
- If your world is Microsoft first , Entra ID (formerly Azure AD), Windows Server, SQL Server, Microsoft 365 , Azure gives you tighter integration and licensing benefits.
- If you want the broadest set of building blocks and a massive OSS ecosystem , AWS typically has more primitives and partner depth, plus very mature multi-account governance patterns.
- For containers and Kubernetes , both are strong. AKS is very operationally convenient. EKS is highly configurable and pairs well with the wider AWS networking and security stack.
- For serverless , AWS Lambda and Step Functions are best-in-class. Azure Functions and Durable Functions are excellent, especially if you live in the Microsoft stack.
- For data , DynamoDB is a proven global NoSQL option on AWS. Azure Cosmos DB is multi-model and multi-API. Relational options are strong on both sides.
- For AI , AWS offers Bedrock and SageMaker. Azure offers Azure OpenAI Service and Azure Machine Learning with deep Microsoft integration.
- For governance at scale , AWS Organizations plus Control Tower and SCPs are excellent for multi-account setups. Azure’s Management Groups, Policy, and Landing Zone approach are equally strong for multi-subscription estates.
- Cost , either platform can be cost-effective with the right architecture, rightsizing, and discount programs. Your team’s skills and the friction of integration usually dominate TCO.
Core concepts and resource hierarchy
| Concept | AWS | Azure | Notes for managers |
|---|---|---|---|
| Global tenancy | Account | Subscription | You will likely use many accounts or subscriptions. Both support org-wide policy. |
| Org grouping | AWS Organizations with OUs | Management Groups | Enables guardrails and budget control across many environments. |
| Identity | IAM, IAM Identity Center, Cognito | Microsoft Entra ID (formerly Azure AD) | Azure identity is first class across Microsoft 365 and Windows. AWS IAM has very granular policy constructs. |
| Resource grouping | Tags, CloudFormation stacks, StackSets | Resource Groups, Tags | Azure Resource Groups are a strong operational unit. Tags are essential everywhere. |
Networking and traffic
| Area | AWS | Azure | Practical take |
|---|---|---|---|
| Private networking | VPC | Virtual Network (VNet) | Both provide similar constructs: subnets, route tables, NAT, peering. |
| Global entry and CDN | CloudFront, Global Accelerator, Route 53 | Azure Front Door, Azure CDN, Azure DNS | Azure Front Door and AWS CloudFront both provide global edge presence. |
| Load balancing | ALB, NLB, GWLB | Application Gateway, Azure Load Balancer, Traffic Manager | ALB vs App Gateway are the HTTP layer 7 options. |
| Private connectivity to on-prem | Direct Connect | ExpressRoute | Both deliver predictable private links. |
| Network policy | Security Groups, NACLs, Network Firewall | NSGs, ASGs, Firewall | AKS and EKS add CNI-level policies for pods. |
Containers and Kubernetes
Managed Kubernetes:
- AWS EKS , powerful control over networking, IAM integration with IRSA, flexible data plane options, strong multi-account patterns.
- Azure AKS , fast to get running, deep integration with Azure Monitor and Azure Policy, good Windows container support.
Registry: ECR vs ACR , both integrate with their runtimes and CI.
When to favor EKS
- You need fine-grained control of CNI, security boundaries, and cross-account patterns.
- You are already all-in on AWS networking and security services and want a uniform model.
When to favor AKS
- You want quick developer onboarding, built-in diagnostics with Log Analytics, and Azure Policy integration from day one.
- Your estate includes Windows containers or you rely on Entra ID integration heavily.
Tip: standardize add-ons across both clouds , Ingress, cert-manager, cluster autoscaler, OPA/Gatekeeper or Kyverno, external-secrets, CSI storage drivers. Use GitOps (Argo CD or Flux) to control drift.
Serverless and eventing
| Capability | AWS | Azure | Notes |
|---|---|---|---|
| Functions | Lambda | Azure Functions | Cold start profiles and triggers are excellent on both. AWS has Step Functions for robust orchestration. Azure has Durable Functions. |
| Queues and topics | SQS, SNS, EventBridge | Storage Queues, Service Bus, Event Grid | EventBridge and Event Grid are the event routers. Service Bus is feature-rich for enterprise messaging. |
| Storage for events | Kinesis family, MSK | Event Hubs, Kafka on HDInsight | Pick based on throughput, retention, and ecosystem needs. |
Pattern: Lambda + EventBridge + SQS is a clean decoupled spine on AWS. Azure Functions + Event Grid + Service Bus is the mirror pattern on Azure.
Data platforms
| Need | AWS | Azure | Comment |
|---|---|---|---|
| Relational | RDS (Aurora, MySQL, PostgreSQL, SQL Server) | Azure SQL, Flexible Server for OSS | If you are SQL Server-heavy, Azure has licensing advantages. |
| NoSQL | DynamoDB | Cosmos DB | DynamoDB excels at predictable single-digit ms latency and global tables. Cosmos offers multiple APIs and consistency levels. |
| Object storage | S3 | Azure Blob Storage (ADLS Gen2) | Both underpin lakehouse patterns and cheap archival tiers. |
| Analytics | Athena, EMR, Glue, Redshift | Synapse, Databricks on Azure, Microsoft Fabric | Choose by team skills and BI stack. |
| Caching | ElastiCache (Redis/Memcached) | Azure Cache for Redis | Parity for most use cases. |
AI and ML
- AWS: SageMaker for the ML lifecycle, Bedrock for foundation models and managed agents.
- Azure: Azure Machine Learning, Azure OpenAI Service for enterprise access to leading models plus easy integration with Microsoft data and security.
Pick the platform your data scientists can operate comfortably and where your data already lives. Moving large datasets across clouds will dominate cost and schedule.
Security, identity, and governance
Identity
- AWS: IAM for workloads, IAM Identity Center for SSO, Cognito for application identity, KMS for keys.
- Azure: Entra ID for workforce and workload identity, Managed Identities for resources, Key Vault for secrets and keys.
Guardrails
- AWS: Organizations, Service Control Policies, Control Tower landing zones, AWS Config, Security Hub.
- Azure: Management Groups, Azure Policy and Initiatives, Azure Landing Zone, Defender for Cloud.
Why this matters to managers
- Your cloud choice should reduce time to “compliant by default”. Prefer the platform that lets your platform team bake guardrails once and then scale them to every account or subscription without custom work.
Hybrid and edge
- AWS: Outposts, Local Zones, Wavelength for 5G edge, EKS Anywhere and Snow Family for ruggedized and disconnected.
- Azure: Stack HCI, Stack Hub, Azure Arc for hybrid management and GitOps at the edge, Private MEC options with operators.
Pick based on where your compliance and latency constraints live. Arc is compelling if you have a lot of on-prem Windows and Kubernetes already.
Cost management and pricing levers
- Discount programs: AWS Savings Plans and Reserved Instances. Azure Savings Plans and Reserved VM Instances.
- Rightsizing: use metrics to set conservative requests and limits for Kubernetes, choose instance families carefully, and review storage tiers.
- Visibility: AWS Cost Explorer, CUR and Athena. Azure Cost Management + Advisor.
- Practical rule: architecture and operational discipline matter more than list prices. The right autoscaling, the right data layout, and the right instance shape can swing TCO by 30 to 50 percent on either cloud.
Developer experience and IaC
| Area | AWS | Azure | Takeaway |
|---|---|---|---|
| SDKs and CLI | AWS SDKs, aws cli | Azure SDKs, az cli | Both are mature. Pick what your team knows. |
| IaC native | CloudFormation, AWS CDK, SAM | ARM, Bicep | Bicep is pleasant for Azure. AWS CDK is excellent for higher level constructs. |
| IaC multi-cloud | Terraform, Pulumi, Crossplane | Same | Prefer one toolchain across clouds for consistency. |
| CI/CD | CodeBuild, CodePipeline | Azure DevOps and GitHub Actions | Many teams standardize on GitHub Actions either way. |
Tip: If you already invested in cdk8s for Kubernetes objects, keep using it regardless of cloud. It keeps your cluster configuration consistent across providers.
Decision framework
Use this scorecard with your team. Rate each item 1 to 5 for importance to your business, then circle the provider that fits better today.
- Identity integration with Microsoft 365 and Windows , Azure strong
- Breadth of building blocks and OSS ecosystem , AWS strong
- Kubernetes day-2 operations , slight Azure edge for convenience, AWS for deep control
- Serverless and eventing library depth , AWS edge
- Enterprise messaging and integration , Azure edge with Service Bus, AWS edge with EventBridge simplicity
- Data gravity , choose where your largest datasets live today
- AI platform fit , pick where your data scientists have the most experience
- Hybrid on-prem integration , Azure Arc for Windows estates, AWS Outposts for on-prem AWS parity
- Governance scale out , parity when you adopt each vendor’s landing zone blueprint
- Talent availability in your market , check your hiring pipeline and contractors
The provider that wins on the high-importance rows is your default. The other provider becomes your exception platform for targeted workloads.
Migration playbook in brief
- Establish guardrails first , landing zones, identity, network, logging, and cost controls.
- Pick one golden path per workload type , web API, event-driven service, batch, data pipeline.
- Modernize while moving , do not forklift VMs if containers or serverless will lower TCO and risk.
- Adopt GitOps and policy as code , prevent configuration drift.
- Measure outcomes , lead time for change, change failure rate, MTTR, and unit cost per transaction.
Common pitfalls
- Treating both clouds as identical and duplicating everything. You will double cognitive load and governance overhead.
- Lifting and shifting without rightsizing. Costs spike and reliability does not improve.
- Ignoring identity and RBAC until late. Retrofits are painful and risky.
- Underestimating data egress and movement. Data gravity wins every time.
- Skipping observability. You cannot manage what you cannot see.
Conclusion
Both AWS and Azure are excellent platforms. The right choice depends on your identity story, your data gravity, your team’s skill set, and the integrations your business needs. If you live in the Microsoft ecosystem and want strong identity and Windows alignment, Azure is a natural fit. If you want the widest set of primitives and a deep OSS and partner ecosystem, AWS is hard to beat. Either path can deliver speed, reliability, and compliance when you put solid guardrails and repeatable deployment patterns in place.
Pick consciously, invest in paved paths, and measure outcomes. That is how cloud choices translate into real business value.