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AWS vs Azure
Choosing a cloud platform by Kacper Michalik—AWS Certified, comparing scale, hybrid tooling, and fit.
AWS and Azure both cover the same broad ground—compute, storage, databases, and AI/ML services—but they optimize for different organizations. AWS, launched in 2006, remains the largest provider by IaaS/PaaS market share, with the broadest service catalog and deepest third-party ecosystem. Azure, launched in 2010, is strongest where an organization already runs on Microsoft infrastructure—Active Directory, Office 365, Windows Server—and needs tight, native integration with it. Neither platform is categorically better; the right one depends on existing infrastructure, compliance needs, and team familiarity.
| Criteria | AWS | Azure |
|---|---|---|
| Market position | Largest IaaS/PaaS provider by share | Strong in Microsoft-invested enterprises |
| Serverless execution limit | Lambda: up to 15 min | Functions: 5 min default, up to 10 |
| AI/ML tooling | Broad, often favored for custom ML pipelines | Comparable breadth |
| Hybrid/multi-cloud | Open standards (Outposts, Local Zones, EKS Anywhere) | Azure Arc, deep hybrid investment |
| Best fit | Portability, global reach, service breadth | Microsoft-stack integration, compliance-heavy orgs |
AWS
Use it when: Teams that value open standards and infrastructure portability, need global reach, or want the broadest selection of compute and AI/ML services—including custom silicon (Graviton, Inferentia, Trainium).
Widest service breadth and the deepest third-party ecosystem, with Lambda functions running up to 15 minutes. The tradeoff: less native benefit for organizations already standardized on Microsoft tooling.
Azure
Use it when: Organizations already invested in Microsoft technology—Entra ID, Office 365, Windows Server—or with strict regulatory/compliance frameworks and hybrid infrastructure needs.
Tight native integration with the Microsoft stack and strong hybrid/multi-cloud tooling via Azure Arc. The tradeoff: Azure Functions on the Consumption plan default to a 5-minute timeout (configurable to 10) versus AWS Lambda’s 15 minutes, and the ecosystem outside Microsoft-native tooling is comparatively narrower.
Verdict
For product teams building without a prior Microsoft dependency, AWS’s breadth of managed services and global infrastructure footprint make it the more flexible default—it is the platform behind the cloud architecture and AI/ML certifications underpinning the work described across this site. That said, "which cloud" is rarely a technology question alone: an organization already running on Active Directory and Windows Server will get more value from Azure’s native integration than from AWS’s broader catalog. Evaluate against existing infrastructure and compliance constraints before defaulting to either.
Originally published on Built In, expanded here with production context.
Frequently Asked Questions
Is AWS better than Azure?
Not categorically—AWS has the broadest service catalog, largest market share, and strongest open-standards/portability story. Azure wins for organizations already invested in Microsoft technology (Active Directory, Office 365, Windows Server) or with hybrid and compliance-heavy requirements. The right choice depends on existing infrastructure, not a universal ranking.
Which cloud is better for AI/ML workloads?
Both platforms offer comparable breadth of AI/ML services. AWS’ tooling is often favored for advanced, custom machine learning pipelines and includes custom silicon (Graviton, Inferentia, Trainium) purpose-built for ML workloads.
What is the biggest practical difference for serverless workloads?
Execution time limits: AWS Lambda functions can run up to 15 minutes, while Azure Functions on the Consumption plan default to 5 minutes (configurable up to 10). This can be a deciding factor for longer-running serverless jobs.
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