Pick AWS when you want the broadest catalog of infrastructure services, a cloud-native engineering culture, and a deep ecosystem of tooling and engineers who know it. Pick Azure when your company runs on Microsoft: Entra ID for identity, Microsoft 365, Windows Server and SQL Server licenses, .NET applications and Power BI or Fabric for analytics. Both cover compute, data and AI well, so the decision usually follows identity, licensing and existing skills more than feature lists.
Most cloud security incidents trace back to identity and permissions. AWS IAM is powerful and granular, but writing and reviewing least-privilege policies takes skill. Azure ties cloud permissions to the same Entra ID directory that controls email and laptops, which simplifies joiner-mover-leaver processes for Microsoft shops. Both work with external identity providers like Okta. Choose the cloud whose identity model your security team can govern confidently.
List prices for comparable compute and storage are close enough that they rarely decide anything. What moves the bill: license portability (often favoring Azure for Windows and SQL Server), commitment discounts you already hold, data egress between clouds, and whether your architecture uses managed services efficiently. A poorly tagged, over-provisioned environment costs more on either cloud. Put FinOps practices in place early: tagging standards, budgets and rightsizing reviews.
Until recently, the shorthand was "Azure for OpenAI, AWS for Anthropic." As of October 2026 that is no longer true: OpenAI models are generally available on Bedrock and Anthropic's Claude models are available in Microsoft Foundry. The real differences now are in the surrounding platform: guardrails, agent runtimes, evaluation tooling, regional availability of specific models and how each fits your data estate. Our AWS Bedrock vs Azure OpenAI guide goes deeper.
Applications are easy to move; data is not. If your warehouse, lakehouse or system of record already lives in one cloud, new analytics and AI workloads usually belong next to it, because egress fees, latency and duplicated access controls add up. Databricks and Snowflake run on both clouds, which softens this for teams on those platforms, but the storage accounts, networking and identity around them still tie you to one provider. Map where your most important data lives before you compare service catalogs.
Many enterprises run Microsoft 365 and Entra ID alongside AWS workloads, or keep a data platform on one cloud and applications on the other. That works. What fails is spreading every workload evenly across both without a reason, which doubles the platform skills, guardrails and networking you need. Pick a primary cloud, document when exceptions are allowed, and use Terraform where portability matters.
Ryz Labs puts senior cloud engineers from Latin America on your team, on US business hours with real-time overlap. Only the top 1% of the tens of thousands of engineers we interview make it. Our AWS developers, Azure developers and Terraform engineers build landing zones, Kubernetes platforms and migrations, and our AI pod teams build AI systems in your AWS or Azure account and ship them to production. Our engineers have experience working within standards like SOC 2, HIPAA and PCI DSS in client environments.
If you want a management-consulting engagement to pick a cloud strategy at board level, a large consultancy fits better. If you want engineers who build, talk to us.
Both run large enterprise workloads. Azure tends to fit organizations built on Microsoft identity, Windows Server, SQL Server and .NET. AWS tends to fit engineering-led, Linux and open-source heavy organizations that want the widest set of infrastructure services. Many enterprises use both, with one as the primary.
It depends on your workloads and agreements. Azure can be cheaper for Windows and SQL Server because of license portability. AWS can be cheaper where you already hold commitment discounts. Architecture and FinOps discipline usually matter more than list prices.
As of October 2026 both offer models from OpenAI and Anthropic plus other providers, with enterprise controls for networking, identity and logging. Choose based on where your data and identity already live, the specific models and regions you need, and the platform tooling your team prefers.
Use more than one cloud when there is a clear reason: a SaaS dependency, an acquisition, a data platform, or a specific service. Running everything on both clouds by default doubles your platform work without guaranteeing resilience.
Yes. Ryz engineers work on your team, in your accounts, repos and CI, under your access policies, and our AI pod teams build in your cloud rather than ours.
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