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AWS vs Azure for a growing product team: how to choose in 2026 How to choose between AWS and Azure for a product team of five to fifty: identity, managed services, AI, pricing levers, skills, and what actually decides it.
A product team has outgrown its first server. There are now three environments, a growing database, a customer asking about data residency and a board member who says everyone uses AWS. The CTO's previous company was on Azure. Both clouds can run the product. The question is which one will be cheaper and easier to operate for the next three years.
The short answer to AWS vs Azure: for most growing product teams, choose AWS if you want the widest choice of services and the largest pool of engineers who know it. Choose Azure if your organisation already runs on Microsoft 365 and Entra ID, sells to Microsoft-centric enterprises, or depends on Azure OpenAI. Technical capability is rarely the deciding factor.
We run client workloads on both, from EC2 and Lightsail servers to Azure-hosted AI and RAG systems. Here is how we help teams decide.
The building blocks map closely For the services a typical web or SaaS product uses, the two clouds have direct equivalents. The names differ more than the capabilities:
Virtual machines: EC2 on AWS, Azure Virtual Machines on Azure. Run a container without managing servers: ECS on Fargate, or Azure Container Apps. Managed Kubernetes: EKS, or AKS. Managed PostgreSQL and MySQL: RDS and Aurora, or Azure Database for PostgreSQL and MySQL Flexible Server. Object storage: S3, or Azure Blob Storage. Functions: Lambda, or Azure Functions. Secrets: Secrets Manager or Parameter Store, or Azure Key Vault. Infrastructure as code: CloudFormation and CDK, or Bicep. Terraform and OpenTofu work with both. Hosted foundation models: Amazon Bedrock, or Azure OpenAI within Microsoft Foundry. If your product is an API, a frontend, PostgreSQL, Redis, object storage and a queue, either cloud will run it well. So the decision comes down to the areas below.
Identity and organisation structure This is where the two feel most different day to day, and where early mistakes are hardest to undo.
On AWS, the unit of isolation is the account. A sensible setup uses AWS Organizations with separate accounts for production, staging and shared services, and IAM Identity Center for people to sign in. Permissions are IAM policies, which are precise but verbose. Many small teams skip this structure and put everything in one account with long-lived access keys. That works until it does not.
On Azure, the hierarchy is tenant, management groups, subscriptions and resource groups. Identity is Microsoft Entra ID, the same directory that runs your Microsoft 365 sign-in. If your staff already log in to Outlook and Teams with Entra ID, giving them access to Azure is a role assignment, and leavers lose cloud access when HR disables their account. Resource groups also make it easy to see and delete everything belonging to one environment.
If your company uses Google Workspace or another identity provider, this advantage shrinks. Both clouds support single sign-on from external providers.
AI services and the Microsoft factor A few years ago, Azure OpenAI was a strong reason on its own to choose Azure. The gap has narrowed. Amazon Bedrock offers several model families, including Anthropic's Claude and Amazon's own Nova models, and Microsoft Foundry now hosts models from multiple providers alongside OpenAI's. What matters now is narrower:
Is the specific model you need available in the region where your data must stay? Check the model availability tables, not the marketing page. Do your enterprise customers' security teams already approve one provider's AI terms? That can save months of procurement. Does your retrieval layer fit? Azure AI Search pairs naturally with Azure OpenAI, while on AWS teams often use OpenSearch, Aurora PostgreSQL with pgvector, or a separate vector database. Beyond AI, Azure has practical advantages if you run Windows Server, SQL Server or .NET workloads. Azure Hybrid Benefit lets you apply existing Windows Server and SQL Server licences with Software Assurance, which can change the cost comparison substantially. If you sell into large organisations that standardise on Microsoft, being in the same cloud and marketplace can also simplify their buying process.
What drives the bill List prices for comparable virtual machines and managed databases are close enough that they should not decide the choice. The bill is decided by how you use the cloud. The same things inflate costs on both:
Idle environments: staging and preview environments running all night and all weekend. Oversized instances chosen on day one and never revisited. Outbound data transfer and cross-zone traffic, which neither cloud makes obvious until the invoice arrives. Managed NAT: NAT gateways on both clouds charge per hour and per gigabyte processed. Public IPv4 addresses, which AWS charges for hourly and Azure charges for on Standard public IPs. Snapshots and backups kept forever with no retention policy. Both clouds offer one- and three-year commitments for steady workloads: Savings Plans and Reserved Instances on AWS, Azure savings plan for compute and Reservations on Azure. Both offer spare-capacity pricing for interruptible work: Spot Instances and Azure Spot Virtual Machines. Commit only after a few months of stable usage data, never in the first month.
Startup credits from AWS Activate or Microsoft for Startups can cover a lot of early spend. Treat them as a runway extension, not a reason to choose. Credits expire. Architecture and team habits do not.
Whichever you choose, tag resources by environment from day one and check spend by service every month. These commands show who you are signed in as and where the money went:
# AWS: confirm the account, then last month's cost by service
aws sts get-caller-identity
aws ce get-cost-and-usage \
--time-period Start=2026-09-01,End=2026-10-01 \
--granularity MONTHLY --metrics UnblendedCost \
--group-by Type=DIMENSION,Key=SERVICE
# Azure: confirm the subscription, then list resource groups and tags
az account show --output table
az group list --query "[].{name:name, env:tags.env}" --output tablePeople, skills and regions The cloud your team already knows is usually cheaper than the cloud that is slightly cheaper on paper. A team fluent in AWS will build a safer Azure setup slowly, and the reverse is equally true. When hiring, AWS experience is more common in the startup and product market. Azure experience is common among engineers from enterprise and .NET backgrounds.
Check regions early if data location matters to customers. For India, AWS has Mumbai and Hyderabad regions, and Azure has Central India, South India and West India. Not every service or model is available in every region, so confirm the exact services you plan to use before you commit, especially managed databases and AI models.
Also look at support. Both providers' free tiers of support are limited to billing and documentation. If you want technical help from the provider, that is a paid support plan on either side, so budget for it or for a partner who knows your setup.
Avoiding lock-in where it is cheap to do so You do not need to be multi-cloud. Running the same product on two clouds roughly doubles the operational work for little benefit at this stage. You do want the option to move later without a rewrite. The cheap habits are:
Package services as containers, so they run on ECS, Container Apps, Kubernetes or a plain VPS. Use standard engines such as PostgreSQL and Redis rather than proprietary databases for core data. Write infrastructure in Terraform or OpenTofu, kept in your own repository. Access object storage through an S3-compatible client where possible, or behind a thin storage module in your code. Keep cloud-specific services such as queues and AI calls behind small interfaces, so swapping them touches one file. Accept lock-in where it gives real value. A managed database with automated backups and point-in-time restore is worth being tied to. A proprietary workflow engine for something a cron job could do probably is not.
A quick decision checklist Your company runs on Microsoft 365 and Entra ID, or sells to Microsoft-heavy enterprises: lean Azure. You run Windows Server, SQL Server or .NET and hold licences with Software Assurance: lean Azure. You need the widest set of managed services, or plan to hire engineers from the startup market: lean AWS. Your team already knows one cloud well: stay with it unless a clear requirement says otherwise. A specific AI model or region is non-negotiable: confirm availability on both before deciding. Whichever you pick: separate production from everything else, use single sign-on instead of shared keys, turn on budget alerts, and keep infrastructure in code owned by your organisation. If the team has no one with time to own identity, networking, backups and cost reviews, that gap matters more than the provider. It is a reasonable point to bring in help, whether that is a contractor for the initial setup or an ongoing managed arrangement where coverage hours and response times are agreed in the retainer.
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