An engineering team at PhonePe builds a simple internal web tool, deploys it on a full virtual machine they now have to patch and monitor forever, when an App Service instance would have handled it with zero server management and automatic scaling. This pillar covers Azure's full compute spectrum - from full-control virtual machines to fully managed platforms - so the compute model is chosen based on the actual workload, not habit.
What This Pillar Covers
- Choosing between Virtual Machines, App Service, Containers, and Functions for a given workload
- Designing VM resilience with Availability Sets and Availability Zones
- Scaling automatically with Virtual Machine Scale Sets based on real load
- Deploying zero-downtime releases using App Service Deployment Slots
- Reducing real-world compute costs with auto-shutdown, Spot VMs, and Reserved Instances
- Building and pushing container images to Azure Container Registry
- Choosing between Azure Container Instances and Azure Kubernetes Service for containerized workloads
Who This Is For
Cloud administrators, DevOps engineers, and backend developers responsible for deploying applications, choosing the right compute service for a workload's actual traffic and control requirements, and keeping compute costs proportional to real usage.
Why This Matters in Production
During a flash sale, a CRED-style fintech app needs to handle a sudden ten-fold spike in traffic without over-provisioning capacity that sits idle the rest of the year - this is exactly what Scale Sets and App Service autoscaling solve. Meanwhile, a forgotten test VM left running without auto-shutdown can quietly cost as much as a small production workload, simply because nobody remembered to turn it off after business hours.
Prerequisites
- Completion of Azure Fundamentals and Governance, or equivalent familiarity with Resource Groups and RBAC
- Basic understanding of Linux or Windows server administration
- Familiarity with containers and Docker is helpful for the AKS and ACI topics