Our recent benchmarks reveal surprising performance gaps between Azure and AWS that directly impact development team productivity.

Why Deployment Speed Matters

Impact on Team Velocity

Deployment speed is not just a technical metric—it is a determining factor in your teams’ productivity. Every minute spent waiting during an infrastructure deployment translates to:

• Interrupted development flow: Developers lose focus and context.
  
• Slower feedback cycles: More time passes between code changes and real-world testing.
  
• Team frustration: Waiting generates friction in DevOps processes.
  
• Opportunity cost: Time that could be spent innovating is instead spent waiting.

The Role of DORA Metrics

DevOps Research and Assessment (DORA) shows that high-performing teams stand out for their ability to deploy frequently and quickly. DORA metrics are a popular method for measuring deployment speed and stability. Infrastructure provisioning speed directly influences two key metrics:

• Lead Time for Changes: Time from commit to production.
• Deployment Frequency: How often the team can deploy.

AWS: Continuous Performance Innovation

Recent CloudFormation Improvements

AWS actively invests in optimizing deployment performance. In March 2024, AWS introduced an optimistic stabilization strategy that improved CloudFormation deployment times by up to 40%.

This optimization works by:

• Parallelizing resource creation: Dependent resources can be created simultaneously.
  
• Introducing the CONFIGURATION_COMPLETE event: Better visibility into provisioning status.
  
• Optimizing dependency management: Differentiating between creation and stabilization of resources. 

AWS split the resource creation process into two phases (creation and stabilization), allowing other resources in the stack to be created earlier.

Performance-Focused Approach

These improvements reflect AWS’s proactive approach: identifying deployment bottlenecks and optimizing them. The impact is immediate and transparent for users.

Microsoft notes that creating and activating an APIM instance normally takes 30–40 minutes, but can sometimes take longer due to other factors.

Recurring Problem Patterns

Several recurring issues emerge from user feedback:

• Slow ARM templates: Deployments take about 30 seconds even if no infrastructure changes are made. This consistent latency accumulates quickly in CI/CD workflows.
  
• Regional bottlenecks: Performance issues in Western Europe where ARM reached operational limits, causing deployment delays.
  
• Pipeline timeouts: APIM deployments via ARM template often exceed 60 minutes, causing Azure DevOps pipeline timeouts.

Impact on Developer Experience

A developer shared in 2019: “Sometimes I think unboxing a computer, installing the OS, then deploying the app manually would be faster than this.” This frustration highlights a systemic problem.

Business Impact: Beyond Technical Considerations

Cost of Delays

For a 10-developer team using Azure:

• 5 deployments per day with 15 minutes extra waiting compared to AWS
  
• 75 minutes of waiting per day for the team
  
• ~325 hours lost per year
  
• Estimated cost: $32,500 annually in developer time (at $100/hour)

Impact on Innovation

Deployment delays affect experimentation. When testing an idea takes 30 minutes instead of 5, teams:

• Test fewer hypotheses
  
• Bundle changes (reducing agility)
  
• Lose responsiveness to business needs

AWS CloudFormation, with recent optimizations offering up to 40% performance improvement, demonstrates a product-focused approach centered on the developer experience. By contrast, Azure ARM continues to show delays that can turn simple deployments into long marathons of waiting.

For DevOps teams, the message is clear: evaluate not only the features of your cloud tools but also their impact on development velocity. In the race for innovation, every minute of waiting is a minute less spent creating value.