Deploying on Demand can help with your DORA goals

Many teams evaluate their processes based on isolated metrics, such as how often an individual developer commits code. These narrow evaluations often provide a skewed understanding of performance. Does it really matter how often developers commit if the quality of those commits isn't evaluated?

This is where DORA metrics come into play. Instead of focusing on individual developer output, DORA provides a research-backed framework that helps teams measure software delivery performance holistically.

What Actually Matters in Software Delivery?

DORA’s four key metrics provide a full picture of software delivery effectiveness:

• Lead Time for Changes: How long does it take for a code change to reach production?
• Change Failure Rate: How often do deployments introduce failures?
• Deployment Frequency: How frequently does the team release to production?
• Mean Time to Restore (MTTR): How quickly can the team recover from a failed deployment?

These metrics are interconnected. Shorter lead times can enable more frequent deployments. Frequent deployments may reduce the risk of each release because changes can be smaller and easier to validate. Lower-risk deployments result in fewer failures, and faster recovery times reinforce confidence in releasing more frequently. On paper, this sounds like a clear path to improvement, but many teams struggle with achieving a reliable deployment process.

Can You Deploy on Demand?

Tracking DORA metrics provides valuable insight, but they should be seen as a measure, not the goal. Focusing on improving the numbers without understanding the underlying challenges can lead to meaningless progress.

Instead of asking, "How can we deploy faster?", teams should ask, "What is slowing us down?" Metrics alone won’t reveal real obstacles such as the need for manual approvals, a team can improve deployment frequency while still struggling with bottlenecks that make releases unpredictable and stressful.

DORA metrics should guide conversations, not dictate targets. The real value comes from using them to uncover what needs improvement. A more useful question is: “If needed, could we successfully deploy on demand?”

For many teams, the answer is not a simple “yes”, and that’s where the real discussion begins. Why? Because the challenges that prevent teams from deploying on demand aren’t just about speed—they’re about reliability, confidence, and reducing friction in the release process. Whether it’s waiting for approvals, coordinating with multiple teams, or dealing with unreliable test automation, these blockers indicate deeper structural issues.

In the next sections, we’ll explore the most common challenges teams face and what can be done to overcome them.

Common Deployment Challenges and Where Teams Struggle

These real-world examples reflect common challenges teams face in improving deployments. The path forward is rarely a single fix; it’s a mix of incremental process improvements, technical changes, and cultural shifts that take time to implement.

1. Balancing Stability with Speed

The Challenge: Deployments Feel Too Big to Do Often

Some teams aren’t comfortable deploying frequently. Instead of treating deployment as a routine process, they view it as a major event that requires extensive preparation, coordination, and validation. Because deployments feel like a high-effort task, they bundle multiple changes together to minimize how often they have to go through the process.

However, this approach creates more work rather than reducing it:

• Batched deployments require larger validation efforts, since more changes need to be reviewed, tested, and approved at once.
• When issues arise, debugging is harder, as multiple changes are introduced simultaneously, making the cause less clear.
• Each deployment feels like a risk, because the process itself is long and complex, reinforcing the team’s reluctance to deploy more often.

How teams can make progress:

• Running deployments in an emulated environment accelerates feedback loops, reducing lead time for changes by allowing teams to catch issues early and refine changes before they reach production.
• Smaller, more frequent deployments decrease change failure rate by reducing the blast radius of each change.
• Eliminating manual infrastructure definition helps teams deploy more confidently, increasing deployment frequency by automating cloud resource provisioning.

2. Aligning Application and Infrastructure Requirements

The Challenge: Deployment Readiness Issues

Deployment failures often stem from infrastructure being out of sync with application changes. Services may experience unexpected runtime failures, or rollbacks may be required due to mismatched environments.

• Unclear runtime requirements lead to failures due to missing dependencies.
• Infrastructure updates are handled separately from application changes, causing version mismatches.
• Teams hesitate to update Infrastructure as Code (IaC) due to concerns about breaking production.

How teams can make progress:

• Generating infrastructure alongside application code reduces lead time for changes by ensuring cloud resources are provisioned automatically based on application needs.
• Ensuring automatic environment consistency lowers change failure rate by reducing misconfigurations between staging and production.
• Tracking how an application interacts with cloud services helps prevent deployment failures, contributing to higher deployment frequency and fewer rollbacks.

3. Reducing Bottlenecks in the Deployment Process

The Challenge: Manual Steps Slowing Down Releases

Strict compliance requirements, manual approvals, and slow infrastructure provisioning can cause deployments to take much longer than necessary.

• Manual approvals delay even small, low-risk updates.
• Security and compliance checks happen late in the process, leading to last-minute issues.
• Infrastructure provisioning requires human intervention, adding unnecessary complexity.

How teams can make progress:

• Automating policy and compliance checks in CI/CD improves lead time for changes by eliminating manual gatekeeping for routine deployments.
• Integrating feature flags allows teams to quickly mitigate issues, reducing mean time to restore (MTTR).
• Using infrastructure-aware CI/CD pipelines ensures that missing dependencies don’t block deployments, increasing deployment frequency.

Summing It Up

There is no single path to on-demand deployment. Different teams face different bottlenecks; however, being able to produce an answer for the following questions can set you up for success:

• Can infrastructure be automatically provisioned based on application needs?
  Leads to shorter lead times and higher deployment frequency

• Are security and compliance checks slowing things down?
  Reducing unnecessary manual approvals accelerates lead time for changes

• Are there low-risk automation opportunities, such as automated rollbacks?
  Faster recovery reduces mean time to restore

• Does your Infrastructure as Code align with runtime needs?
  Prevents failures, reducing change failure rate

• Can local emulation accelerate development?
  Shorter feedback loops improve lead time for changes

Improving deployment confidence is not about chasing arbitrary metrics. Progress happens when teams identify sources of deployment friction and address them through a mix of process improvements, automation, and cultural changes.

For teams working toward deploying on demand, the goal isn’t to remove every manual step overnight but to gradually reduce risk and increase confidence in each deployment.