Every engineering leader knows the four DORA metrics by heart: deployment frequency, lead time for changes, change failure rate, and mean time to recovery. They're the industry standard for measuring DevOps performance. Teams track them religiously. Executives demand quarterly improvements.
But here's the uncomfortable truth: most organizations have no idea how to systematically improve these metrics.
They measure. They dashboard. They set OKRs. And then they hit a wall, because the metrics expose problems their tooling was never designed to solve.
The four DORA metrics aren't just performance indicators. They're a diagnostic tool that reveals whether your infrastructure is helping or actively sabotaging your engineering velocity. And for most teams operating in complex cloud environments, the diagnosis is: self-sabotage.
Why DORA Metrics Stall in Cloud-Native Environments
The original DORA research identified what separates elite performers from everyone else. Elite teams deploy multiple times per day, have lead times measured in hours, keep change failure rates below 15%, and recover from incidents in under an hour.
Most teams aren't even close.
It's not because they lack talent or ambition. It's because cloud-native infrastructure introduced complexity that traditional DevOps practices can't handle:
- Manual infrastructure changes create bottlenecks that slow deployment frequency
- Configuration drift between what's documented and what's live increases lead time
- Lack of infrastructure visibility across multi-cloud environments drives up change failure rates
- Undocumented dependencies make recovery a guessing game that extends MTTR
You can't improve what you can't control. And most teams have lost control of their cloud infrastructure. They just don't realize it yet.
The Infrastructure Automation Gap
Here's the pattern we see repeatedly: teams adopt infrastructure-as-code, implement CI/CD pipelines, embrace DevOps culture, and their DORA metrics barely budge.
Why? Because IaC coverage is aspirational, not actual.
Engineers spin up resources in cloud consoles "just for testing." Someone manually tweaks a security group during an incident. A contractor provisions infrastructure that never gets codified. Six months later, nobody knows what exists, why it exists, or what will break if they touch it.
And it directly impacts every DORA metric.
How Cloud Complexity Kills Each DORA Metric
Let's break down how infrastructure chaos sabotages performance:
Deployment Frequency: Death by a Thousand Manual Steps
Elite teams deploy multiple times per day. But that requires infrastructure changes to be as automated as application deployments.
What actually happens:
- Developers wait for platform teams to provision resources
- Manual review processes create bottlenecks
- Fear of breaking things slows everything down
- Infrastructure changes require tribal knowledge and manual validation
The fix: Self-service infrastructure with built-in guardrails. When developers can provision compliant resources without writing IaC or waiting for tickets, deployment velocity increases naturally.
Lead Time for Changes: The Drift Tax
Lead time measures how long it takes to go from code commit to production. But in cloud environments, infrastructure drift adds hidden delays that never show up in your CI/CD metrics.
What actually happens:
- Drift between live state and IaC forces manual reconciliation
- Policy violations discovered late in the deployment process
- Time wasted tracking down who made undocumented changes
- Configuration issues that could have been caught early derail deployments
Customer evidence: AppsFlyer saved over 200 hours of engineering time by automatically codifying their manually-created infrastructure—time that was previously spent reconciling drift and reverse-engineering configurations.
Change Failure Rate: When You Can't See What You're Breaking
Change failure rate spikes when teams lack comprehensive visibility into their infrastructure dependencies and real-time state.
What actually happens:
- Teams deploy changes without knowing what else might break
- Configuration drift creates unexpected side effects
- Manual processes introduce human error
- No way to validate compliance before deployment
Plus, did you know? Firefly has detected over 512,000 drift events across its customer base, preventing an estimated $3.8M in annual costs from configuration-related incidents.
Mean Time to Recovery: The Infrastructure Knowledge Problem
When incidents happen, MTTR depends on how quickly you can understand what changed, why it matters, and how to fix it.
What actually happens:
- No single source of truth for infrastructure state
- Manual troubleshooting across multiple cloud consoles
- Unclear ownership of resources
- Recovery requires reconstructing context from Slack threads
The fix: Complete asset inventory, change history, and automated remediation turn alerts into actionable fixes: CLI commands or automated PRs instead of war rooms.
The Possibilities with Firefly: From Reactive Measurement to Systematic Improvement
Here's what actually moves DORA metrics for teams operating at cloud scale, and what you can do with Firefly:
1. Achieve Real IaC Coverage (Not Aspirational Coverage)
Most teams think they have high IaC coverage because their repositories contain Terraform. But actual coverage means every cloud resource—including the ones created manually—is codified and tracked.
What this enables:
- Automated infrastructure deployments without manual reconciliation
- Policy enforcement at provision time, not discovery time
- Drift detection that catches deviations before they cause incidents
2. Make Infrastructure Changes Self-Service (With Guardrails)
Deployment frequency doesn't improve by telling developers to learn Terraform. It improves by giving them self-service infrastructure with built-in compliance.
What this enables:
- Developers provision resources without platform team bottlenecks
- Security validation, policy enforcement, and cost estimation happen automatically
- Infrastructure changes flow through the same CI/CD pipelines as application code
3. Automate Drift Detection and Remediation
Configuration drift is a tax on every DORA metric. It increases lead time, drives up change failure rates, and extends MTTR during incidents.
What this enables:
- Real-time monitoring of live environment vs. IaC-defined state
- Automated correction of deviations before they impact production
- AI-powered remediation that generates fixes instead of just flagging problems
The teams with the best DORA metrics don't tolerate drift; they've automated it out of existence.
4. Build a Single Source of Truth for Infrastructure
You can't improve MTTR if troubleshooting starts with "does anyone know why this resource exists?"
What this enables:
- Complete cloud inventory across multi-cloud, Kubernetes, and SaaS environments
- Audit trails showing who changed what and when
- Integration with ITSM tools for faster incident resolution
Finally, you can stop wasting time reconstructing context during incidents.
Why Platform Engineering Is the DORA Multiplier
Gartner recognized Firefly as a Cool Vendor in Platform Engineering for a reason: abstracting infrastructure complexity is what enables high DORA metrics at scale.
Platform engineering isn't about adding another layer to your stack. It's about creating the foundation that makes improvement possible:
- Measurement: Real-time visibility into what exists and what's changing
- Automation: Workflows that eliminate manual toil and enforce compliance
- Reliability: Proactive management that prevents issues before they impact production
- Velocity: Self-service with governance that removes bottlenecks without sacrificing control
Teams using platform engineering approaches report 40% improved CloudOps efficiency: not through heroic effort, but through systematic elimination of manual processes.
Stop Measuring. Start Improving.
DORA metrics are useful because they diagnose problems. But diagnosis without treatment is just expensive dashboards.
If your metrics have plateaued (read: if you're measuring, but not improving) the problem isn't your people or your processes. It's that your infrastructure tooling was designed for a simpler era and probably can't handle the complexity of modern cloud environments.
The teams achieving elite DORA performance aren't working harder. They've automated the infrastructure management that was sabotaging their velocity. They've eliminated drift, visibility gaps, and manual processes that made improvement impossible.
You can't improve what you can't control. And you can't control what you can't see.
The question isn't whether to improve your DORA metrics. It's whether you'll address the infrastructure complexity that's preventing improvement, or keep measuring your way to mediocrity.
