Software delivery metrics for growth-stage companies

For growth-stage software companies, success depends on scaling engineering operations without losing momentum. After finding product-market fit, the next challenge is delivering new features, maintaining quality, and keeping deployments predictable while expanding the team and infrastructure.

At this stage, investors, customers, and internal stakeholders expect faster feature delivery, greater reliability, and consistent performance. But without the right metrics and tools to measure and optimize software delivery, teams risk hidden inefficiencies, slower cycles, and mounting technical debt.

The best-performing scaleups do more than just measure delivery velocity. They track how efficiently engineering effort translates into real business impact while keeping infrastructure and team performance optimized.

Why growth-stage scaleups need the right metrics

At the growth stage, scaling software delivery is about more than just pushing code faster. Engineering leaders need visibility into delivery efficiency, reliability, and team throughput to prevent bottlenecks and ensure software delivery scales with the business.

Common pain points for scaleups include:

• Slower development cycles due to increasing complexity and coordination challenges across teams.
• Wasted engineering time on slow CI/CD pipelines instead of shipping value.
• Growing deployment risks as teams move fast but lack safeguards to prevent failures.
• Infrastructure costs ballooning due to inefficient pipeline executions and redundant tests.
• Difficulty measuring engineering efficiency and aligning software delivery with business outcomes.

Without data-driven insights, teams at this stage struggle to maintain a balance between velocity, reliability, and scalability.

Key software delivery metrics for growth-stage companies

1. Cycle time (overall engineering velocity)

How long does it take for an idea to go from inception to production?

• In growth-stage companies, cycle time is a leading indicator of engineering efficiency.
• If cycle time increases, it signals friction in development, testing, or release processes.
• Ideal state: Faster cycles mean teams are delivering value frequently and predictably.

How to improve:
✅ Reduce PR review bottlenecks with automated code checks and well-defined approval processes.
✅ Optimize CI/CD workflows to shorten the time from commit to deployment.
✅ Increase developer autonomy by automating repetitive manual tasks.

🚀 How CircleCI helps: Workflow duration insights track CI/CD’s impact on cycle time, identifying bottlenecks and helping teams speed up builds, tests, and deployments.

2. Time to first commit (developer ramp-up & productivity)

How long does it take for a new developer to contribute production code?

• As teams scale, onboarding new engineers quickly is critical to maintaining momentum.
• A long time to first commit suggests unclear documentation, overly complex environments, or too much setup friction.
• Ideal state: Engineers should be shipping meaningful contributions within their first few days.

How to improve:
✅ Standardize developer environments using containers and reproducible builds.
✅ Automate onboarding processes with self-serve documentation and streamlined access control.
✅ Provide clear, well-documented CI/CD pipelines so new developers can deploy confidently.

🚀 How CircleCI helps: Shareable workflows and centralized administration features let teams self-serve reliable pipelines, making it easy for new developers to onboard and contribute quickly.

3. PR review time (code collaboration & merge speed)

How long do pull requests sit waiting for review?

• At the growth stage, slow code reviews create major bottlenecks in feature delivery.
• Long review times mean developers get pulled off tasks, losing context and velocity.
• Ideal state: Code reviews should be completed within hours, not days, without sacrificing quality.

How to improve:
✅ Automate static analysis, security checks, and linting to reduce human review effort.
✅ Set SLOs for review time (e.g., “PRs should be reviewed within 4 hours”).
✅ Enable async reviews and smaller PRs to encourage faster turnaround.

🚀 How CircleCI helps: Automated testing and code validation reduce manual review burden, allowing teams to merge faster.

4. Deployment frequency (release cadence & efficiency)

How often is the team successfully shipping code to production?

• Growth-stage companies must move fast without accumulating risk in big-batch releases.
• If deployments are infrequent, teams risk longer cycle times, higher failure rates, and slower iteration.
• Ideal state: Deployments happen multiple times per day for fast iteration with low-risk changes.

How to improve:
✅ Encourage small, incremental changes instead of big-batch releases.
✅ Implement feature flags to separate deployment from release activation.
✅ Automate deployment approvals for low-risk changes to increase speed.

🚀 How CircleCI helps: Tracks project throughput to help teams benchmark and improve release velocity.

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5. Success Rate (CI/CD Reliability & Stability)

How often do builds, tests, and deployments succeed on the first attempt?

• Frequent failures signal unstable test environments, flaky tests, or misconfigured pipelines.
• A low success rate means wasted developer time debugging issues instead of shipping code.
• Ideal state: CI/CD success rate above 90% ensures teams can trust automation.

How to improve:
✅ Identify and fix flaky tests that cause unnecessary failures.
✅ Improve environment consistency across dev, staging, and production.
✅ Reduce test execution time to ensure fast, stable feedback loops.

🚀 How CircleCI helps: Provides visibility into pipeline success rates and automatically flags flaky tests, helping teams fix instability issues.

6. Mean time to recovery (MTTR) from failures

When something breaks, how fast can the team fix it and redeploy?

• Growth-stage companies can’t afford long outages. Failures must be detected, resolved, and redeployed quickly.
• A high MTTR means incident response is inefficient, leading to longer downtimes.
• Ideal state: Failures are detected and fixed within hours, not days.

How to improve:
✅ Automate rollback mechanisms to revert quickly after a failed deployment.
✅ Improve observability — logs, monitoring, and test reports should surface failures instantly.
✅ Ensure on-call teams have automated playbooks for faster resolution.

🚀 How CircleCI helps: MTTR tracking helps teams measure and reduce the time between failure detection and successful redeployment. AI-powered insights give teams actionable information they can useto resolve issues quickly.

Start tracking & improving your software delivery metrics

Fast-growing companies can’t afford to rely on guesswork when it comes to software delivery. Tracking the right metrics helps teams scale engineering operations without slowing down. But measuring performance is just the first step. Acting on those insights is what drives real impact.

With CircleCI, growth-stage teams can accelerate deployments, improve developer efficiency, and ensure that software delivery keeps pace with business growth. From workflow insights to automated testing and release orchestration, CircleCI provides the tools needed to optimize CI/CD and move faster with confidence.

Now is the time to build a high-performing, data-driven engineering organization. Start tracking and improving your software delivery today.

🚀 Sign up for a free CircleCI account to start improving lead time, deployment frequency, and reliability.

🚀 Talk to our sales team for a CI/CD solution that scales with your company’s growth.

🚀 Explore case studies and see how top startups and scaleups optimize software delivery with CircleCI.