What Are Deployment Frequency?
A DORA metric that measures how often your team successfully releases code to production. Elite teams deploy on-demand, multiple times per day.
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What it means
Deployment frequency is one of the four DORA metrics and measures the cadence at which your team ships code to production. It's a proxy for batch size — teams that deploy frequently are shipping smaller changes, which are easier to review, test, and roll back. The metric doesn't count deploys to staging or development environments, only production releases. Deployment frequency is strongly correlated with lead time: teams that deploy more often typically have shorter lead times because they're not batching up weeks of work into a single release. Google's DORA research found that elite teams deploy on-demand (multiple times per day), while low performers deploy less than once per month. The metric is typically pulled from CI/CD pipeline data, git tags, or deployment tracking tools.
Why Deployment Frequency matter
Deployment frequency is the single best indicator of engineering velocity that non-technical stakeholders can understand. When a CTO tells the board 'we deploy 15 times per week,' that's a concrete measure of shipping speed. For engineering managers, low deployment frequency signals process bottlenecks: long-running branches, slow CI pipelines, manual QA gates, or change advisory boards that batch approvals. Improving deployment frequency forces teams to solve these underlying issues, which improves the entire delivery pipeline. Teams that deploy frequently also have lower change failure rates because each deployment contains fewer changes, making failures easier to isolate and fix.
How to measure
Count the number of successful production deployments per time period (daily, weekly, or monthly). Pull this data from your CI/CD system (GitHub Actions, GitLab CI, Jenkins), deployment platform (Vercel, AWS CodeDeploy, Kubernetes), or git tags. Exclude failed deployments and rollbacks from the count. For monorepos, decide whether to count per-service or per-repository deployments. DORA benchmarks: Elite = on-demand (multiple per day), High = weekly to monthly, Medium = monthly to every 6 months, Low = less than once every 6 months.
Real-world example
An engineering team deploys their main API service twice per month through a manual release process: a developer creates a release branch on Monday, QA tests it for 3 days, and it ships Thursday. After switching to trunk-based development with feature flags and automated testing, they deploy 3-4 times per day. Each deployment is a single merged PR. Their change failure rate drops from 8% to 2% because each deployment is smaller and easier to verify.
Related terms
Common questions
What is a good deployment frequency?
According to DORA research, elite teams deploy on-demand (multiple times per day). High-performing teams deploy between once per day and once per week. If your team deploys less than once per month, there's significant room for improvement. The right target depends on your context — a consumer SaaS app should aim for daily deploys, while embedded systems might target weekly.
Does deploying more often increase risk?
Counter-intuitively, no. DORA research consistently shows that higher deployment frequency correlates with lower change failure rates. Each deployment contains fewer changes, making it easier to catch issues in review, test effectively, and roll back if something breaks. The riskiest deployments are large, infrequent releases with weeks of accumulated changes.
How do you increase deployment frequency?
Start with three changes: adopt trunk-based development (short-lived branches merged daily), invest in automated testing (unit + integration tests that run in CI), and use feature flags to decouple deployment from release. This lets you deploy code to production without exposing unfinished features to users. Most teams can go from monthly to weekly deploys within a quarter.
Should hotfixes count toward deployment frequency?
Yes. All production deployments count, including hotfixes. However, if a large percentage of your deployments are hotfixes, that indicates a change failure rate problem. Track hotfix deployments separately so you can see the ratio of planned vs. emergency deployments.
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