Plan releases with clear metrics, not guesses ever. Convert raw logs into actionable cadence insights. Supports teams, services, and environments for consistent improvement ahead.
Choose a date range or enter a fixed number of days. Add optional fields for richer insights across teams, services, and environments.
Use these sample rows to sanity-check your inputs. Copy values into the form and compare your results.
| Period | Days | Deployments | Per week | MTBD (days) | Band |
|---|---|---|---|---|---|
| 2-week sprint | 14 | 20 | 10.000 | 0.70 | High |
| Monthly release cycle | 30 | 8 | 1.867 | 3.75 | High |
| Quarterly batch | 90 | 6 | 0.467 | 15.00 | Medium |
| Ad-hoc releases | 10 | 15 | 10.500 | 0.67 | High |
Deployment frequency is a core delivery signal for modern teams. Higher cadence reduces batch size and risk. Many teams track weekly and monthly rates together. This calculator converts release counts into stable rates and intervals. Use it for sprints, quarters, and release trains.
A sprint team may ship 10 to 30 times in 14 days. That equals 5 to 15 deployments per week. A monthly cycle might ship 4 to 12 times in 30 days. That equals 0.9 to 2.8 per week. Quarterly batches often fall below 0.7 per week.
Workday counting removes weekends from the period. A 14 day window usually has 10 business days. Twenty deployments then become 2.0 per business day. The same data becomes 1.43 per calendar day. Choose the rule that matches reporting and staffing.
Add successes and failures for an outcome view. Nine successes and one failure equals 90% success. Track rollbacks as failures when they impact users. Pair success rate with mean time between deployments. A fast cadence with low success may signal poor quality gates.
Multi-service programs need normalization for fair comparisons. Forty deployments across four services equals 10 per service. Ten developers shipping 20 times in 14 days equals 1.0 per developer per week. Use these ratios to spot bottlenecks and uneven ownership.
Compare last sprint to this sprint using the same period rule. Track a rolling 30 day window for seasonality control. Share CSV and PDF outputs in retrospectives. Set targets like one production deployment per day. Validate improvements after pipeline or branching changes.
Count a release that changes running software in the selected environment. Include feature, fix, and config releases. Exclude dry runs and failed builds that never reached the environment.
Include hotfixes when you want total operational load. Exclude them when you measure planned delivery cadence. If you exclude, use notes to document your rule for consistent reporting.
It shows spacing between releases within the period. Lower values usually indicate smoother flow. Very low values may stress operations without automation. Track it alongside success rate and incident volume.
Business days shrink the denominator and raise the rate. This matches staffing patterns for many teams. Use calendar days for 24/7 products. Use business days for office hour delivery teams.
Use the same time window and counting rules. Normalize by services when architectures differ. Also compare per developer per week. Add context like on-call load, compliance gates, and release windows.
Use 14 days for sprint teams and 30 days for product reporting. Use 90 days for program level trends. Keep the period stable across quarters. Avoid mixing business day and calendar day baselines.
Important Note: All the Calculators listed in this site are for educational purpose only and we do not guarentee the accuracy of results. Please do consult with other sources as well.