Calculate Your ROI

See how much your team could save by improving delivery performance

Your Team

Currency

Number of developers

Avg fully loaded cost per dev ($/yr)

Your Current DORA Metrics

Deployment Frequency

per week

How often you deploy to production

Lead Time for Changes

days

Commit to production

Change Failure Rate

% of deployments causing failures

Mean Time to Recovery

hours

Avg time to restore service

Expected Improvement

Percentage improvement in delivery metrics across your team

Annual Savings

Equivalent New Devs

Hours Recovered / Year

Savings Breakdown

How we calculate this

Faster lead time $0

Higher deploy frequency $0

Lower change failure rate $0

Shorter MTTR $0

Net annual savings $0

Net of Positron Flux subscription cost

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Calculation Methodology

How we estimate the return on investment from improving software delivery performance

Foundational Constants

All calculations use the following base values derived from your inputs:

\[H_{\text{annual}} = 1{,}880 \text{ hours (47 weeks} \times \text{40 hours)}\] \[C_{\text{hour}} = \frac{S_{\text{annual}}}{H_{\text{annual}}}\] \[D_{\text{annual}} = f_{\text{deploy}} \times 52\]

Where $S_{\text{annual}}$ is the fully loaded annual cost per developer, $f_{\text{deploy}}$ is weekly deployment frequency, and $n$ is team size.

Default Salary Assumptions

Currency	Default	Basis	Source
$ USD	$195,000	US average software engineer total compensation (base + benefits + overhead × 1.3)	Bureau of Labor Statistics, 2024
£ GBP	£125,000	London average software engineer fully loaded cost	Glassdoor UK, 2024
€ EUR	€110,000	Western Europe average software engineer fully loaded cost	Glassdoor EU, 2024

Fully loaded cost includes base salary, benefits, payroll taxes, equipment, and office/infrastructure overhead, estimated at 1.3× base salary.

Default DORA Metric Assumptions

Metric	Default	DORA Benchmark (Medium)
Deployment Frequency	2 per week	Between once per week and once per month
Lead Time	5 days	Between one week and one month
Change Failure Rate	15%	16%–30%
MTTR	4 hours	Less than one day

Sources: DORA State of DevOps Report 2024; Forsgren et al., Accelerate (2018)

1. Lead Time Savings

Lead time measures the elapsed time from first commit to production deployment. Excess lead time generates context switching overhead, which research estimates consumes approximately 20% of the waiting period for each developer actively working on the change (Weinberg, IEEE Software, 2017).

\[W = t_{\text{lead}} \times 8\] \[C_{\text{switch}} = W \times 0.20\] \[\text{Savings}_{\text{lead}} = C_{\text{switch}} \times D_{\text{annual}} \times n \times C_{\text{hour}} \times p\]

Sources: DORA State of DevOps Reports, 2019–2024; Weinberg, "Quality Software Management", IEEE Software

2. Deployment Frequency Savings

Lower deployment frequency produces larger change batches. Research demonstrates a superlinear relationship between batch size and integration defect rate (Humble & Farley, 2010). Each integration cycle with a large batch incurs an estimated overhead of 4 developer hours for merge resolution, integration testing, and release coordination.

\[O_{\text{batch}} = \max(0,\; 5 - f_{\text{deploy}}) \times 4\] \[\text{Savings}_{\text{freq}} = O_{\text{batch}} \times 52 \times C_{\text{hour}} \times n \times p\]

Sources: Humble & Farley, Continuous Delivery (Addison Wesley, 2010); DORA State of DevOps Reports

3. Change Failure Rate Savings

Change failure rate measures the proportion of deployments resulting in degraded service. Each failure event triggers investigation, rollback, and remediation. Industry incident response data indicates an average of 3 developer hours per failure event across the responding team (PagerDuty, 2022).

\[F_{\text{annual}} = D_{\text{annual}} \times \frac{r_{\text{cfr}}}{100}\] \[H_{\text{rework}} = F_{\text{annual}} \times 3\] \[\text{Savings}_{\text{cfr}} = H_{\text{rework}} \times C_{\text{hour}} \times n \times p\]

Sources: DORA State of DevOps Report 2023; PagerDuty State of Digital Operations, 2022

4. MTTR Savings

Mean time to recovery quantifies the duration from incident detection to service restoration. During recovery, an average of 2 developers are engaged in diagnosis and resolution. The annual incident count is derived from the change failure rate and deployment frequency inputs (Google SRE Workbook, 2018).

\[I_{\text{annual}} = D_{\text{annual}} \times \frac{r_{\text{cfr}}}{100}\] \[H_{\text{recovery}} = I_{\text{annual}} \times t_{\text{mttr}} \times 2\] \[\text{Savings}_{\text{mttr}} = H_{\text{recovery}} \times C_{\text{hour}} \times p\]

Sources: DORA State of DevOps Reports; Google SRE Workbook (O'Reilly, 2018)

5. Aggregate Metrics

\[\text{Total Savings} = \sum_{i=1}^{4} \text{Savings}_i\] \[\text{Net Savings} = \text{Total Savings} - C_{\text{subscription}}\] \[\text{Equivalent Developers} = \frac{\text{Net Savings}}{S_{\text{annual}}}\] \[\text{Hours Recovered} = \frac{\text{Net Savings}}{C_{\text{hour}}}\]

$C_{\text{subscription}}$ is the annual Positron Flux platform cost, calculated internally.

These estimates are derived from published research and industry benchmarks cited above. Actual outcomes will vary depending on team composition, technology stack, organisational context, and implementation approach. The DORA metrics framework is maintained by the Google Cloud DevOps Research and Assessment programme. All referenced publications are available through their respective publishers. Default values represent median industry figures and should be adjusted to reflect your organisation's specific circumstances.