Every ROI calculator on the internet has the same problem: it is built to produce a big number, not an honest one. We built ours the other way around. The Software Delivery ROI calculator exists to help an engineering leader frame the size of a delivery-improvement opportunity in euros, using conservative assumptions grounded in public research, so the figure survives scrutiny from a CFO rather than collapsing under it. This article explains exactly who it is for, what data sits behind every default, and how to read what it gives you back.
Who this calculator is for
It is built for the person who has to justify investment in delivery: a Director or Head of Software Engineering, a platform or DevOps lead, a VP of Engineering or CTO, typically at an organisation with fifty or more engineers. If you are the one being asked "what would we actually get for this?" in a budget meeting, this is your tool. It is deliberately not a procurement questionnaire. It asks for six inputs you already know or can estimate in a minute, all of them phrased in the language you already report upward: team size, fully-loaded cost per engineer, and your four DORA metrics (deployment frequency, lead time for changes, change failure rate, and time to restore service).
The output is designed to be a one-page artifact you can forward to your team and your finance function without editing. That is the whole point: a defensible, sourced starting figure that opens a conversation, not a vanity number that ends one.
What it measures: three value drivers
The calculator estimates the annual value of closing the gap between your current delivery performance and a realistic target, across three drivers. We chose these three because they are the ones most directly supported by evidence, and we deliberately excluded softer levers (retention, brand, morale) that are real but too easy to inflate.
- Reclaimed engineering time. Low-performing delivery quietly taxes every engineer through toil, rework, and waiting on slow builds and environments. Improving your DORA profile returns a share of that capacity to building product. Incident and failure cost is counted separately, in the next driver, so the two never overlap. This is the largest and most defensible driver.
- Reduced failure and incident cost. Fewer failed changes and faster recovery mean less remediation labour and less customer-facing downtime. We cost the labour every time and treat downtime as an optional, conservative add-on.
- Faster delivery (opportunity). Some reclaimed capacity is redirected to revenue-generating work. This is the softest of the three, so it is the smallest and is clearly labelled as an estimate.
The sources behind the numbers
Every default in the model carries a citation. Here is where the important ones come from and how we use them.
- Performance bands and targets: the 2024 Accelerate State of DevOps report from Google's DORA programme. The four metrics and the recovery-time bands come from here. DORA is peer-reviewed, independent, and the industry standard, which is why we anchor to it rather than to any vendor. Two honest caveats: DORA's bands are re-derived each year by cluster analysis of self-reported data (they are not fixed constants), and DORA's 2025 report reframed the Elite/High/Medium/Low tiers into team archetypes. The four keys themselves remain the standard, so we keep the widely-understood tier labels for clarity while treating the exact thresholds as approximate.
- That delivery improvements carry a financial value: DORA's 2026 ROI of AI-Assisted Software Development report sets out an official value chain running from engineering capabilities, through the DORA metrics, to financial outcomes. We cite it for that structure - it is DORA's own confirmation that moving these metrics has a euro value - rather than for its AI-adoption or J-curve model, because this tool estimates the value of improving delivery, not the ROI of adopting AI tools.
- How much capacity delivery friction consumes: Stripe's Developer Coefficient found engineers lose roughly 42% of their week to maintenance and technical debt, and the DevEx research (Noda, Forsgren, Storey and Greiler, ACM Queue 2023) characterises the friction, feedback-loop delay, and context-switching cost underneath that. Google's SRE guidance on capping toil at 50% frames the ceiling. Being precise about this: the Stripe figure is a 2018 self-reported average with no breakdown by performance tier, and the DevEx paper frames friction qualitatively rather than as a single percentage. So the per-band "delivery drag" curve is our own conservative assumption, calibrated to sit below Stripe's 42% aggregate, not a measured gradient.
- Cost of an engineer, in euros: a fully-loaded mid-level Dutch engineer at roughly EUR 104,000 per year. That is a gross base of about EUR 80,000 (from Levels.fyi and Hays benchmarks, already including the statutory 8% holiday allowance) multiplied by about 1.30 for the genuine Dutch employer burden: unemployment (AWf), disability (Aof/WIA), the health-insurance-act contribution (Zvw) and a modest pension share, per Deel and the Belastingdienst. We deliberately exclude the 27.65% national insurance (volksverzekeringen), because that is withheld from the employee, not paid by the employer. Divided across roughly 1,880 full-time-equivalent working hours a year. You can override this to match your own numbers.
- Cost of downtime and incidents: figures from PagerDuty and ITIC. Their headline downtime numbers (often quoted above USD 300,000 per hour) reflect large enterprises in critical sectors and overstate a mid-market reality by a wide margin, so downtime in our model is optional, defaults to a conservative EUR 25,000 per hour, and can be switched off entirely. When it is off, the reliability driver falls back to remediation labour only, which is always defensible.
- The wider ROI evidence base: Forrester's Total Economic Impact studies of internal developer platforms (for vendors such as Cortex, GitLab and Red Hat) report three-year ROI figures from roughly 220% to 700%, and McKinsey's Developer Velocity Index links top-quartile engineering practice to materially faster revenue growth. We treat the Forrester numbers as an optimistic ceiling (they are vendor-commissioned) and the McKinsey link as narrative, not as a multiplier, because it is a correlation. Independent Nucleus Research, which finds that roughly two thirds of the technology deployments it studies clear 200% three-year ROI, is the neighbourhood our conservative figures land in.
How to read your results
The result is presented as a range and a set of supporting figures, on purpose.
- The range (conservative to typical). No single number is honest here, so we show two. Both discount the raw model, because in practice not all of the theoretical value is captured. Read the conservative end as the number you would be comfortable defending to a skeptic, and the typical end as a reasonable expectation for an engagement that goes well.
- Engineering hours and the FTE equivalent. The same value expressed as capacity: how many hours you would reclaim, and roughly how many full-time engineers that represents. This is often the more persuasive framing for a leader who thinks in headcount rather than budget lines.
- Your DORA profile, today to target. Each metric shows your current band and the target we assume: a realistic one-band improvement (a High team's target is Elite, never a multi-band leap). Only a team already at Elite on a metric sees no uplift there, which is correct: we do not manufacture a return that is not there. Each of the four metrics is valued on its own, so a single weak metric still shows value even when the others are strong.
- The optional inputs. Setting a downtime cost adds the reliability upside for revenue-critical systems. Entering an expected budget unlocks an annual ROI percentage and a payback period. Both are off by default so the headline stays grounded.
What the calculator is not
It is an estimate, not a promise, and it is not a substitute for measuring your own systems. Gartner's own research is the useful check here: it estimates that around three quarters of DevOps initiatives fail to meet expectations, usually for organisational rather than technical reasons. The value in this model is real, but realising it depends on the engagement, not the spreadsheet. That is exactly why the conservative end of the range exists, and why the honest next step is to ground these numbers in a short assessment of your actual delivery pipeline rather than to treat the estimate as settled.
Try it
Move the sliders to your reality and see the range for your team on the ROI calculator. If the numbers look interesting, the right next move is to make them specific. Book a call and we will ground them in how your teams actually ship.
