Calculate the cost of ungoverned AI coding.
StackRails compares your current AI-assisted engineering workflow with a governed, context-aware workflow where agents understand your code and architecture before they generate. Same engineers, same AI tools. Three views: the cost today, what StackRails recovers, and what agentic-at-scale looks like.
Every CTO and VP of Engineering is fielding the same board question: what's our AI strategy?
The same models ship governed code at the frontier and ungoverned, drifting code everywhere else. What separates them is whether every agent works with your architecture and your policies.
Your numbers
Not using AI coding tools yet? Slide to 0% to see baseline costs that exist either way, then increase to project what you'd pay as you roll out. Typical enterprise: 70%.
−$4.35M
/yr · $43,489/eng
Annual cost at 70% AI adoption.
+$1.52M
/yr · $15,231/eng
Value StackRails recovers at your current adoption
+$4.38M
/yr · $43,821/eng
Value when governed agents do more of the work
Without governance and context: the annual cost of ungoverned AI coding at your current adoption level. If you haven't rolled AI out yet, slide adoption to 0% to see the baseline costs that exist regardless, then ramp up to project the cost as AI usage grows.
With StackRails: the slice of that cost pool we recover at the same adoption level.
At agentic scale, with StackRails: a future state that combines high adoption with mature agentic governance, beyond the adoption slider maxed out.
Five cost pools, plus one net-new throughput line.
Each line shows the same value pool across all three states. Hover sources for the underlying research.
1. Engineering capacity
100 engineers × $220,000 loaded × 70% AI-adopted. The model's most-contested line: it recovers part of the METR 19% slowdown. Treat the dollar figure as upside. The defensible spine is the coverage ratio: AI drives code volume up while reviewer capacity stays flat (Faros: PR volume +98%, review time +91%; Stanford: junior hiring −16%), so governed context is what holds review coverage without adding headcount.
2. PR review and rework
1,400 AI-touched PRs × 1.5 hours rework × $138/hr loaded. Without governance shows the full burden. With StackRails reduces it 40% via pre-generation context. At agentic scale: 55% reduction even as PR throughput grows.
3. Code churn, duplication, drift
Without governance applies 3% of payroll as the gross drag. With StackRails indexes existing code so agents stop reimplementing what's already there, 2% recovered. At agentic scale: full drift closure at 3%.
4. Onboarding and senior leverage
15 new hires/year × ramp time + 15 senior architects × 2.5 hrs/week interrupt time. Without governance: full drag. With StackRails: new hires ramp 2 weeks faster; senior interrupt time down 30%. At agentic scale: 3 weeks faster; 50% interrupt recovery.
5. Vendor and services displacement
$96,000/year on external architectural advisory and modernization. Without governance: the full line item. With StackRails: half displaced, half retained, 75% confidence. At agentic scale: full displacement.
6. Agent-throughput leverage
At agentic scale, governed agents complete work that simply did not happen before. 100 engineers × 90% agent-enabled × $8,000/eng additional value. New throughput, not recovery from a cost pool.
Annual total
Per engineer: $43,489 cost · $15,231 recovered · $43,821 at agentic scale
These are model defaults. Not StackRails-measured outcomes.
Every multiplier is anchored to 2024-2026 tier-1 research. Expand any line above for its source and the exact extract we used. Lead with the defensible lines (vendor displacement, PR rework, and churn), which stand on their own; the capacity-recovery line is the model's softest and most-contested input and should be read as upside, not the headline. Its defensible spine is the coverage ratio: code volume rises while reviewer capacity stays flat (Faros, Stanford, DORA). Loaded-cost defaults follow BLS wage and benefits data, and the AI-code risk premise follows peer-reviewed defect-profile research. The real numbers for your codebase come from the 14-Day Architectural Risk Assessment, where agent traces, PR telemetry, token logs, and policy events replace every assumption.