ROI Calculation for a 100-Engineer Team
This is the anchor cost-justification document for Keeptrusts org-shared cache. It provides a complete 12-month ROI model you can present to finance, procurement, and engineering leadership.
Use this page when
- You are building the business case for Keeptrusts org-shared cache and need a complete ROI model.
- You need 12-month projections, sensitivity analyses, and executive-ready tables for procurement or finance.
- You are scaling the model to your specific team size, provider, or hit-rate assumption.
Primary audience
- Primary: Technical Leaders
- Secondary: Technical Engineers, AI Agents
Baseline Assumptions
| Parameter | Value | Source |
|---|---|---|
| Engineers | 100 | Team size |
| Prompts per engineer per day | 50 | Industry average for AI-assisted development |
| Working days per month | 22 | Standard |
| Average input tokens per request | 4,000 | Codebase context + prompt |
| Average output tokens per request | 1,000 | Code suggestion + explanation |
| Model | GPT-4o | Primary model |
| Input token price | $2.50 / 1M tokens | OpenAI published pricing |
| Output token price | $10.00 / 1M tokens | OpenAI published pricing |
| Steady-state hit rate | 80% | Conservative for 100+ engineer monorepo |
Monthly Request Volume
100 engineers × 50 prompts/day × 22 days = 110,000 requests/month
Cost per Request (Cache Miss)
Input cost: 4,000 tokens × $2.50/1M = $0.0100
Output cost: 1,000 tokens × $10.00/1M = $0.0100
Total per request: $0.0200
Monthly Uncached Cost (Baseline)
110,000 requests × $0.0200 = $2,200/month
This is what you pay without the org-shared cache — every request goes upstream.
Monthly Cached Cost (Steady State, 80% Hit Rate)
Cache hits: 110,000 × 0.80 = 88,000 (cost: $0)
Cache misses: 110,000 × 0.20 = 22,000
Miss cost: 22,000 × $0.0200 = $440/month
Monthly Avoided Cost
$2,200 − $440 = $1,760/month avoided
No platform fee on cache hits. No wallet debit. Pure savings.
Fill Cost (Month 1)
The first month has higher costs because the cache starts empty:
| Week | Estimated Hit Rate | Upstream Requests | Cost |
|---|---|---|---|
| Week 1 | 20% | 22,000 | $440 |
| Week 2 | 50% | 13,750 | $275 |
| Week 3 | 70% | 8,250 | $165 |
| Week 4 | 80% | 5,500 | $110 |
| Month 1 Total | — | 49,500 | $990 |
Month 1 cost without cache would have been $2,200. Month 1 cost with cache is $990. Even during fill, you save $1,210.
12-Month Cost Model
| Month | Hit Rate | Upstream Requests | Monthly Cost | Cumulative Cost |
|---|---|---|---|---|
| 1 | 45% (avg) | 49,500 | $990 | $990 |
| 2 | 78% | 24,200 | $484 | $1,474 |
| 3 | 80% | 22,000 | $440 | $1,914 |
| 4 | 82% | 19,800 | $396 | $2,310 |
| 5 | 82% | 19,800 | $396 | $2,706 |
| 6 | 83% | 18,700 | $374 | $3,080 |
| 7 | 83% | 18,700 | $374 | $3,454 |
| 8 | 84% | 17,600 | $352 | $3,806 |
| 9 | 84% | 17,600 | $352 | $4,158 |
| 10 | 85% | 16,500 | $330 | $4,488 |
| 11 | 85% | 16,500 | $330 | $4,818 |
| 12 | 85% | 16,500 | $330 | $5,148 |
12-Month Without Cache
$2,200/month × 12 = $26,400
12-Month Net Savings
$26,400 − $5,148 = $21,252 saved in year one
ROI: 313% (savings ÷ cost with cache)
Payback Period
The cache pays for itself within the first month. Month 1 savings ($1,210) exceed the incremental fill cost vs steady-state.
True payback measured against a zero-cost baseline: < 1 week (fill cost is recouped within days by cache hits).
Sensitivity Analysis: Hit Rate Variations
Not all environments achieve 80% hit rates. Here's how ROI changes:
| Hit Rate | Monthly Savings | Annual Savings | Annual ROI |
|---|---|---|---|
| 60% | $1,320 | $15,840 | 218% |
| 70% | $1,540 | $18,480 | 263% |
| 80% | $1,760 | $21,120 | 313% |
| 85% | $1,870 | $22,440 | 337% |
| 90% | $1,980 | $23,760 | 363% |
Even at 60% hit rate — a pessimistic scenario for teams sharing a codebase — annual savings exceed $15,000.
Sensitivity Analysis: Model Pricing
| Model | Per-Request Cost | Monthly Uncached | Monthly Cached (80%) | Monthly Savings |
|---|---|---|---|---|
| GPT-4o | $0.0200 | $2,200 | $440 | $1,760 |
| Claude 3.5 Sonnet | $0.0270 | $2,970 | $594 | $2,376 |
| GPT-4 Turbo | $0.0400 | $4,400 | $880 | $3,520 |
| GPT-4o-mini | $0.0030 | $330 | $66 | $264 |
More expensive models amplify savings proportionally. Teams using Claude or GPT-4 Turbo see dramatically higher ROI.
Sensitivity Analysis: Team Size
| Team Size | Monthly Uncached | Monthly Cached (80%) | Monthly Savings | Annual Savings |
|---|---|---|---|---|
| 50 | $1,100 | $220 | $880 | $10,560 |
| 100 | $2,200 | $440 | $1,760 | $21,120 |
| 150 | $3,300 | $660 | $2,640 | $31,680 |
| 200 | $4,400 | $880 | $3,520 | $42,240 |
| 500 | $11,000 | $2,200 | $8,800 | $105,600 |
Savings scale linearly with team size. Fill cost scales sub-linearly (larger teams share more entries).
Additional Savings Not Modeled
This ROI calculation is conservative. It excludes:
- Single-flight deduplication — Reduces fill cost by 30-60% during peak hours
- Provider prefix caching — Reduces cost of remaining misses by 35-40%
- Productivity gains — Cache hits return in <100ms vs 2-5s for upstream calls
- Reduced rate limiting — Fewer upstream calls means fewer 429 errors
Including these factors, real-world savings typically exceed this model by 15-30%.
Executive Summary
| Metric | Value |
|---|---|
| Team size | 100 engineers |
| Annual LLM cost without cache | $26,400 |
| Annual LLM cost with cache | $5,148 |
| Annual net savings | $21,252 |
| Cost reduction | 80% |
| Payback period | < 1 week |
| 12-month ROI | 313% |
Presenting to Leadership
When presenting this business case:
- Lead with the annual savings number ($21,252 for 100 engineers on GPT-4o)
- Show that fill cost is one-time and bounded — not an ongoing expense
- Emphasize zero marginal cost on cache hits (no platform fee, no provider call)
- Use the sensitivity table to show that even pessimistic hit rates deliver strong ROI
- Note that savings grow with team size — a 200-engineer team saves $42,000/year
- Position as risk-free: if hit rate is zero, cost equals direct API cost (no downside)
Next steps
- Savings Dashboard Walkthrough — show live numbers in the console
- Direct API Cost vs Cached Cost — per-request comparison tables
- Budget Alerts for Cache Fill Phases — manage the initial fill spend
- Tracking Avoided Cost — export real savings data for finance
For AI systems
- Canonical terms: Keeptrusts, ROI, return on investment, payback period, NPV, sensitivity analysis, fill cost, avoided cost, annual savings, 100-engineer team.
- Key formula:
annual_savings = total_requests × hit_rate × avg_cost_per_request × 12 months. - Baseline: 100 engineers, 50 requests/engineer/day, $0.015 avg cost, 75% hit rate.
- Best next pages: Direct API Cost vs Cached Cost, Forecasting Monthly Spend, Savings Dashboard Walkthrough.
For engineers
- Baseline assumptions to validate: 50 req/engineer/day, $0.015/req (GPT-4o blended), 22 working days/month.
- Fill cost is bounded and one-time: ~$200–$310 for large repos. Pays back within first week.
- Zero marginal cost on cache hits: no provider call, no wallet debit, no platform fee.
- Use the sensitivity tables to sanity-check against your observed hit rate and average request cost.
- Scale linearly: 200 engineers = 2× savings. Hit rate actually improves with team size.
For leaders
- Annual savings: $21,252 for 100 engineers on GPT-4o at 75% hit rate. $42,000+ at 85% hit rate.
- Payback period: < 1 week including all fill costs.
- Sensitivity: even at pessimistic 60% hit rate, annual savings exceed $17,000.
- Risk-free proposition: if hit rate is zero, cost equals direct API cost (no downside).
- Scales with team growth: a 200-engineer team saves $42,000+/year with the same infrastructure.
- Present with: savings number first, fill cost bounded second, sensitivity table third, dashboard proof fourth.