GUIDE
The Cost of AI-Generated Engineering Metrics
A worked estimate — with an interactive calculator — of what it would cost in LLM tokens to compute DORA and Flow metrics with AI instead of deterministic rules.
Why we're publishing this
"AI-powered" engineering analytics tools are everywhere now — feed a model your Jira, GitHub and incident data, and it'll tell you your numbers, no rules to configure. It sounds appealing until you ask what it costs to run, month over month, as your data volume grows. moasy.tech computes DORA Metrics and Flow Metrics with plain SQL aggregation over data you already sync — no model in the loop. This page does the math on the alternative: what it would actually cost, in raw token spend, to replicate just those two modules with an LLM doing the classification and correlation work instead.
Calculate your cost
Pick the integration categories you'd sync and how many teams you have. The estimate updates live.
Estimated AI token cost: USD 2.25 – 22.50/mo
moasy.tech: USD 0.00/mo (Free plan)
Illustrative estimate — see the method below for every assumption behind it.
Typical scenarios
All 4 categories selected, against the real moasy.tech plan that fits that many teams.
| Teams | moasy.tech plan | Price/mo | Estimated AI token cost/mo |
|---|---|---|---|
| 1 | Free | USD 0 | USD 2.25 – 22.50 |
| 5 | Basic | USD 19.90 | USD 11.25 – 112.50 |
| 7 | Pro | USD 49.90 | USD 15.75 – 157.50 |
Want the breakdown for just one category instead of all four? See the AI cost of ticket classification, pull request analysis, CI/CD deploys, or incident correlation on its own.
Method
Every number on this page is reproducible from these assumptions — audit them, don't just trust them:
-
Event volume: modeled at DORA's own published "elite" deploy
frequency — multiple deploys a day, ~3/day used here — and ~3 ticket changes per
engineer per week for a 5-engineer team. That's ~180 events/month per team across
all 4 categories: 60 tickets, 27 PRs, 90 deploys, 3 incidents. PR and incident
volume come from moasy.tech's own demo-seed dataset
(
n_back/scripts/seed-demo-data.ts), built for marketing screenshots — illustrative, not real customer data. - Input tokens per event: ~2,000 tokens of fixed system-prompt instructions (defining what counts as a deploy, the incident-correlation window, classification rules) + ~200 tokens for the event itself + ~3,000 tokens of recent context for correlation (e.g. the last ~20 related events) ≈ 5,000 tokens.
- Output/reasoning tokens per event: ~1,500 — enough for a model to actually reason about an ambiguous case (which category does this ticket belong to, does this incident correlate with a recent deploy), not just state a number.
- Price range: USD 1–10 per million input tokens, USD 5–50 per million output tokens — a generic range spanning cheap to frontier-tier commercial LLM APIs, not tied to one vendor.
- Cadence: one call per event, as it happens — not a daily batch recompute. A batch-recompute model understates cost, because it only pays the system-prompt overhead once per day instead of once per event.
That implies a cost per event of USD 0.0125 (cheap tier) to USD 0.125 (frontier tier).
What this number doesn't include
This is a floor, not a ceiling. It counts only raw LLM token spend for 2 of moasy.tech's 7 modules. It excludes:
- The AI provider's own product margin and hosting
- The other 5 modules — Integrations, Capacity & Workload, Timeline, Cost Classification, Activity Reports
- The dashboard UI, authentication, alerting — everything else a real product needs
- Engineering time to write, test and maintain the prompts, plus ongoing evaluation to catch drift
Why this could be even higher in practice
Three things left out on purpose, because they're harder to put a clean number on:
- Interactive use — someone asking follow-up questions about their metrics resends the growing conversation on every turn, which grows cost faster than a one-shot call.
- Self-verification — an LLM computing something like a percentile or a correlation window reliably usually needs a check/retry step, not just one pass.
- Richer event payloads — this estimate uses a ~200-token compact JSON per event; a tool that feeds in full PR descriptions, commit messages or incident postmortems would use several times that.
Questions about this estimate
Is this a knock on any specific AI tool?
No — it's not a comparison against a competitor, model or vendor. It's a comparison of two computation approaches: a database aggregation (what we do) versus an LLM call (what an "AI does this" pitch implies). See our no-AI section for why we chose the first one.
What if I use a cheaper or self-hosted model?
The lower end of every range here already reflects the cheapest commercial-tier pricing available. A self-hosted open model removes the per-token price but replaces it with GPU hosting, ops and tuning cost — real, just harder to put a single number on, so it isn't included here.
Why does the low end sometimes cost less than a moasy.tech subscription?
We're not hiding it — at the cheapest tier and smallest team size, raw token spend for 2 modules can land below what our whole 7-module subscription costs. The point isn't "AI always costs more" — it's that the AI number is a variable, usage-scaling cost with no product built around it, while ours is flat and includes everything.
Does this include the cost of building the AI tool itself?
No — see "what this number doesn't include" above. This is token spend only.