GUIDE
The AI Cost of Classifying Tickets
What it would cost in LLM tokens to classify Jira or Linear ticket changes with AI instead of configurable rules.
What this covers
Every ticket change — created, moved, commented on, closed — carries a classification question: is this a Bug, a Feature, Technical Debt, Toil or Risk? moasy.tech's Flow Metrics and Cost Classification both depend on that answer, applied consistently across an entire team's ticket history. An LLM-based alternative would answer that question with a model call per ticket change instead of a configured rule.
The math
A 5-engineer team generates roughly 60 ticket changes a month (~3 per engineer per week). Each classification call costs an estimated USD 0.0125 (cheap-tier model) to USD 0.125 (frontier-tier model) — see the full method for how that per-event cost is built (input tokens for the rules + the ticket + recent context, plus reasoning/output tokens for the classification itself).
| Teams | Ticket changes/mo | Estimated AI cost/mo |
|---|---|---|
| 1 | 60 | USD 0.75 – 7.50 |
| 5 | 300 | USD 3.75 – 37.50 |
| 7 | 420 | USD 5.25 – 52.50 |
How moasy.tech does it instead
Ticket classification runs on semantic rules you configure yourself — a company-wide default, overridable per team — applied instantly and consistently to every synced item, with zero per-classification cost. See the full implementation on the Flow Metrics and Cost Classification feature pages, or the full cost comparison, with a calculator, for every integration category combined.
Questions about this estimate
Does this count every ticket ever created, or just this month's activity?
Just ongoing monthly activity — created, updated or closed tickets in a given month, not a one-time cost to classify your entire backlog. A backfill of existing history would be a separate, one-time cost on top of this.
Why does classifying a ticket need 5,000 tokens of context?
Most of that is the fixed rulebook (~2,000 tokens) and recent related tickets for consistency (~3,000 tokens) — the ticket itself is a small fraction. That overhead is paid on every single call, which is the main reason per-event AI classification adds up faster than it looks at first.