Engineering cost tracking that's a live number, not a postmortem
Most teams figure out what a feature cost after the budget is already spent. Infersync surfaces cost-per-feature live, computed from your real commits and PRs, with budget gates wired directly into the AI assignment ranker so over-budget candidates are auto-excluded from new work.
Why teams end up here
- Engineering management knows the total budget at the end of the quarter, not which feature drove the overrun.
- Cost-per-feature gets pieced together in a spreadsheet two weeks after delivery, when nobody can act on it.
- Time tracking lives in a separate tool from the work items, so the numbers never reconcile.
- Assignment decisions get made on availability alone; the cost of routing a P1 to a senior engineer instead of a mid-level never enters the equation.
- When a feature goes over, there's no automatic guardrail. The team just keeps going until someone notices in the next sprint review.
Cost-per-feature, computed live from the commits that actually shipped
Infersync attributes every commit and PR back to the feature it's building, multiplies through the assignee's seat cost, and surfaces a live burn number per feature, per repo, per sprint, per client. The AI COO assignment ranker reads the burn data and auto-excludes over-budget candidates from new task assignments, so the cost lens is wired into the workflow rather than parked in a dashboard nobody opens.
Cost-per-feature analytics
Live burn per feature, per repo, per sprint, per client. Drill into a feature to see the per-engineer breakdown, the timeline, and where the overrun started.
Burn analytics with sprint forecasting
Daily burn breakdown, projected sprint close date based on current velocity, top-burn issues per project, blocked work items flagged automatically.
Budget gates on AI assignment
The assignment ranker uses (skill fit × availability × cost inverse). Over-budget candidates are excluded before they're shown as options. No manual cost discipline required.
Two-way GitHub sync as the source
Commits and PRs from your existing GitHub repos drive the cost roll-up automatically. No double entry. Issues edited in Infersync write back to GitHub via webhook.
CSV exports for finance
Burn summary and time entries export to CSV for finance team reporting, monthly close, or board-deck prep.
PDF burn reports
One-click PDF generation of the burn report for stakeholder review or quarterly business review packs.
Steps to get from zero to live
- 1
Connect GitHub
OAuth in 30 seconds. Two-way sync covers issues, PRs, labels, assignees, state, comments.
- 2
Set seat costs
Per-member hourly or daily rate in workspace settings. Defaults to a sensible average if you don't want to set it per person.
- 3
Tag work to features
Use GitHub labels or Infersync project mapping. The system rolls cost up automatically from there.
- 4
Open the burn page
Live cost-per-feature visible from day one. By day seven, the forecasting and per-engineer breakdown are calibrated.
Available on the Operations plan and up
Cost-per-feature analytics, burn forecasting, and budget gates on AI assignment all ship on the Operations plan at £15 per seat per month. Base (£7) covers attendance and standard dashboards; Operations adds the cost lens. The 14-day free trial unlocks Operations-tier features for all users regardless of plan, so you can see the burn surface against your real GitHub data before committing.
FAQs about engineering cost tracking that's a live number, not a postmortem
How is cost-per-feature actually computed?
For each commit and PR, Infersync resolves the author's seat cost (set in workspace settings), the time on the issue (from clock in/out + work-item timer), and the feature label or project mapping. The product of those three rolls up to a per-feature burn number, refreshed live as new commits and time entries land. Forecasting projects the sprint-close date based on current velocity versus remaining work.
What if my team doesn't currently track time?
Two paths: (1) Use Infersync's native clock in/out and work-item timer with daily reminders, no separate tool needed. (2) Estimate from commit cadence and PR cycle time as a starting baseline, then layer in real time entries as the team adopts the timer. The cost surface works with either; precision improves as more real time data lands.
What's the budget-gate behaviour exactly?
When the AI COO ranks candidates for a new assignment, it computes skill_fit × availability × (1 / cost_per_hour). Any candidate whose remaining capacity for the feature would push the feature over its budget threshold is excluded from the ranked list before the human sees it. The threshold is configured per feature (or inherited from project default). The full reasoning is shown in the AI COO preview step before execution.
Does this work without an AI / LLM key?
Yes for the cost analytics and budget tracking — those are pure deterministic computation from commits + time. The AI COO assignment ranker uses an LLM for the natural-language reasoning step; you bring your own Anthropic / OpenAI / Google key (zero token markup from us), or the assignment ranker falls back to deterministic skill + cost ranking without natural-language input.
Can finance use this for monthly close?
Yes. CSV exports for burn summary and time entries, PDF burn reports for quarterly business reviews, audit log for billing-relevant events. The data model is built so engineering management runs the day-to-day surface and finance pulls the monthly numbers without needing Infersync access themselves.
How does this compare to Tempo + Jira or Toggl + Linear?
Tempo and Toggl are time-tracking plugins that produce a separate set of numbers; you still have to manually correlate to features and recompute cost. Infersync's cost-per-feature is computed in-platform from your existing GitHub commits, so there's no double entry and no reconciliation step. The budget-gate-into-assignment loop is the bit that doesn't exist in any plugin combination.
See also: Engineering cost tracking • AI task assignment • GitHub time tracking • Capacity planning.