Reports
Usage reports, billing breakdowns, and job performance analytics
Reports provide detailed analytics and cost breakdowns for your Warpbuild usage. They are on two pages under Insights in the WarpBuild sidebar:
- Spend: Cost breakdowns for CI runners, Docker builders, cache usage, and agent runners.
- Jobs: Aggregated per-job performance metrics including duration, queue time, CPU, and memory.
All reports support date range selection, sorting, filtering, and CSV export. The CI and Agent runners tabs and the Jobs page also support search.
Spend
The Spend page has four tabs - CI, Docker builders, Cache, and Agent runners - each showing costs for the respective service.
CI Spend
Shows per-job cost data for CI runner usage.
- Daily chart: Stacked bar chart of daily costs, grouped by repository or runner label.
- Summary cards: Total cost, runner cost, snapshot cost, and total jobs for the selected period.
- Table: Each row is a single job execution showing repository, job name, runner label, stack, snapshot usage, execution time, billed time, and cost breakdown (runner + snapshot).
Filters: Repository, runner label, stack, job name, snapshot usage.
Docker Builder Spend
Shows per-session cost data for Docker Builder usage.
- Daily chart: Stacked bar chart of daily costs, grouped by profile or architecture.
- Summary cards: Total cost and total sessions.
- Table: Each row is a single Docker Builder session showing profile, architecture, duration, and cost.
Filters: Profile, architecture.
Cache Spend
Shows cost data for cache operations (storage and access).
- Daily chart: Stacked bar chart of daily costs by cache type.
- Summary cards: Total cost, storage cost, operations cost, and total entries.
- Table: Each row is a cache billing entry showing type and cost.
Filters: Cache type (storage, operation-hit, operation-commit).
Agent Runner Spend
Shows per-run cost data for agent runners.
- Daily chart: Stacked bar chart of daily costs.
- Summary cards: Total cost and total agents.
- Table: Each row is a single agent run showing agent VM, label, environment, session, run time, and cost.
Filters: Label, environment.
Jobs
The Jobs page provides aggregated performance metrics per unique (repository, workflow, job name) combination.
Metrics Table
Each row represents a unique job across all its runs in the selected period. Use the p75 / p90 switch at the top of the page to choose which percentile the table and chart show:
| Metric | Description |
|---|---|
| Runs | Total number of executions |
| Success | Percentage of successful runs |
| Duration p75 / p90 | 75th and 90th percentile execution time |
| Queue p75 / p90 | 75th and 90th percentile time spent waiting in the queue |
| CPU p75 / p90 | 75th and 90th percentile peak CPU utilization |
| Memory p75 / p90 | 75th and 90th percentile peak memory utilization |
CPU and memory metrics require Observability to be enabled. Jobs without telemetry data will show a dash (—) for these columns.
Time-Series Chart
The chart shows a selected metric (Duration, Queue time, CPU, or Memory) at the selected percentile (p75 or p90) over time. Each line represents one of the five jobs with the most runs in the selected period, making it easy to spot performance regressions or improvements. Click a job in the legend to hide or show its line.
Filters: Repository, workflow, job name, runner label, stack.
Queue Timings
Queue timings show how long jobs wait in the queue before they start running. They appear in two places:
- Per job: The Queue column on the Jobs page.
- Per runner: Open a runner from the Runners page. The Activity card shows Queue time p90 for the selected date range, and the Jobs on this runner table shows Queue p90 for each job.
CSV Export
All reports support CSV export. Open the More actions menu (⋯) at the top of the page and choose Export CSV. The export is prepared in the background, and you get an email when it is ready. You can also download it from Export history in the same menu. The CSV includes all rows matching the current filters and sort order (not just the current page). This is useful for further analysis in spreadsheet tools or for sharing with your team.
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