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Analyze and Export

Once you have a view in the Metrics tab that answers an interesting question, you can export the underlying data and analyze it further — in a spreadsheet, a BI tool, or simply to share with a non-AI-Kit colleague.

Exporting​

Click Export in the Metrics tab. The button uses your current filters: same period, same range, same automation filter. So the typical pattern is:

  1. Adjust the filters until the on-screen view matches what you want.
  2. Click Export.
  3. The platform downloads a file.

The Metrics tab — the Export button sits next to Refresh in the control bar.

What you get​

The export file contains one row per (period, automation) combination, with columns for:

  • Period start — the timestamp of the bucket.
  • Automation — name and ID.
  • Job count — number of runs in that period.
  • Successful jobs / failed jobs — counts.
  • Input tokens / output tokens / total tokens.
  • Estimated cost — in your workspace's currency.
  • Average duration — in milliseconds.
  • Max duration — for spotting outliers.

The format is CSV with a header row. Most spreadsheets and BI tools open it directly.

Reporting patterns​

Monthly cost report for finance​

Period Month, range 12 months, automation filter All. Export. The result is one row per month per automation, which pivots nicely into a per-automation column chart or a summary table.

Operational reliability for ops review​

Period Week, range 8 weeks, automation filter All. Export. Pivot success rate per automation — anything trending below the team's threshold goes on the review list.

Cost per business unit​

If your automations follow a naming convention (e.g. "[Sales] Daily digest", "[HR] Vacation agent"), you can extract the bracketed prefix in your spreadsheet and group cost by business unit.

Combining with provider invoices​

The provider invoice (OpenAI, Anthropic, Mistral) is the authoritative cost number. Reconciliation is a useful monthly hygiene exercise:

  1. Export Metrics for the month.
  2. Sum the estimated cost column.
  3. Compare to the provider invoice.

The two usually match within 1-3 %. Larger discrepancies typically have one of these causes:

  • The provider's rate changed mid-month and the rate stored in the AI Kit is from before or after.
  • An automation outside the AI Kit is hitting the same API key.
  • The provider is billing for something that does not appear as tokens in the AI Kit's view (file uploads, fine-tuning, separate APIs).

Update the model rate or rotate keys as needed.

Tools that work well with the export​

The CSV format is intentionally boring. It opens cleanly in:

  • Excel and Google Sheets — for ad-hoc analysis and reports.
  • Power BI and Tableau — for ongoing dashboards.
  • Python (pandas) — for one-off analyses, anomaly detection, or merging with other data sources.

The platform does not provide a built-in BI tool. For most teams the export plus a spreadsheet is enough; for serious dashboarding, hook the export into your existing analytics pipeline.

Recommendations​

  • ✅ Apply the filters first, then export. The export reflects exactly what is on the screen.
  • ✅ Use monthly granularity for finance, weekly for ops, daily only for debugging.
  • ✅ Reconcile with the provider invoice once a month. Small drift is fine; large drift is a signal.
  • ⚠️ The export is a snapshot. Re-running an analysis a month later may show slightly different numbers because the underlying data may have been pruned.
  • ❌ Do not use the export as an audit log. It is summary data; the per-job details live in the Jobs view of each automation.

What to do next​