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An experiment run executes each dataset row against each active candidate. If your dataset has 250 rows and the experiment has four candidates, the run creates 1,000 result cells. Your current plan limits how many result cells a new run can create:
  • Developer: 50 result cells
  • Growth: 250 result cells
  • Pro: 1,000 result cells
  • Enterprise: 5,000 result cells
If your plan changes, existing experiments and past runs remain available. New runs use your current plan limit, so a run that fit on Pro may need fewer dataset rows or candidates after a downgrade.

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How do I start a run?

Open the experiment. Click Start run. Confirm the dataset version. Review the candidate count and total result cells. Click Start run.

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Billing and usage

Experiment generations use normal model execution and billing. The total run cost depends on:
  • dataset row count
  • candidate count
  • model settings
  • tool usage
  • retries or failures that still produce billable provider work
Review the result cell count before starting large runs.

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Tracking progress

While a run is active, Fetch Hive shows run status, phase, completed count, failed count, pending count, token totals, cost, and duration. Runs may take time when datasets are large or candidates use slower models or tools. Each run has a stable run ID such as exrun_a1b2c3d4e5f6. Use this ID when sharing a run link with your team or when matching dashboard activity back to API responses.

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Cancelling a run

Open the run. Click Cancel run when the run is still pending or running. Cancellation stops remaining work where possible. Results already completed remain available.

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Failed cells

A failed result cell does not always mean the whole run failed. Open the result detail to review the error. Common causes include missing inputs, unavailable models, provider failures, tool failures, or configuration issues in the candidate. See also: Review results and Task costs