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The difference is not only when work runs. It shapes how you think about task ownership, execution history, and how downstream systems consume datasets over time.

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One-off runs

One-off execution is best for:
  • Ad hoc extraction or discovery
  • Testing a new schema or modular plan
  • Running a job on demand
  • Collecting a one-time snapshot
The usual pattern is:
  1. Create a task
  2. Trigger execution manually
  3. Monitor the execution
  4. Download results

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Scheduled runs

Scheduled execution is best for:
  • Ongoing monitoring
  • Recurring collection
  • Change detection
  • Dataset accumulation over time
In a scheduled workflow, the task remains stable while the platform creates new executions according to the configured schedule.

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Why the distinction matters

Both paths use the same task and execution objects, but they serve different operating models:
  • One-off tasks emphasize immediate output.
  • Scheduled tasks emphasize repeatability and a history of runs.
That distinction matters for downstream consumers: one-off work is often consumed as a single export, while scheduled work is often consumed as a time series of executions or datasets.

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Triggering execution

Manual task runs typically use: 
curl -X POST "$TITAN_API_URL/api/v1/tasks/$TASK_ID/run" \
  -H "Authorization: Bearer $TITAN_TOKEN"
You can also trigger work through execution endpoints, depending on your integration style (see the API Reference).

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Scheduling guidance

When using schedules:
  • Keep the output schema stable when consumers depend on a fixed shape.
  • Keep the task identity stable so history stays grouped.
  • Treat executions as the units of runtime history.
That yields a clearer long-term dataset model and simpler monitoring.

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Next steps