Prefect specialists for your dedicated team.

Where Prefect fits in a long-term product team.

Dynamic workflow structure is Prefect's practical advantage. A flow whose shape depends on runtime data, such as processing however many files arrived or fanning out across whatever partitions exist, is ordinary code here, where statically defined DAGs need workarounds.

Because flows are plain Python, adoption cost is low for a team that already writes Python, and testing is ordinary function testing. The trade-off is fewer opinions: Prefect will not stop a team building an unmaintainable workflow, where a more prescriptive orchestrator would.

What your dedicated team does with Prefect.

Retry configuration is where reliability is won. A transient API failure should retry with backoff; a malformed input should not retry at all. Treating every failure the same either masks real problems or gives up on recoverable ones.

Getting that distinction right depends on knowing the specific sources and their behaviour, which is product knowledge that accumulates in a standing assignment and is set out in how the monthly fee is built.

Common Prefect use cases.

• Workflows whose shape depends on the data Fan-out across whatever arrived, without contorting a static DAG definition.
• Retries that distinguish failure types Backoff for transient errors, immediate failure for bad input.
• Flows tested as ordinary functions Logic verified without standing up a scheduler.

Adding Prefect skills to your team.

Prefect work sits with a dedicated data engineering team, with failure handling designed for each source.

Prefect specialists join your team remotely as dedicated, full-time team members, selected, employed and managed by us in Chiang Mai and Bangkok. Companies that hire offshore developers through Azendo add the skill at the next monthly cycle.