AWS Step Functions orchestrates workflows defined in Amazon States Language, billed per state transition or per request. Pages here cover moving state machines to self-hosted Temporal, Kestra or Apache Airflow.
Comparing Step Functions state machines with self-hosted Temporal workflows written as code: durability and execution guarantees, run length, history retention, versioning, pricing per transition against running a Temporal cluster, and portability beyond AWS.
For data and platform teams: Step Functions' JSON state machines against Kestra's declarative YAML flows, triggers and scheduling, integration with AWS services, observability, and what self-hosting Kestra adds to the operations load.
When state machines mostly schedule batch and data jobs: how Airflow's Python DAGs compare with Step Functions for scheduling, retries, backfills and AWS integrations, and when Step Functions' event-driven model still fits better.
Moving state machines to Temporal: exporting Amazon States Language definitions, translating Task, Choice, Wait, Parallel and Map states into workflow and activity code, replacing callback patterns with signals, and running both side by side during cutover.
Cost of ownership by component: per-transition Standard pricing and per-request and duration Express pricing, against Temporal's servers, persistence database, Elasticsearch or OpenSearch visibility, engineering time and on-call. Where the break-even sits.
Getting your workflows out: retrieving Amazon States Language definitions through DescribeStateMachine, decrypting KMS-encrypted definitions, capturing IAM roles and service integrations, saving execution history before the 90-day window closes, and building the migration inventory.
For teams whose workflows must run outside AWS, across clouds or in their own data center: replacing Step Functions with self-hosted Temporal or Kestra, what changes for AWS service integrations, and who runs the orchestration layer.
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