Comparing GCP Workflows, AWS Step Functions, and Temporal for Stateful Workflow Orchestration

Stateful workflow orchestration is essential for engineers managing complex multi-step processes across various services. In this realm, Google Cloud Workflows (GCP Workflows), AWS Step Functions, and Temporal stand out as reliable tools designed to handle long-running, stateful tasks with built-in resilience. However, choosing the right orchestration tool can be a daunting task for senior engineers. Let’s delve into a comparison of these three prominent options from the perspective of developer usability and experience.

Workflow Modeling Approach: Visual vs. Code-Based

In the realm of workflow modeling, GCP Workflows and AWS Step Functions take a declarative approach. AWS Step Functions utilize the Amazon States Language, a JSON-based language with YAML support through specific tools. Each state machine within Step Functions is defined by states and transitions in JSON, with the added option of visual design using AWS’s Workflow Studio. On the other hand, Google Cloud Workflows employ a YAML-based DSL (or JSON) to delineate a sequence of steps that execute sequentially. In GCP’s YAML, steps naturally progress to the subsequent step unless directed otherwise, mirroring a coding paradigm. Both systems facilitate conditional branching and loops, which are configured rather than coded explicitly.

Stay tuned for the next section where we will delve into error handling capabilities, observability features, and cost considerations of these stateful workflow orchestration tools.