Stateful workflow orchestration tools help engineers reliably coordinate multi-step processes across services. Google Cloud Workflows (GCP Workflows) and AWS Step Functions are fully managed cloud services for defining workflows as a series of steps/states, whereas Temporal is an open-source orchestration engine that developers can self-host or use via a managed offering . All three aim to handle long-running, stateful sequences of tasks with built-in reliability. This article compares GCP Workflows, AWS Step Functions, and Temporal from a senior engineer’s perspective, focusing on developer usability and experience. We examine their workflow modeling approaches, error handling capabilities, observability, cost and scalability considerations, and deployment models. The goal is to help you choose the right tool for your use case in a vendor-neutral way.
Workflow Modeling Approach (Visual vs. Code-Based)
GCP Workflows and AWS Step Functions – Declarative Definitions: Both GCP Workflows and AWS Step Functions use a declarative syntax to model workflows, though in different flavors. AWS Step Functions expresses workflows in the Amazon States Language (JSON-based, with YAML support via tools). Each state machine (workflow) in Step Functions is defined by states and transitions in JSON, and you can design it visually using AWS’s Workflow Studio. Google Cloud Workflows uses its own YAML-based DSL (or JSON) to describe a sequence of steps that execute in order . In GCP’s YAML, steps implicitly flow to the next unless directed otherwise, similar to a coding style . Both systems support conditional branches (e.g. AWS Choice state, GCP switch statements) and loops, but you author them as config rather than writing general-purpose code.