AWS Step Functions
AWS Step Functions orchestrates multi-step workflows as state machines. Instead of writing retry logic, error handling, and branching inside a Lambda function, Step Functions externalizes workflow control — each state in the machine is a discrete step with its own retry policy, timeout, and error handler. We design Step Functions workflows for complex business processes that Lambda alone is not the right tool for.
Capabilities
What we build with Step Functions
Sequential workflows
Multi-Step Processing Pipelines
Data processing pipelines where each step hands its output to the next, with retry logic and error handling defined per-step rather than inside a single monolithic Lambda function.
Wait for callback
Human Approval Workflows
Workflows that pause at a step waiting for human approval — via email, Slack, or a custom UI — before continuing. Step Functions waits indefinitely using a task token until the approval signal arrives.
Map · Parallel states
Fan-out and Aggregation
Distributing work across multiple parallel Lambda invocations using Map state (one invocation per item in a list) or Parallel state (multiple simultaneous branches), then aggregating results.
Catch · Retry states
Error Handling and Retry
Retry policies with exponential backoff on each task, and Catch states that route failed executions to compensating workflows or dead-letter paths rather than failing the entire execution.
Days · Weeks
Long-Running Business Processes
Standard Workflows that run for up to a year, covering business processes that span time — document review cycles, approval chains, or order fulfillment workflows with external dependencies.
Direct AWS service calls
SDK Integrations
Step Functions SDK integrations that call AWS services directly — DynamoDB, SQS, SNS, ECS — without an intermediate Lambda function, reducing latency and cost for simple integration steps.
Our approach
Step Functions is for workflow, not compute
Step Functions orchestrates; Lambda computes. A state machine that contains all the business logic inside a single Lambda pass-through task has not gained anything from Step Functions. We decompose workflows into discrete states where each Lambda task does one focused thing and the state machine manages flow control, retry, and error routing.
Standard versus Express Workflows
Standard Workflows are durable, support exactly-once execution, and can run for up to a year — right for business process workflows. Express Workflows are lower cost, higher throughput, and at-least-once — right for high-volume event processing pipelines. We choose based on execution duration and idempotency requirements.
SDK integrations reduce Lambda overhead
Optimistic Step Functions architectures from before SDK integrations existed use Lambda as a thin wrapper around a single AWS SDK call. SDK integrations eliminate that wrapper for common operations like writing to DynamoDB, sending an SQS message, or starting an ECS task, reducing both latency and cost.
State machine definitions in ASL, managed by Terraform
State machine definitions in Amazon States Language (ASL) are verbose JSON or YAML. We define them in Terraform using the aws_sfn_state_machine resource so that the workflow definition is version-controlled, reviewable, and deployable automatically.
All engineering work is done by US-based engineers. We do not offshore any development or architecture work.
Part of our serverless practice
FAQ
Common questions
Virginia · United States
Designing a Step Functions workflow?
Reach out and we will discuss the workflow requirements, state design, and integration patterns before any work begins.