When building multi-step AI pipelines on AWS, you have two main approaches: Lambda functions that call each other directly (Lambda chains), or Step Functions state machines that orchestrate Lambda invocations. Both work; the right choice depends on workflow complexity, error handling requirements, and operational visibility needs.

Lambda Chains

Lambda A calls Lambda B directly, which calls Lambda C. Each Lambda passes data to the next via the return value or by writing to S3/DynamoDB.

Advantages:

  • Simple to understand and implement for linear workflows
  • Low overhead - no state machine configuration or Step Functions pricing
  • Fast iteration: change a Lambda without updating a state machine definition

Disadvantages:

  • Error handling is the caller’s responsibility - each Lambda must handle downstream failures
  • No built-in retry with backoff - you implement this logic manually
  • Visibility is poor - a CloudWatch log per Lambda, no unified execution history
  • Debugging failures requires correlating logs across multiple functions
  • Execution state lives in the call chain - if Lambda B fails after calling Lambda C, you have no record of what Lambda C did

When to use it: Simple 2-3 step pipelines where the workflow is stable and errors are rare. Quick prototypes. Workflows where the “steps” are more accurately described as sub-routines of a single process.

Step Functions

A state machine definition describes the workflow: states, transitions, parallel branches, and error handlers. Step Functions invokes Lambda functions as tasks and manages the execution state.

Advantages:

  • Built-in retry and error handling - configure retry policies per state without code
  • Full execution history - every state transition is logged, inspectable in the console
  • Parallel execution built in - the Parallel and Map states run branches concurrently without custom coordination code
  • Long-running workflows - Step Functions handles wait states and callbacks for human-in-the-loop or external service callbacks
  • Visual workflow editor - useful for communicating architecture to non-engineers

Disadvantages:

  • More upfront configuration - state machine definitions in JSON or CDK require more setup than Lambda chains
  • Cost - Step Functions charges per state transition (around 0.000025 USD per transition for Standard workflows). High-frequency, high-volume workflows can accumulate significant Step Functions costs
  • Learning curve for first-time users of the state machine model

When to use it: Workflows with 4+ steps; any workflow with complex error handling requirements; workflows with parallel branches; workflows that include human approval steps; any workflow you need to debug or audit in production.

Cost Comparison

For a 5-step AI pipeline processing 10,000 documents per day:

  • Lambda chain: Lambda execution cost only (5 invocations x 10,000 = 50,000 invocations/day, ~$0.10/day)
  • Step Functions Standard: 5 transitions x 10,000 = 50,000 transitions/day at $0.025/1000 = $1.25/day

Step Functions Express Workflows (for high-volume, short-duration workflows) price by duration rather than per transition - often cheaper for high-throughput scenarios.

For most AI pipelines, the operational benefits of Step Functions (visibility, retry handling, parallel execution) justify the cost premium. The exception is very high-volume, simple linear workflows where the cost difference becomes material.

Recommendation

Default to Step Functions for production AI pipelines. The debugging and error handling benefits pay back the configuration overhead within the first incident. Use Lambda chains for prototypes, simple preprocessing functions, or genuinely trivial 2-step flows where the overhead is genuinely not worth it.