Why Orchestration?

Distributed workflows in production AI pipelines require coordination across multiple services, durable state management, and graceful failure recovery. Without proper orchestration, teams end up with fragile chains of HTTP calls, manual retry logic, and lost work when services crash mid-execution.

Key challenges orchestration solves:

• Coordination - Sequencing steps across independent services
• Durability - Persisting state so work survives crashes
• Parallelism - Fan-out to process items concurrently, fan-in to aggregate
• Observability - Visibility into what's running, what failed, and why

Comparison Approach

Built hands-on POCs implementing the same invoice processing workflow across multiple frameworks. This enables apples-to-apples comparison of fan-out/fan-in patterns, replay behavior, and operational overhead.

Monorepo structure: Each framework lives in its own directory with identical business logic, allowing direct comparison of:

• Developer experience and learning curve
• Operational complexity and infrastructure requirements
• Performance characteristics and scaling patterns
• Cost models at different volumes

Scope: Real-Time, Not Batch

This evaluation focuses on event-driven, real-time workflows—not scheduled batch jobs. The use case is synchronous request/response where a client waits for results.

In scope: Fan-out/fan-in orchestration, durable execution, failure recovery, sub-second latency

Out of scope: Scheduled ETL, nightly batch processing, DAG-based data pipelines (Airflow, Dagster)

Test Workflow: Invoice Processing

A common background task implemented across all frameworks to enable fair comparison.