| Metric | Before | After Temporal | 
|---|---|---|
| Successful video jobs | 85 % | 98 % | 
| Lost progress after crash | frequent | none | 
| Average recovery time | 15 min | 10 s | 
| GPU utilization | 65 % | 90 %+ | 
| Approach | Key Tools / Examples | Pros | Cons | 
|---|---|---|---|
| Stateful Workflow Engine | Temporal, Airflow, Azure Durable, Netflix Conductor | Deterministic execution, built-in retries, full observability, easy debugging. | Requires learning the engine model; adds a central orchestration layer to manage. | 
| Stateless Microservices | REST endpoints, pub/sub events | Simple to deploy, loosely coupled services. | Hard to recover from failures; manual retries and idempotency handling required. | 
| Choreography (Decentralized) | Event-driven systems, peer agents | No central bottleneck; flexible agent interactions. | Complex global consistency; hard to monitor distributed state. | 
| Batch Tools | Airflow, Luigi | Well-suited for ETL and scheduled data pipelines. | Not designed for real-time or interactive AI workflows. |