Why AI agents need durable workflow orchestration. A practical comparison of Inngest, Trigger.dev, and Vercel Workflow for AI features in production.

Alex CloudStar

Running multi-step AI workflows on plain serverless functions leads to timeouts, duplicate LLM charges, and data loss during provider outages. Durable workflow engines solve this by checkpointing each step so failures resume from where they left off rather than restarting from scratch. This is a practical comparison of three tools for 2026: Inngest (mature, event-driven, polished managed service), Trigger.dev (open source, self-hostable, fastest setup, AI-native SDK), and Vercel Workflow (seamless for Vercel/Next.js apps, cost-efficient via Fluid Compute). A decision framework covers when to pick each, plus practical patterns: checkpoint every LLM call, store raw completions, use native rate limiting, design for idempotency, and keep step inputs small.

Durable AI Workflows 2026: Inngest, Trigger.dev, Vercel Workflow

Trigger.dev: The Open Source Friendly Pick