Trigger.dev

Trigger.dev's pitch is durability without a DSL. You write async TypeScript in your own repo, the CLI bundles and deploys it, and the platform handles retries, queues, concurrency, waits, and scaling. That matters most for AI work where a single request can fan out into tool calls, database writes, and an email — and where losing the run on redeploy means a support ticket. The parts that are hard to replicate: chat.agent() with tools declared once and typed inside run(), needsApproval: true that pauses a run on a waitpoint (a waiting run doesn't consume compute), Realtime streams that forward any provider's LLM output to your frontend without an API route, and AI observability that records model, tokens, cost, and latency per call. The v4.6.x line added Zod 4 by default, a managed streamText returned from run(), chat.agent transcript storage, and chat.close(); v4.7.0 added multiple concurrent concurrency limits and per-tenant caps with a total limit. Operations tooling is real: version skew protection pins each run to the deployment built from the same commit across production, staging, and preview; atomic deployments; environments for Dev, Preview, and Prod; bookmarkable smart runs-list columns; and bulk runs.bulk.replay()/runs.bulk.cancel() from the SDK. The honest weaknesses: pricing scales with compute seconds and run invocations, so high-frequency sub-second workloads are a poor fit — a 10s task at 100 runs/day costs about $1.09/month, but chatty polling blows past that; plans cap concurrent runs, schedules, log retention, and Realtime connections, so larger workloads pay for overage bundles; the whole model assumes TypeScript/Node; self-hosting works via Docker or Kubernetes but you run your own workers and upgrades; and it isn't a transport layer for real-time voice or video. If you want a black box, this isn't it. If you want durable execution you can debug in your own codebase and an Apache 2.0 exit hatch, it is.