Trigger.dev
Trigger.dev · Coding
Trigger.dev is an open-source background jobs platform for TypeScript developers. It runs long jobs with no timeouts, automatic retries, and built-in observability.
About Trigger.dev
What Is Trigger.dev
Trigger.dev is an open-source platform for background jobs and durable AI agents. It works as a TypeScript task runner: you write tasks in TypeScript, deploy the code to Trigger.dev's managed infrastructure, and it handles retries, queues, and scaling for you. Your server doesn't babysit a long HTTP request anymore; the platform does that part.
The problem it solves is easy to state. Fixing it by hand is another matter. A webhook comes in, an image pipeline runs for eleven minutes, or an agent loops through a dozen LLM calls. Somewhere in there, a deploy, a crash, or a request timeout kills the whole thing.
So you write your own queue. Your own retry logic. Your own status tracking. None of that's fun. Trigger.dev takes that job off your plate.
The catch is that it's built for TypeScript and JavaScript. If your stack is Python, Go, or Rails, the SDK won't slot in, and self-hosting the open-source version means running a full stack of services yourself. The hosted cloud is the path most people take.
Getting Started
- Create a free account at cloud.trigger.dev, then install the SDK in your project with
npm i @trigger.dev/sdk. - Add your first task file (commonly under
src/trigger) using thetaskfunction with a uniqueidand an asyncrunfunction. - Run the local dev CLI to develop with hot reloading, so you can test tasks against real runs before anything ships.
- Deploy with the CLI to push your tasks to the managed infrastructure, or follow the self-hosting guide for Docker and Kubernetes.
- Trigger the task from your backend code and monitor progress in the dashboard, where you can inspect runs, retry failures, and bulk-manage tasks.
Product Information
A quick look at Trigger.dev's pricing, supported platforms, and performance.
Best for
The users, tasks, and scenarios where this tool fits best.
Users
- TypeScript and Node.js developers
- AI product teams
- Small startups without a platform team
Tasks
- Long-running AI workflows
- Scheduled and recurring jobs
- Data processing and ETL
Scenarios
- Handling webhooks that kick off slow work
- Streaming AI responses to your frontend
- Running multi-day marketing flows
Key features
No timeouts and durable execution
Tasks run for as long as they need to. When a run crashes or your code redeploys mid-task, durable execution checkpoints the state and resumes from where it stopped instead of starting over. No restart from zero. That's what makes agents and multi-hour pipelines practical.
Automatic retries and scheduled tasks
Every task retries by default when an error is thrown, and you control the number of attempts and backoff in the retry field. Scheduled and cron-style tasks cover recurring jobs, so you don't stand up a separate scheduler for nightly syncs or weekly reports.
Queues and elastic concurrency
Tasks run through configurable queues with concurrency limits that scale with your plan. Waiting tasks don't count against your concurrency, and batchTrigger() fits up to 1,000 tasks into a single API call, which matters when one event needs to fan out to many jobs.
Realtime status and streaming
Trigger.dev Realtime shows run status and metadata anywhere in your app, and Realtime streams forward LLM responses from a run to your users as they're generated. It's the piece that turns a background job into an interactive experience that users can actually watch progress in real time as each step of a long AI workflow completes.
Observability and error alerts
A run dashboard lets you filter runs, apply bulk actions, and dig into failures fast. Alerts fire to email, Slack, or webhooks when tasks or deployments fail. Billing alerts catch runaway usage at several budget thresholds. If something breaks at 3am, you'll know before your users tell you.
Atomic versioning on deploy
Each deploy is an atomic version, so tasks that already started keep running on the code they began with. You stop worrying that a mid-flight job will break the moment you push a change.
Framework agnostic with self-hosting
Trigger.dev works with most JavaScript frameworks, with guides for popular ones, and supports popular AI tooling like the Vercel AI SDK. The whole platform is Apache 2.0 licensed, so you can read the source, contribute, or self-host on Docker and Kubernetes.
Usage-based cloud pricing
The cloud charges for compute seconds plus a per-run invocation cost, with monthly credits included on each paid plan. Past your credits, tasks keep running on paid plans and are billed at usage rates. A busy month won't take down your jobs. How much will it cost? That depends on your run volume, which the docs walk through with a worked example.
Pros and cons
Pros
- No timeouts and durable execution make long AI agent runs and multi-hour pipelines safe to deploy.
- Automatic retries, queues, and scheduled tasks come built in, cutting out the custom background job code most teams write.
- Realtime status and LLM streaming get wired to your frontend without extra API routes.
- Open source under Apache 2.0, so you can inspect the code or self-host if you'd rather not use the cloud.
- A free plan with $5 of monthly compute lets you build and test real tasks before paying.
Cons
- The SDK is TypeScript and JavaScript only, so Python, Go, and Rails projects can't use it directly.
- Cloud pricing is usage-based, which makes costs hard to predict if your task volume swings a lot.
- Self-hosting works, but it means running a multi-service stack yourself, which is a real time investment.
Frequently asked questions
It's a background jobs platform for running long-running tasks and durable AI agents. You write tasks in TypeScript, deploy them to managed infrastructure, and Trigger.dev handles retries, queues, scaling, and monitoring.
Related content
Explore related tools, skills, and articles for Trigger.dev.
Trigger.dev Alternatives
Base Code
Base44 (Wix) · Coding · ProductivityBase Code is a shared cloud development environment from Base44. Teammates describe changes to an existing GitHub codebase in chat, preview them live and send them back as pull requests.
Google EmbeddingGemma 2
Google DeepMind · Writing · Image · CodingGoogle EmbeddingGemma 2 is a 740M-parameter open model that turns text, code, images, audio, and video frames into vectors in one 768-dimension space, running on phones and laptops.
GitGlow
GitGlow · Coding · ProductivityGitGlow is a local-first desktop Git client that lets independent developers review their own code and their AI agents' changes in one place before shipping.