What Is Trigger.dev? The GTM Engineer's Guide to AI Workflows

Trigger.dev is an open-source background jobs platform that lets you write reliable AI workflows in plain TypeScript. It solves a specific problem: serverless functions timeout too fast for AI tasks, and no-code tools break when automation gets complex.

I use Trigger.dev to run a workflow that fires 7 parallel research tasks every time someone books a meeting with me. Exa researches the company. DeepSeek analyzes the data. Notion stores everything. All before I walk into the call. That's the kind of automation Trigger.dev enables—stuff that would be a nightmare to build in Zapier or Make.

This guide covers what Trigger.dev actually does, why it matters for GTM Engineers specifically, and when you should use it over alternatives like Temporal or Inngest.

What Trigger.dev Actually Does

Trigger.dev is a platform for running background jobs—tasks that execute outside your main application, often triggered by events like webhooks, schedules, or API calls.

The core innovation: checkpoint-resume execution. Your tasks can run for minutes or hours without hitting serverless timeouts. When Trigger.dev saves a checkpoint, it can resume exactly where it left off, even if the underlying infrastructure cycles.

Key Features

• Long-running tasks: No 10-second or 5-minute limits. Tasks run as long as they need to.
• Parallel execution: Run multiple AI calls simultaneously and synchronize results.
• Human-in-the-loop: Pause workflows for human approval using waitpoint tokens.
• Plain TypeScript: Write tasks in your IDE with version control, GitHub Copilot, and real debugging tools.
• Managed or self-hosted: Use their cloud or deploy on your own infrastructure.

Company Background

Trigger.dev was founded in 2022 by Matt Aitken (CEO), Eric Allam, Dan Patel, and James Ritchie. The team came from JSON Hero—an open-source JSON viewer used by 35,000+ developers monthly.

The Problem It Solves

The Serverless Timeout Problem

Most developers build on serverless platforms like Vercel or AWS Lambda. These platforms have hard timeout limits:

AI workflows often need more time. A single LLM call can take 30+ seconds. Chain a few together with data enrichment, and you're way past these limits.

The workarounds suck: Background queues, message brokers, edge functions, polling mechanisms. Developers waste hours implementing infrastructure that shouldn't be their problem.

Trigger.dev calls this the "Timeout Tax"—the hidden cost of fighting serverless limits instead of building features.

The No-Code Scaling Problem

No-code tools like Zapier and Make work great for simple automations. Three steps, basic logic, done.

But they break at scale:

A real example: One retailer's Zapier workflow hit rate limits at 10,000 daily orders. They rebuilt it as a Node.js service that handled 100,000+ orders with batching, retries, and structured logging.

"No-code got them moving, but code got them scaling."

Where no-code breaks down:

• Rate limits and API throttling
• Complex error handling and retries
• Version control and collaboration
• Debugging multi-step workflows
• Cost at high volume (per-execution pricing adds up)

Why GTM Engineers Should Care

What Is a GTM Engineer?

A GTM (Go-to-Market) Engineer bridges sales, marketing, and technology. They're not purely technical, not purely commercial—their value comes from blending both.

GTM Engineers design, build, and automate repeatable systems that support go-to-market motions. They're becoming what DevOps became to software—the invisible engine driving speed and scalability.

Key Responsibilities

• System integration (CRM, marketing automation, data pipelines)
• Lead scoring and routing automation
• Data enrichment (firmographics, technographics, intent signals)
• Cross-functional alignment between product, marketing, sales, customer success

Why Trigger.dev Fits

GTM Engineers build complex automations that touch multiple systems. A typical workflow might:

1. Receive a webhook when a lead fills out a form
2. Enrich the lead with 5-10 data providers
3. Score the lead based on ICP fit
4. Route to the right rep based on territory
5. Create records in CRM, Slack, and task management
6. Trigger a personalized outreach sequence

That's 6+ steps with dependencies, error handling, and multiple API calls. No-code tools make this a visual nightmare. Trigger.dev lets you write it as clean TypeScript with proper version control.

Trigger.dev vs Alternatives

Three platforms dominate the TypeScript workflow orchestration space: Temporal, Inngest, and Trigger.dev.

Temporal

Best for: Large-scale batch processing, mission-critical workflows.

Temporal was created by Uber engineers as a fork of Cadence. It's the most battle-tested option for enterprise workloads.

Use case: Processing 100,000 product descriptions? A parent workflow spawns thousands of child workflows that run for days.

Tradeoff: Higher learning curve, more infrastructure overhead. You're managing more yourself.

Inngest

Best for: Event-driven, low-latency AI applications.

Inngest uses native language primitives for direct execution. Debugging is simpler than Temporal.

Tradeoff: Less community adoption (4,408 GitHub stars vs Trigger.dev's 13,023). Fewer resources and examples available.

Trigger.dev

Best for: TypeScript developers who want production AI agents without infrastructure complexity.

The positioning is "easier-to-use Temporal with integrations." You write tasks in plain async code, deploy to cloud or self-host, and let Trigger.dev handle the orchestration.

Key differentiator: The DX focus. Real IDE support, proper debugging, version control baked in. For developers who already know TypeScript, there's almost no learning curve.

When to Use Code vs No-Code

This isn't an either/or decision. It's about matching the tool to the job.

When No-Code Wins

• Prototypes and validation: Test an idea before investing in code
• Non-critical workflows: Internal notifications, simple data syncing
• Marketing/ops self-service: Let non-technical teams build their own automations
• Simple integrations: Less than 3 steps, straightforward logic

When Code Wins

• Performance matters: Optimize runtime, memory, latency
• Complex error handling: Custom retries, fallbacks, logging
• Legacy system integration: APIs that no-code tools don't support
• Compliance requirements: Audit trails, data governance
• Scale: Per-execution pricing becomes expensive at volume

The Decision Framework

Ask these questions:

1. Will this workflow change frequently? No-code is faster to iterate.
2. Does it need to handle edge cases? Code gives you control.
3. What's the volume? High volume = code for cost efficiency.
4. Who maintains it? Technical team = code. Mixed team = no-code.
5. Is it mission-critical? Critical = code for reliability.

Real-World Use Cases

My Meeting Research Workflow

When someone books a meeting with me, a webhook fires to Trigger.dev. The workflow:

1. Extracts company information from the booking
2. Fires 7 parallel research tasks via Exa
3. Analyzes results with DeepSeek
4. Structures the data and pushes to Notion
5. Sends me a Slack notification with the summary

All of this happens in the background before the meeting. I walk in knowing their company size, recent news, tech stack, and potential pain points.

Building this in Zapier would be a mess—7 parallel branches, API calls that timeout, no proper error handling. In Trigger.dev, it's ~100 lines of TypeScript.

MagicSchool AI

MagicSchool is the fastest-growing ed-tech company with 4.5M teachers on the platform.

Challenge: Teachers needed real-time insights into student AI interactions.

Solution: Trigger.dev task fires when an AI response completes → queries data → runs analysis via Vercel AI SDK → broadcasts to teachers via Supabase Realtime.

Result: 1 million+ student interactions summarized in weeks.

"With Trigger.dev, we've summarized over a million student interactions in just a couple of weeks." — Ben Duggan, Staff Software Engineer at MagicSchool AI

Icon.com

Icon uses Trigger.dev for AI ad creation at scale—generating thousands of custom video ads and static assets.

The workflow involves parallel processing, long-running video generation tasks, and reliable delivery. Exactly the kind of workload that would choke a no-code tool.

How to Get Started

Step 1: Install the CLI

npx trigger.dev@latest init

This scaffolds a new project or adds Trigger.dev to an existing one.

Step 2: Write Your First Task

import { task } from "@trigger.dev/sdk/v3";

export const helloWorld = task({
  id: "hello-world",
  run: async (payload: { name: string }) => {
    console.log(`Hello, ${payload.name}!`);
    return { message: `Greeted ${payload.name}` };
  },
});

Step 3: Deploy

npx trigger.dev@latest deploy

Your task is now running in Trigger.dev's cloud. Trigger it via webhook, schedule, or API call.

Step 4: Add AI Logic

import { task } from "@trigger.dev/sdk/v3";
import { generateText } from "ai";
import { openai } from "@ai-sdk/openai";

export const analyzeCompany = task({
  id: "analyze-company",
  run: async (payload: { companyName: string }) => {
    const result = await generateText({
      model: openai("gpt-4"),
      prompt: `Analyze ${payload.companyName} and identify their main pain points.`,
    });
    return { analysis: result.text };
  },
});

Resources

• Trigger.dev Documentation
• AI Agents Guide
• GitHub Repository
• MCP Integration (for Claude/Cursor users)

Frequently Asked Questions

Key Takeaways

• Trigger.dev is an open-source background jobs platform for TypeScript that solves serverless timeout limits and no-code scaling problems.
• Checkpoint-resume execution lets tasks run for minutes or hours without infrastructure headaches.
• GTM Engineers benefit most because they build complex, multi-system automations that break no-code tools at scale.
• Use Trigger.dev over Temporal when you want easier setup. Use it over Inngest when you want stronger community support.
• No-code still has its place for prototypes, simple workflows, and non-technical team self-service.
• Real production use cases include AI research pipelines, ed-tech analytics (MagicSchool AI), and creative asset generation (Icon.com).

The bottom line: if you're a developer building AI workflows that need to scale, Trigger.dev removes the infrastructure pain so you can focus on the actual product.