Updated March 2026

Trigger.dev vs Temporal

Long-running workflows need to survive crashes and restarts. Temporal solves this with deterministic replay across seven languages, proven at Netflix-scale with advanced workflow primitives like Signals and Queries. Trigger.dev uses checkpoint-resume to run normal TypeScript with no determinism constraints, processing millions of tasks for thousands of teams on managed infrastructure. Choose Temporal for polyglot enterprise orchestration. Choose Trigger.dev for TypeScript teams that want to ship fast without managing workers.

What is Trigger.dev?

Trigger.dev is a managed compute platform for building AI agents and workflows in TypeScript. Your code runs as normal TypeScript with no determinism constraints and no execution timeout. It includes built-in retries, queues, scheduling, and OpenTelemetry observability. Tasks can stream data to your frontend in real-time, receive typed input while running, and pause indefinitely for human approval or external events. Thousands of engineering teams run production workloads on it, processing millions of runs.

What is Temporal?

Temporal is a durable execution platform that guarantees your code runs to completion through crashes and outages using event-sourced deterministic replay. It has official SDKs for Go, Java, Python, TypeScript, .NET, Ruby, and Swift, and is used in production by Netflix, OpenAI, Stripe, and JPMorgan Chase. Temporal Cloud has processed over 9.1 trillion lifetime actions.

Feature deep-dive

How does developer experience compare?

Temporal requires you to understand Workflows, Activities, Workers, Task Queues, and Namespaces before you ship. Trigger.dev starts with tasks; queues, workers, and environments are managed for you.

Feature	Trigger.dev	Temporal
Minimum building blocks	Tasks	Workflows + Activities
Execution	Workers (managed or self-hosted)	Workers (self-hosted)
Routing	Queues	Task Queues
Isolation	Projects + Environments	Namespaces
Code constraints	None	Deterministic workflows required
Boilerplate	Minimal: just the task function	Activity stubs, worker setup, workflow registration
Error debugging	Dashboard with trace viewer, run filtering, and custom views	Event history + replay debugging
Type safety	Full TypeScript types	Full TypeScript types (with proxy indirection)
Primary language	TypeScript-native	Go, Java, Python, TypeScript, .NET, Ruby, Swift
Production CLI	`login`, `init`, `dev`, `deploy`	`workflow`, `batch`, `schedule`, `operator`, `task-queue`
Onboarding	`init` into your project or a blank folder	Tutorials + sample apps for each SDK
Versioning	Deploy new code, new runs use it. No patching needed	Workflow versioning / patching for in-flight deterministic code
Child tasks	`triggerAndWait` / `batchTriggerAndWait`	Child workflows with parent-close policies
AI coding tools	MCP server (docs, metrics, write tasks, trigger, monitor, deploy), agent rules, skills, llms.txt	MCP server (start, signal, cancel, inspect workflows)
Local development	Hot reload, requires internet	Fully offline dev server

Temporal splits work across Workflows, Activities, Workers, Task Queues, and Namespaces, each unlocking patterns like Signals, Queries, and child workflows. Trigger.dev has one concept: tasks. Built-in retries, queues with concurrency keys, scheduled tasks, batch triggering, a Realtime API with React hooks, and OpenTelemetry tracing. Run npx trigger.dev@latest init and you're writing tasks in minutes. No determinism constraints means deploys are simple: new runs use the new version, in-progress runs finish on theirs. Temporal requires explicit versioning when workflow logic changes. Tasks can spawn other tasks and wait for results with triggerAndWait. Trigger.dev also ships an MCP server , agent rules, and skills for Claude Code, Cursor, Windsurf, and more. Temporal has an MCP server focused on workflow operations (starting, signaling, canceling, and inspecting workflows).

How does the build and deploy pipeline compare?

Trigger.dev deploys with a single command and manages build dependencies through config. Temporal workers are your build pipeline to own.

Feature	Trigger.dev	Temporal
Build customization	Build extensions (Prisma, FFmpeg, Playwright, Python, more) or write your own	Manage your own Dockerfile
Deploy integrations	GitHub + Vercel: auto-deploy on push, env var sync	Manage your own CI/CD pipeline
Environments	Production, Staging, Preview (per-branch), Development	Namespaces (manual setup)

Build extensions add system-level dependencies (Prisma, FFmpeg, Playwright, Python, system packages) with a config line, and you can create custom extensions for anything not covered. The GitHub integration auto-deploys on every push, and the Vercel integration syncs environment variables and supports atomic deploys. Trigger.dev includes built-in production, staging, and development environments, plus preview branches that create isolated environments per PR with their own API key, env vars, and schedules, auto-archived when the PR merges. Temporal uses namespaces, but environment management is manual.

How does durability work in each platform?

Both guarantee your code survives crashes and restarts, but Temporal replays events from scratch while Trigger.dev restores memory snapshots.

Feature	Trigger.dev	Temporal
Durability model	Checkpoint-resume snapshots	Event-sourcing with deterministic replay
Determinism required	No	Yes: no `Date.now()`, `Math.random()`, or direct I/O
Recovery mechanism	Restore from checkpoint	Replay from event history
Long-running workflows	Checkpoints grow with process memory	`continue-as-new` resets event history

Temporal uses event-sourcing with deterministic replay. Every workflow action is recorded as an event, and on recovery the workflow replays from the start, skipping completed activities. This gives Temporal a complete, replayable audit trail and powers advanced patterns like continue-as-new for extremely long-running workflows. Trigger.dev uses checkpoint-resume instead. When your task reaches a wait point, the platform snapshots your task's state and restores it later. The tradeoff is losing the replayable event history, but your code runs as plain TypeScript with no determinism rules. There are no limits on execution time, you don't need to do workarounds like "continue-as-new"

How do AI agent capabilities compare?

Temporal has deep Python AI integration through the OpenAI Agents SDK and multi-language agent orchestration. Trigger.dev is purpose-built for TypeScript AI agents with real-time streaming, human-in-the-loop, and no timeout.

Feature	Trigger.dev	Temporal
AI framework support	Any TS framework (AI SDK, Mastra, LangGraph.js, etc.)	OpenAI Agents SDK (Python, public preview)
Real-time streaming	Realtime Streams (SSE to frontend)	No built-in streaming
Human-in-the-loop	Waitpoints (pause for approval, webhooks, events)	Signals (external data into workflows)
Bidirectional communication	Input streams (typed data into running tasks from backend or frontend)	Signal/Query/Update
Max execution time	No limit	No limit
Multi-language agents	TypeScript only	Go, Java, Python, TypeScript, .NET

Temporal's AI story is strongest in Python, where the OpenAI Agents SDK wraps agent interactions as durable workflow steps with activity_as_tool, and Signal/Query/Update lets external systems push data into running agent workflows or query their state. If your AI agents are in Python or span multiple languages, Temporal is the natural fit. Trigger.dev takes a TypeScript-native approach: any AI framework (AI SDK, Mastra, LangGraph.js) works without wrappers, Realtime Streams push tokens to your frontend over SSE, and Waitpoints pause tasks indefinitely for human approval or external events, and input streams let you send typed data into running tasks from your backend or frontend (cancel signals, approvals, or any structured payload).

What infrastructure do you need to manage?

Temporal gives you full control over your worker infrastructure, even on Cloud. Trigger.dev runs your code on managed compute with nothing to provision.

Feature	Trigger.dev	Temporal
Managed option	Trigger.dev Cloud (fully managed)	Temporal Cloud (server managed, workers are yours)
Self-hosted minimum	Docker Compose	Database + Elasticsearch + Server + Workers
Workers to deploy	None	At least one per task queue
Compute isolation	Each run gets its own container with configurable CPU/RAM	Activities share worker process resources
Time to production	~5 minutes	Days to weeks (self-hosted) or hours (Cloud)
License	Apache 2.0	MIT (server)

Temporal's architecture separates the server (orchestration) from workers (your code), giving you full control over where and how your code runs. Teams with strict compliance, data residency, or custom runtime needs require this level of control. Temporal Cloud manages the server side; you manage workers. Trigger.dev takes a different approach: write your task, run npx trigger.dev@latest deploy, and your code runs on managed compute with nothing to provision or scale. Each run gets its own container with a configurable machine preset (up to 4 vCPU, 8 GB RAM), so a memory spike in one task can't affect others. Temporal workers share resources across activities on the same task queue, isolation requires dedicating separate task queues and worker pools. Both offer self-hosting: Trigger.dev via Docker Compose or Kubernetes (Apache 2.0), Temporal via its MIT-licensed server. Temporal Nexus connects workflows across namespace and region boundaries with typed service contracts, used by Netflix for cross-team orchestration. Trigger.dev has no equivalent.

How does pricing compare at scale?

Temporal bills per action across every workflow event. Trigger.dev bills per compute-second plus a per-run fee. The models favor different workload shapes.

Feature	Trigger.dev	Temporal
Pricing model	Compute-seconds + per-run fee	Per-action (every timer, signal, activity is billable)
Free tier	Free plan with $5 per month compute credit	$1,000 trial credits
Self-hosted cost	Free (Apache 2.0) + your infra	Free (MIT) + significant infra
Essentials	—	$100/month, 1M actions included
Business	—	$500/month, 2.5M actions included

Temporal's per-action model works well for workflows with few steps. A workflow with 5 activities may consume 15-20 actions, and the Essentials tier includes 1M actions per month. For high-volume, simple workflows, actions stay predictable. Trigger.dev's compute-second model charges for actual execution time, which tends to favor long-running tasks like AI summarization where a single task may run for minutes.Example: 100,000 AI summarization tasks per month at 30 seconds average. With Temporal, those tasks at ~8 actions each = 800K actions, within the Essentials tier. With Trigger.dev, you pay for ~833 compute-hours. The models favor different shapes: Temporal is cost-effective for many short workflows, Trigger.dev for fewer long-running ones. Note that Temporal Cloud pricing covers orchestration only; you still pay separately for the compute to run your workers. Self-hosting either platform eliminates cloud fees entirely.

How do self-hosting and licensing compare?

Trigger.dev licenses its entire platform under Apache 2.0, while Temporal uses an MIT license for its server.

Feature	Trigger.dev	Temporal
License	Apache 2.0	MIT (server)
Minimum setup	Docker Compose	Database + Elasticsearch + Server + Workers
Production complexity	Docker Compose or Kubernetes	Database cluster + Elasticsearch + multiple services
Managed alternative	Trigger.dev Cloud	Temporal Cloud (starts $100/month)

This server runs the exact full-featured codebase that powers Temporal Cloud, letting teams with dedicated platform engineering run it at massive scale. The setup involves a database cluster (PostgreSQL or Cassandra), Elasticsearch, and multiple server services. Trigger.dev self-hosts with Docker Compose for small deployments or Kubernetes for production, with the entire platform under Apache 2.0. Your choice between them comes down to how much operational surface area your team wants to manage.

Code comparison

Normal TypeScript vs deterministic workflows

Trigger.dev (1 file)

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

export const myTask = task({
  id: "my-task",
  run: async (payload: { url: string }) => {
    const now = Date.now(); // fine
    const id = crypto.randomUUID(); // fine
    const data = await fetch(payload.url); // fine
    return { now, id, data: await data.json() };
  },
});

Temporal (2 files)

// workflow.ts
import { proxyActivities } from '@temporalio/workflow';
import type * as activities from './activities';

const { fetchData } = proxyActivities<typeof activities>({
  startToCloseTimeout: '5 minutes',
});

export async function myWorkflow(url: string) {
  const data = await fetchData(url);
  return { data };
}

// activities.ts
export async function fetchData(url: string) {
  const response = await fetch(url);
  const now = Date.now();
  const id = crypto.randomUUID();
  return { now, id, data: await response.json() };
}

Normal TypeScript vs deterministic workflows

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

export const myTask = task({
  id: "my-task",
  run: async (payload: { url: string }) => {
    const now = Date.now(); // fine
    const id = crypto.randomUUID(); // fine
    const data = await fetch(payload.url); // fine
    return { now, id, data: await data.json() };
  },
});

AI document summarizer

Trigger.dev (1 file)

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

export const summarizeDoc = task({
  id: "summarize-doc",
  run: async (payload: { documentUrl: string }) => {
    const doc = await fetch(payload.documentUrl).then(r => r.text());

    const { text } = await generateText({
      model: anthropic("claude-sonnet-4-20250514"),
      prompt: `Summarize this document:\n\n${doc}`,
    });

    return { summary: text };
  },
});

Temporal (3 files + worker)

// workflow.ts: must be deterministic (no fetch, no LLM calls here)
import { proxyActivities } from '@temporalio/workflow';
import type * as activities from './activities';

const { fetchDocument, summarizeWithLLM } = proxyActivities<typeof activities>({
  startToCloseTimeout: '10 minutes',
});

export async function summarizeDocWorkflow(
  documentUrl: string
): Promise<{ summary: string }> {
  const doc = await fetchDocument(documentUrl);
  const summary = await summarizeWithLLM(doc);
  return { summary };
}

// activities.ts: side effects go here
import { generateText } from 'ai';
import { anthropic } from '@ai-sdk/anthropic';

export async function fetchDocument(url: string): Promise<string> {
  const response = await fetch(url);
  return response.text();
}

export async function summarizeWithLLM(content: string): Promise<string> {
  const { text } = await generateText({
    model: anthropic('claude-sonnet-4-20250514'),
    prompt: `Summarize this document:\n\n${content}`,
  });
  return text;
}

// worker.ts: you deploy and operate this separately
import { NativeConnection, Worker } from '@temporalio/worker';
import * as activities from './activities';

async function run() {
  const connection = await NativeConnection.connect({
    address: process.env.TEMPORAL_ADDRESS ?? 'localhost:7233',
  });
  const worker = await Worker.create({
    connection,
    workflowsPath: require.resolve('./workflow'),
    activities,
    taskQueue: 'summarize-queue',
  });
  await worker.run();
}

run().catch(console.error);

AI document summarizer

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

export const summarizeDoc = task({
  id: "summarize-doc",
  run: async (payload: { documentUrl: string }) => {
    const doc = await fetch(payload.documentUrl).then(r => r.text());

    const { text } = await generateText({
      model: anthropic("claude-sonnet-4-20250514"),
      prompt: `Summarize this document:\n\n${doc}`,
    });

    return { summary: text };
  },
});

What developers say about Trigger.dev

We moved our workflows from Temporal to Trigger.dev and went from 87% to 100% success. Temporal workers couldn't stay stable with bursty loads and FFmpeg CPU spikes, which caused queue-depth throttling and missed activities. Trigger.dev handles the load without degrading.

Andreas Asprou

Trigger.dev is one of those tools that you set up once and then never have to worry about again. Previously, we had a homegrown cron job solution. Not good.

Read Magic Patterns's story

Alex Danilowicz

We found software that runs in Node.js environments. Combined with Trigger's tasks and runs, it solved our problem instantly. We are now easily processing around 6,000 documents per month.

Read Papermark's story

Marc Seitz

Frequently asked questions

Can I migrate from Temporal to Trigger.dev?

Yes. The main work is removing the Workflow/Activity split and converting to Trigger.dev tasks. Since Trigger.dev has no determinism constraints, most Temporal Activities can be inlined directly into task code. The worker infrastructure can be decommissioned entirely.

Does Trigger.dev support multiple languages like Temporal?

Temporal has native SDKs for Go, Java, Python, .NET, Ruby, and Swift. Trigger.dev is TypeScript-native, with a build extension for running Python scripts alongside your TypeScript tasks.

Is Temporal's deterministic replay actually a problem in practice?

Non-determinism errors are a common pain point in Temporal's community: calling Date.now(), using libraries with internal randomness, or making API calls without Activity wrappers. Once you learn the rules it's manageable, but the learning curve is real. Trigger.dev uses a different durability model, so your code runs as plain TypeScript with no replay constraints.

Can Trigger.dev handle Temporal-level scale?

Trigger.dev processes millions of runs for thousands of engineering teams and scales automatically with no worker infrastructure to manage. Temporal Cloud has processed over 9.1 trillion lifetime actions for companies like Netflix and Stripe. For most workloads, both platforms are more than capable.

Does Temporal have better observability?

Different approaches. Temporal's event history provides a replayable record of every workflow execution, which is powerful for debugging deterministic workflows. Trigger.dev provides OpenTelemetry tracing, a dashboard with trace viewer, run filtering, and custom views. Both have web dashboards for monitoring runs.

Which is better for AI agents specifically?

Depends on your language. Temporal has deep Python AI integration through the OpenAI Agents SDK. For TypeScript AI agents, Trigger.dev supports any framework (AI SDK, Mastra, LangGraph.js) without modification, with Realtime Streams, input streams , Waitpoints for human-in-the-loop, and no execution timeout.

Can I use Temporal's Signal/Query/Update pattern with Trigger.dev?

Partially. Trigger.dev's input streams and Waitpoints cover the Signal use case: send typed data into a running task from your backend or frontend. Realtime Streams cover reading task output. Queries (inspecting workflow state) and Updates (validated state mutations) don't have a direct equivalent.

Which platform is more future-proof?

Both are open source and actively developed. Temporal has $650M in funding , deep enterprise traction, and SDKs across seven languages. Trigger.dev has $16M in Series A funding, thousands of teams in production, and is growing fast in the TypeScript and AI agent ecosystem. Your code isn't locked in either way.

Can Trigger.dev replace Temporal for TypeScript background tasks?

For TypeScript teams running background tasks, data pipelines, or AI workflows, yes. Trigger.dev offers a simpler model with no determinism constraints, no worker infrastructure, and deploys in minutes. Temporal provides additional patterns like Signal/Query/Update and multi-language support that some architectures depend on.

Product

AI Agents

Trigger.dev Realtime

Concurrency & queues

Scheduled tasks

Observability & monitoring

Roadmap

Latest changelogs

Vercel integration

Trigger.dev v4.4.3

Query & Dashboards: analytics for your Trigger.dev data

Latest blog posts

How we give every user SQL access to a shared ClickHouse cluster

Skills: teaching AI agents to act consistently

How Tierly builds AI-powered pricing intelligence using Trigger.dev

Documentation

Guides

Trigger.dev vs Temporal

What is Trigger.dev?

What is Temporal?

Feature deep-dive

How does developer experience compare?

How does the build and deploy pipeline compare?

How does durability work in each platform?

How do AI agent capabilities compare?

What infrastructure do you need to manage?

How does pricing compare at scale?

How do self-hosting and licensing compare?

Code comparison

Normal TypeScript vs deterministic workflows

Normal TypeScript vs deterministic workflows

AI document summarizer

AI document summarizer

What developers say about Trigger.dev

Frequently asked questions

Can I migrate from Temporal to Trigger.dev?

Does Trigger.dev support multiple languages like Temporal?

Is Temporal's deterministic replay actually a problem in practice?

Can Trigger.dev handle Temporal-level scale?

Does Temporal have better observability?

Which is better for AI agents specifically?

Can I use Temporal's Signal/Query/Update pattern with Trigger.dev?

Which platform is more future-proof?

Can Trigger.dev replace Temporal for TypeScript background tasks?