Trigger.dev vs n8n

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 with custom dashboards and a SQL-style query language. 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 n8n?

n8n is a workflow automation platform with a visual drag-and-drop builder for connecting services, transforming data, and automating business processes. It ships with hundreds of built-in integrations (called nodes) for services like Slack, Stripe, Google Sheets, and Salesforce, plus a community-built node ecosystem on npm. n8n is used by teams ranging from solo operators automating CRM workflows to enterprises running internal tooling. It can be self-hosted or run on n8n Cloud.

Visual builder vs code-first: what's the difference?

n8n workflows are visual graphs built in a canvas. Trigger.dev tasks are TypeScript functions in your codebase. This is the fundamental difference that shapes everything else: how you test, version, debug, and deploy.

n8n's visual canvas is genuinely fast for connecting services. Dragging a Slack node to a Google Sheets node to a Stripe node takes minutes, and the hundreds of built-in integrations cover most common services without writing code. For teams where operations staff or non-developers build automations, this is a real strength. Trigger.dev takes a different approach: your workflows are TypeScript functions that live in your repository alongside your application code. You get full IDE support (autocomplete, type checking, refactoring), standard git workflows (readable diffs, pull requests, branches), and any npm package works as an integration. For developer teams that want their workflows versioned, tested, and code-reviewed like the rest of their code, this is a natural fit.

How does the developer experience compare?

n8n optimizes for speed-to-first-workflow with its visual builder. Trigger.dev optimizes for long-term maintainability with code-first TypeScript.

n8n's canvas gives you instant visual feedback: you can see data flowing through nodes, inspect outputs at each step, and iterate without leaving the browser. For quick automations and prototyping, it's fast. n8n's API operates at the workflow level, so programmatic edits involve pulling the entire workflow JSON, modifying it, and writing it back. This can create issues like expression stripping and JSON encoding bugs that don't exist when your workflows are TypeScript files. Trigger.dev's developer experience is built around the tools TypeScript developers already use: VS Code, git, CI/CD pipelines. The MCP server works across development and production environments, letting AI editors search docs, write tasks, trigger runs, and deploy. Tasks compose with triggerAndWait and batchTriggerAndWait, and the TRQL query language lets you build custom dashboards or ask the AI assistant to generate queries from plain English. Trigger.dev doesn't have native webhook endpoints yet (planned). n8n handles webhook ingestion natively.

How does durability and reliability compare?

n8n workflows do not survive mid-execution failures and retry from the beginning. Trigger.dev provides durable execution with checkpoint-resume that restores your task from where it left off.

n8n workflows execute nodes sequentially, and if a node fails partway through, the workflow fails. You can configure error workflows that trigger on failure, retry executions via the API, or re-run manually from the canvas. For simple automations with fast-executing nodes, this is fine. Trigger.dev uses checkpoint-resume: when your task hits a wait point, the platform snapshots the entire task state and restores it later. A task that processes 1,000 documents and crashes at document 750 resumes from 750, not from scratch. Tasks can run for hours, days, or weeks with no timeout. For long-running background jobs, AI agent workflows, or anything where restarting from zero is expensive, this is a different category of reliability.

How do AI agent capabilities compare?

n8n has a deep set of LangChain-powered AI nodes for visual AI workflows. Trigger.dev runs any TypeScript AI framework natively with real-time streaming and human-in-the-loop.

n8n ships a deep LangChain-powered AI stack. The Tools Agent and Conversational Agent nodes wire up to OpenAI, Anthropic, Google, Mistral, or local models via Ollama. Vector store nodes cover Pinecone, Qdrant, Supabase, Weaviate, and more, with matching embeddings nodes for RAG pipelines. Memory nodes (Simple Memory for buffer/window plus per-store chat memory for Postgres, Redis, MongoDB, Motorhead, Xata, and Zep) give chatbots context across messages, and document loader nodes ingest PDFs, URLs, Google Drive, and databases into a single flow. For teams building retrieval-augmented agents or multi-model chains without writing glue code, it's a genuine advantage. Trigger.dev runs any TypeScript AI framework (AI SDK, Mastra, LangGraph.js, etc.) without wrappers or adapters. Realtime Streams push tokens and structured data to your frontend and backend, Waitpoints pause tasks for human approval (with configurable timeouts), and input streams let you send typed data into running tasks from your backend or frontend. Neither platform has built-in agent memory across runs, but Trigger.dev's durable execution means your agent survives crashes mid-conversation.

What infrastructure do you need to manage?

n8n Cloud hosts the UI and execution engine but runs your workflows in a shared environment. Trigger.dev runs each task in its own container with configurable CPU and RAM.

n8n's default architecture runs everything in a single Node.js process: the UI, webhooks, polling triggers, and workflow execution share the same resources. For production scale, queue mode distributes execution to separate worker processes via Redis. Both modes are available on self-hosted (including the free community edition). n8n Cloud handles hosting; Code nodes on Cloud run vanilla JS and Python without external package imports. Trigger.dev takes a managed compute approach: each task run gets its own container with configurable machine presets, so a memory spike in one task can't affect others. Build extensions add system-level dependencies (Prisma, FFmpeg, Playwright, Python) with a config line. Deploy with the CLI, GitHub auto-deploy, or the Vercel integration.

How does pricing compare?

n8n charges per workflow execution. Trigger.dev charges per compute-second plus a per-run fee. The models favor different workload shapes.

n8n's per-execution model is simple to predict: one workflow run equals one execution, regardless of how many nodes it touches or how long it takes. This works well for high-volume, fast-executing automations (send a Slack message when a form is submitted). Trigger.dev's compute-second model charges for actual CPU time, which favors workloads where execution time varies (AI summarization, video processing, data pipelines). Both offer free self-hosting. See n8n pricing and Trigger.dev pricing for current details.

How do self-hosting and licensing compare?

Both can be self-hosted, but the licenses are different. Trigger.dev is Apache 2.0. n8n uses a Sustainable Use License that restricts commercial embedding.

n8n's Sustainable Use License makes the source code available and allows free internal use, but prohibits embedding n8n in a competing product or reselling it as part of a commercial offering. Some features (environments, source control, SSO, LDAP) are gated behind paid plans, with Enterprise unlocking the full set. Trigger.dev is Apache 2.0 with no usage restrictions, and the self-hosted version shares the same core codebase as Trigger.dev Cloud. Cloud-only features include warm starts, auto-scaling, checkpoints, and dedicated support. Both platforms deploy with Docker. n8n uses SQLite by default and supports PostgresDB. Trigger.dev uses Docker Compose for small deployments or Kubernetes for production.

How does observability and debugging compare?

n8n shows per-node execution logs in the canvas. Trigger.dev provides OpenTelemetry tracing, custom dashboards, and a SQL-style query language across all your runs.

n8n's execution log shows input and output data at each node, which is useful for debugging individual workflow runs in the canvas. Execution logs are flat, though, so tracing data flow across Wait node boundaries or through branching logic can be difficult. Error workflows trigger when a workflow fails, letting you route failures to Slack or email. Trigger.dev shows every subtask as a linked run in the dashboard with its own logs, timing, and retry status, so you can trace a parent task through all its children. On top of that: TRQL for querying runs and metrics data, an AI assistant that generates queries from plain English, custom dashboards with charts and tables, and the SDK query API and REST API for pulling data into your own monitoring tools.