# Hatchet

**Source:** https://geo.sig.ai/brands/hatchet-run  
**Vertical:** Developer Tools  
**Subcategory:** Workflow Orchestration  
**Tier:** Emerging  
**Website:** hatchet.run  
**Last Updated:** 2026-04-14

## Summary

Hatchet is an open-source distributed task queue with Python, TypeScript, and Go SDKs, offering retries, rate limiting, and concurrency controls for reliable background jobs at scale.

## Company Overview

Hatchet is an open-source distributed task queue and workflow orchestration platform designed for engineering teams that need reliable, observable background job execution at production scale with the flexibility to self-host on their own infrastructure. The platform provides a strongly-typed task definition model where engineers define workers and workflows in code — using Python, TypeScript, or Go SDKs — with first-class support for retries, timeouts, rate limiting, concurrency controls, and priority queuing built into the framework rather than bolted on through application logic. This opinionated approach to task definition ensures that reliability patterns are consistently applied across all background work in an application.

Hatchet's dag-based workflow model allows teams to define directed acyclic graph (DAG) workflows where multiple tasks execute in parallel when their dependencies are resolved, enabling efficient orchestration of complex multi-step processes like data pipelines, document processing, and AI inference chains. The platform's dashboard provides real-time visibility into running workers, task queues, workflow execution state, and failure diagnostics, reducing the operational overhead of managing distributed background work across a microservices architecture. Detailed replay and debugging tools allow engineers to re-run failed workflow runs with their original inputs for rapid iteration on failure resolution.

Hatchet targets platform engineering teams and senior backend developers at growth-stage and enterprise companies who are hitting the limits of simpler job queue systems like BullMQ, Celery, or cloud-native queuing services and need workflow orchestration with production-grade observability. The open-source self-hosted model is a key differentiator for teams with data residency requirements or cost sensitivity around task volume pricing. Hatchet competes with Temporal, Inngest, and Trigger.dev in the developer workflow orchestration category.

## Frequently Asked Questions

### What types of workflows is Hatchet designed to handle?
Hatchet handles DAG-based parallel workflows — multi-step processes like data pipelines, AI inference chains, and document processing — where tasks execute concurrently when dependencies resolve, with built-in retries, concurrency controls, and priority queuing at each step.

### What is Hatchet and how does it differ from traditional job queues?
Hatchet is an open-source distributed task queue and workflow orchestration platform. Unlike basic queues like Sidekiq or BullMQ that handle simple job execution, Hatchet provides first-class support for multi-step DAG workflows, concurrency controls, priority queuing, rate limiting, and observable job execution — all defined in application code.

### Is Hatchet open source?
Yes. Hatchet is fully open source under the MIT license. It can be self-hosted using Docker or Kubernetes. Hatchet Cloud is a managed deployment option for teams that want Hatchet without infrastructure management.

### What languages does Hatchet support?
Hatchet provides SDKs for Python, TypeScript/Node.js, and Go, covering the most common backend languages for web applications and data engineering workflows.

### How does Hatchet handle AI inference pipelines?
Hatchet is well-suited for AI inference workflows where steps involve external LLM calls or GPU inference. Concurrency controls prevent overloading API rate limits, priority queuing ensures high-priority requests are processed first, and durable step execution retries failed inference calls without restarting the entire pipeline.

### Does Hatchet support event-driven workflows?
Yes. Hatchet workflows can be triggered by external events pushed to the Hatchet event bus, enabling event-driven architectures where business events (user signup, payment completed, document uploaded) automatically trigger multi-step processing workflows.

### How does Hatchet provide observability for background jobs?
Hatchet includes a real-time web dashboard showing workflow execution status, step-level timing, error details, and queue depth for each worker. This visibility into background job execution is typically missing from basic queue libraries that provide no built-in observability.

### How does Hatchet compare to Temporal or Inngest?
All three are durable workflow platforms. Hatchet differentiates with a self-hosted-first model, MIT licensing, and strong Python and Go SDK support. Temporal has a larger enterprise feature set and broader language support; Inngest is serverless-first and deeper into Next.js and edge function integration.

## Tags

developer-tools, open-source, saas, b2b, startup, platform, infrastructure, cloud-native, automation, api-first

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*Data from geo.sig.ai Brand Intelligence Database. Updated 2026-04-14.*