Hatchet AI

Hatchet AI is an open-source distributed task queue and workflow orchestration platform built for large-scale background job processing that requires high reliability and observability. By offering persistent queues, complex workflow (DAG) orchestration and real-time monitoring, it helps developers simplify asynchronous job management and data processing pipelines.

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distributed task queueworkflow orchestration platformbackground job processingopen-source task orchestratorAI job schedulinghigh-reliability task queueasync job orchestrationdata processing pipeline

Features of Hatchet AI

Persistent task queue backed by PostgreSQL to ensure jobs aren’t lost and to support automatic retries.

Define and manage complex, dependency-aware workflows using a DAG (directed acyclic graph) model.

Web console for visual monitoring of task status, performance metrics and execution logs.

Multiple queue scheduling policies, including FIFO, LIFO, round-robin and priority queues.

Traffic control features such as concurrency limits and rate limiting to protect system stability.

Support for scheduled jobs, delayed execution and one-off task scheduling.

Official SDKs for Python, TypeScript and Go to simplify integration.

Supports both cloud-hosted and Docker-based self-hosted deployment options.

Use Cases of Hatchet AI

Fairly schedule AI inference requests and orchestrate agent tasks when building generative AI applications.

Batch process large volumes of documents or images, e.g., indexing and content transformation.

Coordinate multiple microservices or multi-stage jobs in complex event-driven applications.

Smoothly handle asynchronous traffic spikes to level load and preserve system responsiveness.

Replace traditional task queue systems when you need stronger reliability and state management for backend jobs.

FAQ about Hatchet AI

QWhat is Hatchet AI?

Hatchet AI is an open-source distributed task queue and workflow orchestration platform for managing and executing large-scale background jobs and complex data-processing workflows with strong reliability guarantees.

QWhat is Hatchet AI mainly used for?

It helps developers build and manage large-scale asynchronous jobs and workflows that require reliable execution, complex dependency handling and state tracking—common scenarios include AI job scheduling and data batch processing.

QHow does Hatchet AI ensure tasks aren’t lost?

Tasks are persisted in a PostgreSQL database and the platform provides automatic retries and error-handling mechanisms, aiming for at-least-once delivery of jobs.

QWhich programming languages does Hatchet AI support?

Official SDKs are available for Python, TypeScript and Go, making it easy to integrate with different technology stacks.

QCan Hatchet AI be self-hosted?

Yes. Hatchet AI supports Docker-based self-hosted deployment and also offers cloud-hosted options.

QIs there a cost to use Hatchet AI?

The platform offers a free starter tier; details about feature limits and paid plans are available on the official pricing page.

QHow is Hatchet AI different from traditional message queues (like Celery)?

Hatchet AI focuses on being a complete task orchestration platform, with built-in DAG workflow orchestration, persistent state management and advanced observability—suited for scenarios with more complex dependencies and higher reliability requirements.

QIs Hatchet AI suitable for real-time tasks?

Hatchet AI is designed for background asynchronous processing. It supports low-latency scheduling, but its core strengths are reliable orchestration and batch processing rather than ultra-low-latency real-time request handling.

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