Hatchet vs Celery

Hatchet9.4Visit Celery8.6Visit

Higher score Hatchet #1 in Task Queue Software 9.4/10 Free plan Free plan✓ 0 of 3 features Visit Hatchet

vs

Celery #7 in Task Queue Software 8.6/10 Open source ✓ 3 of 3 features Visit Celery

• Hatchet fits best

  Observable durable workflows and AI tasks

• Celery fits best

  Python teams needing mature distributed tasks

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Side by side

Feature	Hatchet 9.4/10 Visit ↗	Celery 8.6/10 Visit ↗
At a glance
Editor score	9.4	8.6
Ranking	#1 in Task Queue Software	#7 in Task Queue Software
Best for	Observable durable workflows and AI tasks	Python teams needing mature distributed tasks
Pricing model	Free plan + paid	Free
Starting price	$500/mo	Open source
Free plan	✓ (best)	Not checked yet
Free trial	—	—
Deployment	Cloud, Self-hosted	Self-hosted
Platforms	Web	Linux, macOS
Support	Email, Community, Docs	Community, Docs
Integrations	7 integrations	10 integrations
Built for	Small business, Mid-market, Enterprise	Small business, Mid-market, Enterprise
Features Hatchet 0/3 · Celery 3/3
Custom retry policy	Not checked yet	✓ (best)
Scheduled jobs	Not checked yet	✓ (best)
Dead-letter queue	Not checked yet	✓ (best)
Specs
Deployment model	Hybrid	Self_hosted
Maximum payload size	Not checked yet	Not checked yet
Official SDK languages	Not checked yet	Python
Our review
Pros	Durable execution with retries, scheduling, DAGs, and replay Detailed traces, logs, metrics, alerting, and preserved run data Cloud, self-hosted, and hybrid deployment with AI SDK integrations	Runs tasks across worker processes and machines Flexible retries, scheduling, routing, and concurrency options Broad broker, backend, database, and search integrations
Cons	Team starts at $500/month before usage charges Retention is 3 days on Team and 7 days on Scale Advanced compliance and enterprise controls require higher tiers	Official SDK coverage is Python-only Teams manage workers, brokers, and result stores themselves Dead-letter handling depends on broker configuration
Our verdict	Hatchet is a developer platform for AI agents, durable workflows, background tasks, and event-driven applications. Engineering teams define workflows in code, while workers run on customer infrastructure and the orchestration engine runs… Read the review →	Celery is an open-source, self-hosted distributed task queue for Python applications. It lets applications submit asynchronous work through a message broker while workers execute tasks across processes or machines. The feature set suits… Read the review →

Strengths and trade-offs

• Hatchet — where it wins

  • Durable execution with retries, scheduling, DAGs, and replay
  • Detailed traces, logs, metrics, alerting, and preserved run data
  • Cloud, self-hosted, and hybrid deployment with AI SDK integrations

  Where it doesn't

  • Team starts at $500/month before usage charges
  • Retention is 3 days on Team and 7 days on Scale
  • Advanced compliance and enterprise controls require higher tiers

• Celery — where it wins

  • Runs tasks across worker processes and machines
  • Flexible retries, scheduling, routing, and concurrency options
  • Broad broker, backend, database, and search integrations

  Where it doesn't

  • Official SDK coverage is Python-only
  • Teams manage workers, brokers, and result stores themselves
  • Dead-letter handling depends on broker configuration

• Hatchet9.4/10 · Free plan

  A code-first queue for durable execution, observable workflows, and AI task orchestration.

  Visit HatchetFull verdict →
• Celery8.6/10 · Open source

  A mature self-hosted queue for Python teams that need distributed task control.

  Visit CeleryFull verdict →