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Automation Integration · head to head

Hatchet vs Mage AI

Hatchet logo

Hatchet

Automation Integration

Postgres-backed distributed task queue and workflow engine for Python, TypeScript and Go

From
Free
Rated
-
Full reviewWebsite
Mage AI logo

Mage AI

Automation Integration

Data pipeline platform with AI-generated workflows and governance

From
$100/month
Rated
-
Full reviewWebsite

The short version

  • Only Hatchet has a free tier, so it costs nothing to try first.
  • Each has a real cost: Hatchet postgres as the queue backend simplifies operations but sets a throughput ceiling, and very high task rates require database tuning or partitioning work that a purpose-built queue would not demand.; Mage AI usage-based pricing lacks transparency for cost forecasting
  • They diverge on capability: Hatchet covers Postgres-backed queue, Mage AI covers AI-generated workflows.
  • Prices and features above were last checked on 31 August 2026.

Where they differ

Only the attributes on which Hatchet and Mage AI actually diverge.

Attributes where Hatchet and Mage AI differ
AttributeHatchetMage AI
Starting priceFree$100/month
Pricing modelPer month by task run countUsage-based cloud platform
Free tierYesNo
PlatformsLinux, Docker, Kubernetes, CloudCloud, Hybrid, Private Cloud, On-Premises

Identical on both: user rating (Not yet rated), category (Automation Integration).

What each one covers

Drawn from each product's published feature list. An absence here means we hold no record of it - not that the product lacks it.

Only in Hatchet

  • Postgres-backed queue
  • Durable execution
  • DAG workflows
  • Concurrency and fairness
  • Rate limiting
  • Cron and scheduling
  • Multi-language SDKs
  • Self-hosting

Only in Mage AI

  • AI-generated workflows
  • Pipeline building
  • Data validation
  • Workflow orchestration
  • Automatic recovery
  • Reusable components
  • Governance controls

What people use each for

The jobs each tool is most often brought in to do.

Hatchet

  • An AI product running long agent pipelines that must survive worker restarts and resume mid-workflownot Mage AI
  • A team that outgrew Celery and wants concurrency keys, fairness and rate limiting without building themnot Mage AI
  • An engineering group that wants durable execution self-hosted with no datastore beyond the Postgres they already runnot Mage AI
  • A multi-tenant SaaS that needs one noisy customer's jobs not to starve everyone else'snot Mage AI

Mage AI

  • Building and orchestrating data pipelines with visual interfacenot Hatchet
  • Automating ETL workflows with AI assistancenot Hatchet
  • Validating data quality across transformationsnot Hatchet
  • Distributing transformed data to multiple destinationsnot Hatchet

Where each one falls short

Documented limitations, not opinions. Every one is a constraint you would hit in normal use.

Hatchet

  • Postgres as the queue backend simplifies operations but sets a throughput ceiling, and very high task rates require database tuning or partitioning work that a purpose-built queue would not demand.
  • Data retention on the managed tiers is three days on Team and seven on Scale, which is short for debugging intermittent failures and forces you to export run history elsewhere.
  • The step from the free tier to Team is 500 US dollars a month plus usage, which is a large increment for a small team that simply needs a production SLA.
  • Hatchet is a young company in a category with Temporal, Inngest and cloud-native alternatives already established, so ecosystem, hiring pool and third-party material are all thin.
  • Self-hosting the MIT engine is realistic but you then run the engine, its Postgres and its workers yourself, and the managed features such as audit logs and support are not part of that.

Mage AI

  • Usage-based pricing lacks transparency for cost forecasting
  • Limited standalone pricing details on website
  • Requires contact for enterprise deployment options
  • Smaller ecosystem compared to established competitors
  • May require significant customization for complex data models

Pricing, plan by plan

Hatchet

Free
  • DeveloperFree
    • First 100,000 task runs included
    • $10 per million task runs after
    • No card required
  • Team$500/month
    • Usage billed on top of the base fee
    • 10 users and 5 tenants
    • 3 day data retention
  • Scale$1000/month
    • Unlimited users and tenants
    • 7 day data retention
    • Audit logs
  • Enterprise$undefined/year
    • 300 million plus runs a month
    • Latency guarantees and custom SLAs
    • SSO and audit logging

Mage AI

$100/month
  • Cloud$100/month
    • 1 development environment
    • Unlimited users
    • Usage-based infrastructure pricing
  • Hybrid Cloud$null/custom
    • Private data processing
    • Custom infrastructure
    • Contact sales
  • Private Cloud$null/custom
    • Complete isolation
    • Full infrastructure control
    • Contact sales
  • On-Premises$null/custom
    • Full local control
    • Enterprise deployment
    • Contact sales

Which should you pick?

Choose Hatchet if

  • You need postgres-backed queue.
  • You want to start without paying.
  • You work on Linux, Docker, Kubernetes, Cloud.
  • You also want durable execution.
Hatchet review

Choose Mage AI if

  • You need ai-generated workflows.
  • You work on Cloud, Hybrid, Private Cloud, On-Premises.
  • You also want pipeline building.
Mage AI review

Questions people ask

Is Hatchet or Mage AI better?
Neither clearly leads. Hatchet starts at Free and Mage AI at $100/month, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Hatchet or Mage AI?
Hatchet has a free tier; the other does not. Paid plans start at Free for Hatchet and $100/month for Mage AI.
Does Hatchet or Mage AI run on more platforms?
Hatchet runs on Linux, Docker, Kubernetes, Cloud. Mage AI runs on Cloud, Hybrid, Private Cloud, On-Premises.
Can I use Hatchet for free?
Yes. Hatchet has a free tier, so you can try it without paying. Mage AI starts at $100/month.
What is Hatchet best used for?
Hatchet is most often used for an ai product running long agent pipelines that must survive worker restarts and resume mid-workflow, a team that outgrew celery and wants concurrency keys, fairness and rate limiting without building them, an engineering group that wants durable execution self-hosted with no datastore beyond the postgres they already run, a multi-tenant saas that needs one noisy customer's jobs not to starve everyone else's. Of those, an ai product running long agent pipelines that must survive worker restarts and resume mid-workflow and a team that outgrew celery and wants concurrency keys, fairness and rate limiting without building them are not what Mage AI is typically brought in for.
What can Hatchet do that Mage AI cannot?
Hatchet covers Postgres-backed queue, Durable execution, DAG workflows, Concurrency and fairness. Mage AI covers AI-generated workflows, Pipeline building, Data validation, Workflow orchestration.

Answered from the vendors’ own pages

Hatchet: Is Hatchet open source?

Yes, the engine is MIT licensed and can be self-hosted, with a managed cloud offered alongside.

Mage AI: What is the starting price for Mage Cloud?

Cloud plan starts at $100/month for one development environment with unlimited users, plus usage-based compute charges.

Source
Hatchet: What does it cost?

Free for the first 100,000 task runs, then 10 US dollars per million. Team is 500 US dollars a month plus usage; Scale is 1,000.

Mage AI: How is compute usage billed on Mage?

CPU is charged at $0.50 per hour and RAM at $0.50 per 4GB per hour of utilization.

Source
Hatchet: Why Postgres as the queue?

So durable task state lives in a database your team already backs up, replicates and can inspect with SQL, instead of a second system.

Hatchet: How does it compare with Temporal?

Lighter to adopt and operate, with a much smaller ecosystem and a lower throughput ceiling from the Postgres backend.

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