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Automation Integration · head to head

Hatchet vs Relevance AI

Hatchet logo

Hatchet

Automation Integration

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

From
Free
Rated
-
Full reviewWebsite
Relevance AI logo

Relevance AI

Automation Integration

Specialist AI Agents for Every Task

From
On request
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.; Relevance AI no published standard pricing tiers or plans
  • Prices and features above were last checked on 31 August 2026.

Where they differ

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

Attributes where Hatchet and Relevance AI differ
AttributeHatchetRelevance AI
Starting priceFreeOn request
Pricing modelPer month by task run countusage-based
Free tierYesNo
PlatformsLinux, Docker, Kubernetes, CloudWeb

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 Relevance AI

Nothing recorded that Hatchet does not also cover.

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 Relevance AI
  • A team that outgrew Celery and wants concurrency keys, fairness and rate limiting without building themnot Relevance AI
  • An engineering group that wants durable execution self-hosted with no datastore beyond the Postgres they already runnot Relevance AI
  • A multi-tenant SaaS that needs one noisy customer's jobs not to starve everyone else'snot Relevance AI

Relevance AI

  • AI agent platform for automating business processes with LLMsnot Hatchet
  • Low-code automation and workflow orchestration using language modelsnot 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.

Relevance AI

  • No published standard pricing tiers or plans
  • Pay-per-task model pricing varies significantly by AI model selection
  • Enterprise quotes require direct contact with sales
  • Task costs range from 0.01 to 0.11 USD depending on model used

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

Relevance AI

On request

No published plan breakdown. See the Relevance AI review.

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 Relevance AI if

Nothing in the data separates Relevance AI from Hatchet on the points above - pick on price and on how each one feels to use.

Relevance AI review

Questions people ask

Is Hatchet or Relevance AI better?
Neither clearly leads. Hatchet starts at Free and Relevance AI at On request, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Hatchet or Relevance AI?
Hatchet has a free tier; the other does not. Paid plans start at Free for Hatchet and On request for Relevance AI.
Does Hatchet or Relevance AI run on more platforms?
Hatchet runs on Linux, Docker, Kubernetes, Cloud. Relevance AI runs on Web.
Can I use Hatchet for free?
Yes. Hatchet has a free tier, so you can try it without paying. Relevance AI starts at On request.
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 Relevance AI is typically brought in for.
What can Hatchet do that Relevance AI cannot?
Hatchet covers Postgres-backed queue, Durable execution, DAG workflows, Concurrency and fairness.

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.

Relevance AI: What is Relevance AI's pricing model?

Relevance AI uses a pay-per-task model where costs vary by AI model selected. Average cost per task is approximately 0.09 USD, with actual costs ranging from 0.01 to 0.11 USD depending on which model the system routes to. The platform automatically selects the lowest-cost model that meets performance requirements.

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.

Relevance AI: How much do Relevance AI tasks cost?

Task costs depend on model selection. Example usage shows customers spending 11800 USD monthly while processing 1.24 million tasks. For enterprise custom pricing, users must book a demo or contact the sales team.

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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