Automation Integration · head to head
Autonoly vs Hatchet

Autonoly
Automation Integration
AI agents that build and run automation workflows from plain-English prompts
- From
- Free
- Rated
- -

Hatchet
Automation Integration
Postgres-backed distributed task queue and workflow engine for Python, TypeScript and Go
- From
- Free
- Rated
- -
The short version
- Each has a real cost: Autonoly pricing is credit-based, so heavy web-scraping or long-running workflows can consume the monthly allowance quickly and require add-on credit packs.; 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.
- They diverge on capability: Autonoly covers Natural language workflow builder, Hatchet covers Postgres-backed queue.
- Prices and features above were last checked on 31 August 2026.
Where they differ
Only the attributes on which Autonoly and Hatchet actually diverge.
Identical on both: starting price (Free), free tier (Yes), 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 Autonoly
- Natural language workflow builder
- Web scraping
- Reports and dashboards
- Python and code execution
- Scheduling
- App integrations
- Workflow analytics
Only in Hatchet
- Postgres-backed queue
- Durable execution
- DAG workflows
- Concurrency and fairness
- Rate limiting
- Cron and scheduling
- Multi-language SDKs
- Self-hosting
What people use each for
The jobs each tool is most often brought in to do.
Autonoly
- Scraping data from websites into spreadsheets or dashboardsnot Hatchet
- Generating scheduled Excel and PDF reports automaticallynot Hatchet
- Chaining multi-step workflows across business appsnot Hatchet
- Running Python scripts and lightweight ML pipelines on a schedulenot Hatchet
- Sending automated alerts and notifications to Slack, Discord, or emailnot Hatchet
Hatchet
- An AI product running long agent pipelines that must survive worker restarts and resume mid-workflownot Autonoly
- A team that outgrew Celery and wants concurrency keys, fairness and rate limiting without building themnot Autonoly
- An engineering group that wants durable execution self-hosted with no datastore beyond the Postgres they already runnot Autonoly
- A multi-tenant SaaS that needs one noisy customer's jobs not to starve everyone else'snot Autonoly
Where each one falls short
Documented limitations, not opinions. Every one is a constraint you would hit in normal use.
Autonoly
- Pricing is credit-based, so heavy web-scraping or long-running workflows can consume the monthly allowance quickly and require add-on credit packs.
- The free plan allows 1,000 credits and three concurrent workflows a month, which is enough to trial the product but not to run anything continuously.
- No published mobile apps (iOS/Android); the platform is web-based only.
- Company details such as founding date, founders, and team size are not disclosed on the site, making it harder to evaluate the vendor's track record.
- As an AI-agent-driven scraper/executor, reliability on sites with aggressive bot protection or frequently changing layouts may vary compared to purpose-built RPA tools.
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.
Pricing, plan by plan
Autonoly
Free- FreeFree
- 1,000 credits/month
- 3 concurrent AI agent workflows
- Community support
- Starter$29/month
- 10,000 credits/month
- 5 concurrent workflows
- 3 parallel agents
- Growth$59/month
- 30,000 credits/month
- 15 concurrent workflows
- 5 parallel agents
- Pro$99/month
- 60,000 credits/month
- 50 concurrent workflows
- 15 parallel agents
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
Which should you pick?
Choose Autonoly if
- You need natural language workflow builder.
- You want to start without paying.
- You work on web, api.
- You also want web scraping.
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.
Questions people ask
- Is Autonoly or Hatchet better?
- Neither clearly leads. Autonoly starts at Free and Hatchet at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
- Which is cheaper, Autonoly or Hatchet?
- Autonoly starts at Free and Hatchet at Free.
- Does Autonoly or Hatchet run on more platforms?
- Autonoly runs on web, api. Hatchet runs on Linux, Docker, Kubernetes, Cloud.
- Can I use Autonoly for free?
- Both have a free tier, so you can try either at no cost before committing.
- What is Autonoly best used for?
- Autonoly is most often used for scraping data from websites into spreadsheets or dashboards, generating scheduled excel and pdf reports automatically, chaining multi-step workflows across business apps, running python scripts and lightweight ml pipelines on a schedule. Of those, scraping data from websites into spreadsheets or dashboards and generating scheduled excel and pdf reports automatically are not what Hatchet is typically brought in for.
- What can Autonoly do that Hatchet cannot?
- Autonoly covers Natural language workflow builder, Web scraping, Reports and dashboards, Python and code execution. Hatchet covers Postgres-backed queue, Durable execution, DAG workflows, Concurrency and fairness.
Answered from the vendors’ own pages
Autonoly: How does Autonoly charge?
By credits rather than per task. Free covers 1,000 credits a month, Starter is $29, Growth $59 and Pro $99, with Enterprise priced on application. Simple actions cost 1 to 3 credits and advanced AI operations 5 to 15.
SourceHatchet: Is Hatchet open source?
Yes, the engine is MIT licensed and can be self-hosted, with a managed cloud offered alongside.
Autonoly: Is there a free plan?
Yes. It includes 1,000 credits a month and three concurrent AI agent workflows, with community support. No card is required to start.
SourceHatchet: 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.
Autonoly: What happens if I run out of credits?
Autonoly notifies you and suggests optimisations rather than stopping outright: you can upgrade at any time, or have non-critical workflows paused automatically until the allowance resets.
SourceHatchet: 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.
Autonoly: Can I change plan partway through a month?
Yes. Upgrades and downgrades take effect immediately and billing is prorated. Autonoly also offers 50% off paid plans for nonprofits.
SourceHatchet: 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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