Hatchet
by Hatchet · Orchestration engine for AI agents, background tasks and workflows
BenchRank score
Screenshots of Hatchet
Homepage
Overview
Hatchet is an orchestration engine for background tasks, AI agents and multi-step workflows. Tasks are written as functions in TypeScript, Python or Go and composed into workflows with DAGs, conditions, retries, cron schedules and event triggers. The engine runs as a managed cloud service or self-hosted, while workers run on your own container platform such as Kubernetes, Docker or ECS.
- Best for
- Engineering teams orchestrating AI agents and background tasks in TypeScript, Python or Go
- Pricing
- Developer tier is free with the first 100,000 runs included, then $10 per 1M task runs; Team is $500/month and Scale $1,000/month, both plus usage, with Enterprise priced on request.
- Runs on
- WebmacOSSelf-hostedCLI
Strengths and trade-offs
Strengths
- MIT-licensed and self-hostable; runs locally from one CLI command
- Native SDKs for TypeScript, Python and Go
- Built-in OpenTelemetry traces, Prometheus metrics and log search
- Task history kept for automatic retries, manual replay and debugging
Trade-offs
- Short cloud retention: 3 days on Team, 7 days on Scale
- Workers run on your own infrastructure, which you operate and scale
- Audit logs and HIPAA start at the $1,000/month Scale plan
- SSO, custom SLAs and self-host support are Enterprise, priced on request
How Hatchet markets itself
A structured read of the promise, proof and page design on Hatchet’s captured homepage.
Homepage capture
“The orchestration engine for teams who ship”
- Angle: Developer-first
- Hero: Product screenshot
Pricing
Published plans and prices from Hatchet’s own pricing page.
How this score is made up
Each dimension is scored out of 100 and combined into the headline score using fixed weights.
MCP support
Whether an agent can drive the product through the Model Context Protocol, and how much setup that takes.
API quality
Public API surface: machine-readable spec, official SDKs, documented auth, errors, rate limits and versioning.
Documentation
Publicly reachable docs — coverage, freshness, code samples and machine readability.
Agent friendliness
How readable the site is to an automated client: llms.txt, structured data, server-rendered content, crawler access.
Pricing transparency
Whether real prices are published, self-serve signup exists, and usage costs are knowable without a sales call.
Changelog
A public, dated record of what shipped and when — the clearest signal that a product is still alive.
Marketing site structure
Whether the site answers a buyer's questions: clear positioning, the pages that matter, and accessibility.
Page speed
How fast the site loads for real visitors: Chrome UX Report 75th-percentile LCP, INP and CLS, with a Lighthouse mobile run standing in where a site has too little traffic for field data.
Operational trust
Status page and incident history, security disclosure, compliance and data-processing documentation.
Measured, but not part of the score
Useful to know, but not a mark for or against the product — so these do not affect the ranking.
Openness
Source availability, self-hosting, data export and open standards. Scored and shown, but not part of the composite — paid SaaS is not worse for being paid SaaS.
Maintenance
Release cadence and repository activity. Scored and shown, but not part of the composite — it is only measurable for open repositories.
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Where this comes from
The Hatchet pages BenchRank reads when it scores the product — its documentation, release notes, status and security pages, and its repository where there is one.
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