Problem

Modern applications increasingly depend on long-running, failure-prone background work: AI agents, document and data pipelines, scheduled jobs, and multi-tenant task queues. Engineering teams often end up stitching together cron systems, queues, retries, concurrency controls, monitoring, and recovery logic themselves. The result is unreliable execution, difficult debugging, wasted compute, and infrastructure that becomes especially expensive when workloads must scale in parallel or run for minutes at a time.

Hatchet’s clearest use case is production AI and document-processing workflows where a failed task can force costly reprocessing or delay customer deliverables. Its customer examples illustrate the payoff: Aevy reports processing 50,000 documents in under an hour instead of nearly a week, while Greptile reports a 50% reduction in failed runs. Hatchet is therefore aimed at converting fragile, bespoke orchestration into a durable execution layer that improves throughput, reliability, and engineering productivity.

Product / Service

Hatchet is an orchestration platform combining a durable task queue, workflow engine, monitoring interface, API, and developer SDKs. Developers define tasks and workflows in Python, TypeScript, Go, or Ruby; Hatchet persists task and agent state, coordinates dependencies, applies retries and timeouts, schedules work, manages fairness and concurrency, and supports replay, streaming, logging, alerting, and observability. Because execution is durably recorded, long-running agents and mission-critical workflows can recover from failures rather than restarting from scratch.

The delivery model separates customer workloads from the orchestration control plane. Workers run on the customer’s infrastructure, while the Hatchet engine is available through Hatchet Cloud or as a self-hosted, MIT-licensed open-source deployment on container platforms such as Kubernetes and Docker. The commercial cloud offering lets teams start without operating the control plane, while self-hosting provides greater control; the published pricing model starts at $10 per one million task runs, with the first 100,000 runs free.

Market

Hatchet competes in developer infrastructure for workflow orchestration, durable execution, distributed task queues, and AI-agent operations. Its comparison set includes Temporal and Inngest, as well as other durable-execution platforms, but Hatchet differentiates around open-source self-hosting, production-scale parallelism, fine-grained concurrency and fairness controls, and a unified experience for AI agents, background jobs, and mission-critical workflows.

The company is not merely pre-product: Hatchet was founded in 2023, joined Y Combinator’s Winter 2024 batch, and has a managed Cloud business alongside its open-source project. Its documentation reports billions of tasks processed per month, more than 10,000 open-source deployments per month, and hundreds of companies using Hatchet Cloud; its site also states that AI-first companies run more than 100 million tasks per day on the platform. These figures indicate meaningful adoption, although no revenue or valuation figure is provided in the available research.