Hatchet
UnclaimedRun fast and reliable data pipelines for context engineering and AI agents.
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TL;DR - Hatchet
- A distributed workflow orchestrator for AI agents and data pipelines.
- Offers high performance, durability, and code-first development with SDKs.
- Supports ingestion, AI agent orchestration, and massive parallelization.
Pricing: Free plan available
Best for: Growing teams
What is Hatchet?
Hatchet is a distributed workflow orchestrator designed for building resilient and scalable data pipelines, particularly for AI agents and context engineering. It allows developers to define tasks and workflows as code using language-native SDKs (Python, TypeScript, Go), ensuring versionable, reusable, and testable atomic functions. The platform focuses on low-latency, high-throughput workloads, with features like smart assignment rules for rate limits, fairness, and priorities, and durable logging for every task invocation.
Hatchet addresses common challenges in AI and data processing, such as keeping vector databases and knowledge graphs up-to-date, orchestrating complex AI agent behaviors, and parallelizing massive data processing tasks. It offers automatic retries, intelligent rate limiting, checkpoint recovery, and built-in eventing for human-in-the-loop signaling. The orchestration engine can be used as a managed service or self-hosted, with workers deployed on various container platforms, scaling automatically based on workload. It's ideal for scale-ups and enterprises needing robust, fault-tolerant, and high-performance workflow management.
Available on: Web
Pros & Cons
Pros
- Significantly reduces failed runs and improves reliability for data pipelines.
- Enables efficient processing of large datasets and parallel execution.
- Simplifies complex AI agent orchestration and state management.
- Offers flexible deployment options (managed or self-hosted).
- Code-first approach promotes maintainability and testability.
Cons
- Specific pricing details for additional usage are not fully transparent on the pricing page for all tiers.
- Requires some technical expertise to integrate and deploy workers.
- Advanced features like custom SLAs and compliance (SOC 2, HIPAA, BAA) are only available on the Enterprise plan.
Preview
Key Features
Low-latency, high-throughput task execution (<20ms start times)Durable logging of task invocations with checkpoint recoveryCode-first SDKs for Python, TypeScript, and GoAutomatic retries and intelligent rate limitingExactly-once semantics for updating vector databasesBuilt-in orchestration primitives for AI agents (tool calls, timeouts, state management)Eventing for human-in-the-loop signaling and streaming responsesFan-out to thousands of workers with single function calls
Pricing Plans
Pricing checked Jul 31, 2026
Free
$0/mo
- For testing and small-scale experimentation
- Task Runs: 10/s
- Concurrent Runs: 2k
- Included Usage Task Runs: 2k/day
- Active Storage: 1 GB
- Network Bandwidth: 10 GB
- Compute Credits: $5/mo
- Public Discord Support: Included
- Data Retention: 1 day
- Events: 1k/day
- Max Workers: 1
- Users: 1
Starter
$180/mo
- For smaller systems starting to face scaling challenges
- Task Runs: 100/s
- Concurrent Runs: 10k
- Included Usage Task Runs: 20k/day
- Active Storage: 10 GB
- Network Bandwidth: 100 GB
- Compute Credits: $25/mo
- Public Discord Support: Included
- Private Shared Slack Support: Included
- Data Retention: 3 days
- Events: 20k/day
- Max Workers: 50
- Users: 3
Growth
$425/mo
- For larger services experiencing especially tricky scaling problems.
- Task Runs: 500/s
- Concurrent Runs: 100k
- Included Usage Task Runs: 100k/day
- Active Storage: 100 GB
- Network Bandwidth: 1 TB
- Compute Credits: $100/mo
- Additional Usage Task Runs: $10/million
- Public Discord Support: Included
- Private Shared Slack Support: Included
- Onboarding: Included
- Data Retention: 7 days
- Events: 100k/day
- Max Workers: 200
- Users: 10
Enterprise
Contact
- For especially complex systems with unique requirements.
- Task Runs: 500-10k/s
- Concurrent Runs: 100k-1M
- Included Usage Task Runs: Custom
- Active Storage: Custom
- Network Bandwidth: Custom
- Compute Credits: Custom
- Additional Usage Task Runs: Custom
- Additional Usage Active Storage: Custom
- Additional Usage Network Bandwidth: Custom
- Public Discord Support: Included
- Private Shared Slack Support: Included
- Onboarding: Included
- SLAs: Custom SLAs
- Data Retention: Custom
- SOC 2: Available
- HIPAA: Available
- BAA: Available
- Events: Custom
- Max Workers: Custom
- Users: Custom
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Hatchet FAQ
How does Hatchet support context engineering for AI agents?
Hatchet is designed to build resilient and scalable data pipelines specifically for AI agents and context engineering. It helps keep vector databases and knowledge graphs up-to-date, which is crucial for providing accurate context to AI models. The platform orchestrates complex AI agent behaviors and parallelizes massive data processing tasks.
Which teams would benefit most from using Hatchet?
Hatchet is ideal for scale-ups and enterprises that require robust, fault-tolerant, and high-performance workflow management. It particularly suits teams working with AI agents, data processing, and ETL pipelines who need to ensure reliability and scalability.
How does Hatchet compare to Temporal for workflow orchestration?
Hatchet, like Temporal, is a distributed workflow orchestrator, but Hatchet specifically emphasizes its utility for AI agents and context engineering. Hatchet focuses on low-latency, high-throughput workloads with features like smart assignment rules for rate limits and priorities, and durable logging for every task invocation.
What kind of limitations should users be aware of with Hatchet?
Hatchet requires some technical expertise to integrate and deploy its workers effectively. Additionally, advanced features such as custom SLAs and compliance certifications like SOC 2, HIPAA, or BAA are exclusively available with the Enterprise plan.
Does Hatchet include a free tier?
Yes, Hatchet offers a free tier for users to get started with the platform. Paid plans are available for those who require more extensive usage and additional features beyond what the free tier provides.
How does Hatchet ensure the reliability of data pipelines?
Hatchet significantly reduces failed runs and improves reliability for data pipelines through features like automatic retries, intelligent rate limiting, and checkpoint recovery. It also provides durable logging for every task invocation, aiding in fault tolerance and debugging.
Can Hatchet be deployed in different environments?
Yes, Hatchet offers flexible deployment options, allowing it to be used as a managed service or self-hosted. Workers can be deployed on various container platforms and are designed to scale automatically based on the workload.
Source: hatchet.run