Hatchet
UnclaimedRun fast and reliable data pipelines for context engineering and AI agents.
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What is Hatchet?
Hatchet is a big data analytics tool. 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. Key capabilities: Low-latency, high-throughput task execution (<20ms start times), Durable logging of task invocations with checkpoint recovery, Code-first SDKs for Python, TypeScript, and Go, Automatic retries and intelligent rate limiting, Exactly-once semantics for updating vector databases. Hatchet ships a free plan plus paid tiers that unlock as usage grows. Buyers most often compare Hatchet against Prefect, Meltano, Vercel MCP.
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
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
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
About Hatchet
By Toolradar Team·
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.
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Hatchet FAQ
What is Hatchet?
Hatchet is a distributed workflow orchestrator designed to run fast and reliable data pipelines for context engineering and AI agents. It helps manage complex, high-throughput workloads with features like automatic retries, intelligent rate limiting, and durable task logging.
How much does Hatchet cost?
Hatchet offers a freemium model. There is a Free tier for testing and small-scale experimentation. Paid plans include Starter at $180/month, Growth at $425/month, and a custom Enterprise plan for larger requirements. Pricing is based on throughput limits, included usage (task runs, active storage, network bandwidth, compute credits), and additional usage costs.
Is Hatchet free?
Yes, Hatchet offers a Free tier that includes 10 task runs/second throughput, 2k task runs/day, 1GB active storage, and 10GB network bandwidth, suitable for testing and small-scale experimentation.
Who is Hatchet for?
Hatchet is designed for scale-ups and enterprises that need to build resilient, high-performance data pipelines for use cases such as ingestion and indexing, AI agent orchestration, and massive parallelization of tasks like document processing, data enrichment, and GPU workload scheduling.
Source: hatchet.run