ClearML vs Hatchet: Which is Better in 2026?
A comprehensive comparison of ClearML and Hatchet covering features, pricing, use cases, and which tool is the right choice for your needs.
⚡ Quick Verdict
Choose ClearML if:
- →You need automatic experiment tracking or dataset versioning
Choose Hatchet if:
- →You want more affordable paid plans (from $10/mo)
- →You need a broader feature set (6 features vs 5)
- →You need durable execution that resumes rather than restarts or cron scheduling, event and webhook triggers, data ingestion
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ClearML vs Hatchet: At a Glance
Pricing Comparison: ClearML vs Hatchet
Understanding the pricing differences between ClearML and Hatchet is crucial for making the right choice. Here's how their plans compare side by side.
Hatchet Pricing
💡 Pricing takeaway: Both ClearML and Hatchet offer free tiers, making it easy to try before you buy. Visit each tool's website for the latest pricing details.
Feature-by-Feature Comparison
Here's how every feature from ClearML and Hatchet stacks up.
What Makes Each Tool Unique
🔵 Unique to ClearML
Features available in ClearML but not in Hatchet:
- ✓Automatic experiment tracking
- ✓Dataset versioning
- ✓Pipeline orchestration
- ✓Model registry
- ✓Remote execution and autoscaling
🟣 Unique to Hatchet
Features available in Hatchet but not in ClearML:
- ✓Durable execution that resumes rather than restarts
- ✓Cron scheduling, event and webhook triggers, data ingestion
- ✓Idempotency keys and batch tasks
- ✓Run-level observability with status, duration and metadata
- ✓Priced at $10 per million task runs
- ✓Open source and self-hostable, SOC 2 Type II from the free tier
Use Case Recommendations
Best for: ClearML
Open-source MLOps platform for experiment tracking, pipeline orchestration, and model deployment. ClearML automatically logs experiments, version datasets, and orchestrates compute — without locking you into proprietary infrastructure.
Ideal use cases:
- •Teams or individuals who need automatic experiment tracking
- •Teams or individuals who need dataset versioning
- •Teams or individuals who need pipeline orchestration
- •Teams or individuals who need model registry
- •Anyone focused on MLOps workflows
- •Anyone focused on experiment tracking workflows
Best for: Hatchet
Hatchet is an open-source orchestration platform for AI agents, background tasks and workflows that have to survive failure. The core capability is durable execution: a task's progress is checkpointed so a crash, redeploy or timeout resumes rather than restarts, which is the property that separates an agent you can run in production from one you babysit. Around that sit the operational pieces such systems always need and rarely have — cron scheduling, event and webhook triggers, data ingestion pipelines, idempotency keys, batch tasks, throughput controls measured in requests per second, and observability over every run with searchable status, duration and metadata. The dashboard shown on the homepage is the honest tell about who this is for: teams whose problem is not writing the workflow but knowing which of the last hundred thousand runs failed and why. Hatchet is genuinely open source and can be self-hosted, with cloud plans layered on for teams that would rather not run the infrastructure. The cloud pricing model is a base plus usage at $10 per million task runs with the first 100,000 free, which is unusually easy to reason about for this category — most competitors price on compute-seconds or concurrency slots that require a modelling exercise to convert into a bill. SOC 2 Type II is included from the free tier, with HIPAA and audit logs at Scale, and the platform reports processing over 100 million tasks a day.
Ideal use cases:
- •Teams or individuals who need durable execution that resumes rather than restarts
- •Teams or individuals who need cron scheduling, event and webhook triggers, data ingestion
- •Teams or individuals who need idempotency keys and batch tasks
- •Teams or individuals who need run-level observability with status, duration and metadata
- •Anyone focused on orchestration workflows
- •Anyone focused on durable-execution workflows
🤖 Other AI Agent Infrastructure Tools to Consider
ClearML and Hatchet aren't the only options. Here are other popular tools in the same space:
SuperAGI
Open-source autonomous AI agent framework with visual dashboard — 14K GitHub stars
MetaGPT
Multi-agent AI framework simulating software teams — 45K GitHub stars, builds full apps from prompts
Cerebras
Fastest LLM inference powered by the Wafer Scale Engine.
Scale AI
AI data platform for training data and model evaluation.
Roboflow
End-to-end computer vision platform for developers.
Labelbox
Enterprise data labeling platform for ML training datasets.
Is one of these your tool?
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Frequently Asked Questions
Is ClearML better than Hatchet?
It depends on your needs. ClearML offers 5 key features including Automatic experiment tracking and Dataset versioning, while Hatchet provides 6 features including Durable execution that resumes rather than restarts and Cron scheduling, event and webhook triggers, data ingestion. ClearML uses a freemium model with a free tier, while Hatchet is freemium with free access available. Choose based on which features and pricing model align with your requirements.
Is ClearML cheaper than Hatchet?
ClearML doesn't have standard paid plans, while Hatchet starts at $10/month. Both tools offer free tiers, so you can try each before committing. Always check the official websites for the most current pricing.
Can I use ClearML and Hatchet together?
Yes, many users combine ClearML and Hatchet in their workflow. ClearML excels at automatic experiment tracking, while Hatchet shines with durable execution that resumes rather than restarts. Using both allows you to leverage the strengths of each tool, though this means managing two subscriptions — though free tiers can help manage costs.
What's the main difference between ClearML and Hatchet?
While both are ai agent infrastructure tools, ClearML emphasizes automatic experiment tracking, whereas Hatchet is known for durable execution that resumes rather than restarts. The best choice depends on your specific workflow and feature priorities.
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