ClearML vs LangSmith: Which is Better in 2026?
A comprehensive comparison of ClearML and LangSmith 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 LangSmith if:
- →You need full trace visualization for chains and agents or prompt hub and versioning
ClearML vs LangSmith: At a Glance
Pricing Comparison: ClearML vs LangSmith
Understanding the pricing differences between ClearML and LangSmith is crucial for making the right choice. Here's how their plans compare side by side.
💡 Pricing takeaway: Both ClearML and LangSmith 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 LangSmith stacks up.
What Makes Each Tool Unique
🔵 Unique to ClearML
Features available in ClearML but not in LangSmith:
- ✓Automatic experiment tracking
- ✓Dataset versioning
- ✓Pipeline orchestration
- ✓Model registry
- ✓Remote execution and autoscaling
🟣 Unique to LangSmith
Features available in LangSmith but not in ClearML:
- ✓Full trace visualization for chains and agents
- ✓Prompt hub and versioning
- ✓Evaluation datasets and automated tests
- ✓Production monitoring
- ✓Dataset curation
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: LangSmith
LLMOps platform by LangChain for debugging, testing, evaluating, and monitoring LLM applications. LangSmith provides full trace visibility into complex chains, agents, and RAG pipelines built with LangChain or any framework.
Ideal use cases:
- •Teams or individuals who need full trace visualization for chains and agents
- •Teams or individuals who need prompt hub and versioning
- •Teams or individuals who need evaluation datasets and automated tests
- •Teams or individuals who need production monitoring
- •Anyone focused on LLMOps workflows
- •Anyone focused on LangChain workflows
🤖 Other AI Agent Infrastructure Tools to Consider
ClearML and LangSmith 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.
Frequently Asked Questions
Is ClearML better than LangSmith?
It depends on your needs. ClearML offers 5 key features including Automatic experiment tracking and Dataset versioning, while LangSmith provides 5 features including Full trace visualization for chains and agents and Prompt hub and versioning. ClearML uses a freemium model with a free tier, while LangSmith is freemium with free access available. Choose based on which features and pricing model align with your requirements.
Is ClearML cheaper than LangSmith?
Both tools have similar pricing structures. 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 LangSmith together?
Yes, many users combine ClearML and LangSmith in their workflow. ClearML excels at automatic experiment tracking, while LangSmith shines with full trace visualization for chains and agents. 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 LangSmith?
While both are ai agent infrastructure tools, ClearML emphasizes automatic experiment tracking, whereas LangSmith is known for full trace visualization for chains and agents. The best choice depends on your specific workflow and feature priorities.