LangSmith vs Weights & Biases

Detailed side-by-side comparison to help you choose the right tool

LangSmith

🔴Developer

Analytics & Monitoring

Tracing, evaluation, and observability for LLM apps and agents.

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Starting Price

Free

Weights & Biases

🔴Developer

Analytics & Monitoring

Experiment tracking and model evaluation used in agent development.

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Starting Price

Free

Feature Comparison

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FeatureLangSmithWeights & Biases
CategoryAnalytics & MonitoringAnalytics & Monitoring
Pricing Plans16 tiers11 tiers
Starting PriceFreeFree
Key Features
  • Workflow Runtime
  • Tool and API Connectivity
  • State and Context Handling
  • Workflow Runtime
  • Tool and API Connectivity
  • State and Context Handling

LangSmith - Pros & Cons

Pros

  • Best-in-class LLM tracing and debugging platform
  • Deep integration with LangChain ecosystem
  • Powerful evaluation and testing workflows for prompt development
  • Dataset management for building evaluation harnesses
  • Visual trace viewer makes debugging complex chains intuitive

Cons

  • Most valuable when used with LangChain — less useful standalone
  • Paid plans required for team features and higher volume
  • Data sent to LangSmith's servers — privacy considerations
  • Can add overhead to development workflow

Weights & Biases - Pros & Cons

Pros

  • Experiment comparison and visualization capabilities are unmatched — parallel coordinate plots, metric distributions, and run comparisons across thousands of experiments
  • Unified platform for both traditional ML training and LLM evaluation eliminates tool sprawl for teams doing both
  • W&B Tables provide collaborative data exploration with filtering, sorting, and custom visualizations of evaluation results
  • Mature team collaboration with workspaces, reports, and sharing makes it easier to coordinate across ML and LLM teams

Cons

  • LLM-specific features (Weave) feel newer and less polished than W&B's core ML experiment tracking capabilities
  • Platform complexity is high — the learning curve for teams that only need LLM observability is steeper than purpose-built alternatives
  • Pricing can be expensive for larger teams; the free tier has usage limits that active teams hit quickly
  • LLM framework integrations (LangChain, LlamaIndex) are functional but shallower than those in dedicated LLM tools

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🔒 Security & Compliance Comparison

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Security FeatureLangSmithWeights & Biases
SOC2✅ Yes✅ Yes
GDPR✅ Yes✅ Yes
HIPAA
SSO✅ Yes✅ Yes
Self-Hosted🔀 Hybrid🔀 Hybrid
On-Prem✅ Yes✅ Yes
RBAC✅ Yes✅ Yes
Audit Log✅ Yes✅ Yes
Open Source❌ No❌ No
API Key Auth✅ Yes✅ Yes
Encryption at Rest✅ Yes✅ Yes
Encryption in Transit✅ Yes✅ Yes
Data ResidencyUS, EUUS, EU
Data Retentionconfigurableconfigurable
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