LangSmith vs Weights & Biases

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

LangSmith

🔴Developer

Business Analytics

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

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Starting Price

Free

Weights & Biases

🔴Developer

Business Analytics

Experiment tracking and model evaluation used in agent development.

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Starting Price

Free

Feature Comparison

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FeatureLangSmithWeights & Biases
CategoryBusiness AnalyticsBusiness Analytics
Pricing Plans15 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

  • Comprehensive observability with detailed trace visualization
  • Native MCP support for universal agent tool deployment
  • Generous free tier for individual developers and small projects
  • No-code Agent Builder reduces technical barriers
  • Managed deployment infrastructure with production-ready scaling
  • Strong integration with entire LangChain ecosystem

Cons

  • Primarily designed for LangChain applications (limited framework support)
  • Steep pricing jump from Plus to Enterprise tier
  • Pay-as-you-go model can become expensive for high-volume applications
  • Enterprise features require annual contracts
  • 14-day retention on base traces may be insufficient for some use cases

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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