PE Collective
Which LLM Observability Platform Should You Use?
Comparing the two leading platforms for monitoring and evaluating AI applications
Last updated: March 2026
Quick Verdict
Choose LangSmith if: You are building LLM applications with LangChain and need deep tracing, prompt versioning, and evaluation tools designed specifically for LLM pipelines. LangSmith was built for the LLM application stack from day one.
Choose Weights & Biases if: You need a comprehensive ML platform that handles experiment tracking, model training, dataset management, and LLM monitoring under one roof. W&B Weave extends an established ML platform into LLM observability.
Feature Comparison
| Feature | LangSmith | Weights & Biases |
|---|---|---|
| LLM Trace Logging | ✓ Purpose-built for LLM chains | W&B Weave (newer) |
| Prompt Management | Built-in prompt hub | Artifact-based tracking |
| Evaluation Framework | LangSmith Evaluators | W&B Evaluate |
| Experiment Tracking | LLM-focused | ✓ Full ML experiment tracking |
| Dataset Management | Good (test datasets) | Excellent (W&B Artifacts) |
| Model Training Monitoring | Not supported | ✓ Core feature |
| LangChain Integration | ✓ Native (automatic tracing) | Manual integration |
| Framework Agnostic | Best with LangChain | Works with any framework |
| Community and Docs | Growing rapidly | Large, established |
Deep Dive: Where Each Tool Wins
🦜 LangSmith Wins: LLM-Native Observability
LangSmith was designed specifically for LLM application debugging. Every feature assumes you are building with language models: trace visualization shows each step in a chain, token counts and costs are tracked per-call, and the UI lets you replay any trace with different prompts. This focus means zero configuration for LangChain users and minimal setup for other frameworks.
The prompt management hub is a standout feature. Store prompt versions, compare performance across versions, and roll back to a previous version without redeploying your application. For teams iterating on prompts daily, this workflow saves significant time compared to managing prompts in code.
Evaluation in LangSmith is built around LLM-specific metrics: faithfulness, relevance, hallucination detection, and custom rubrics scored by judge LLMs. You define test datasets, run evaluations, and compare results across prompt versions or model changes. The entire loop (edit prompt, evaluate, compare, ship) lives in one platform.
📊 W&B Wins: Full ML Platform and Flexibility
Weights & Biases is a mature ML platform trusted by 95% of Fortune 500 companies. If your team trains models (fine-tuning, RLHF, custom classifiers), W&B handles experiment tracking, hyperparameter sweeps, and model registry alongside LLM monitoring. LangSmith only covers the LLM application layer.
W&B Artifacts provides robust dataset versioning that goes beyond LangSmith's test datasets. Track training data lineage, version evaluation datasets, and maintain reproducible experiment pipelines. For teams that care about data provenance and reproducibility, W&B's data management is significantly more mature.
Framework flexibility matters if you do not use LangChain. W&B Weave works equally well with LlamaIndex, custom Python code, or any other framework. LangSmith works outside LangChain, but the experience is noticeably better within the LangChain ecosystem. If your stack is diverse, W&B adapts more naturally.
Use Case Recommendations
🦜 Use LangSmith For:
- → LangChain-based LLM applications
- → Teams focused purely on LLM app development
- → Rapid prompt iteration and A/B testing
- → LLM pipeline debugging and tracing
- → Teams that need prompt version management
- → Production monitoring of LLM chains
📊 Use Weights & Biases For:
- → Teams doing model training AND LLM apps
- → Organizations already using W&B for ML
- → Multi-framework AI development
- → Research teams needing experiment tracking
- → Teams requiring robust dataset versioning
- → Enterprise ML platforms (Fortune 500)
Pricing Breakdown
| Tier | LangSmith | Weights & Biases |
|---|---|---|
| Free / Trial | Free (5K traces/mo) | Free (personal projects) |
| Individual | Plus: $39/seat/mo | Free for individuals |
| Business | Startup: ~1M traces/mo | Teams: $50/seat/mo |
| Enterprise | Custom pricing | Custom pricing |
Our Recommendation
For LLM Application Developers: If you build with LangChain, start with LangSmith. The native integration means automatic tracing with zero code changes. The prompt hub and evaluation tools are designed for exactly your workflow.
For ML/AI Teams: If your team trains models (fine-tuning, classifiers, custom models) in addition to building LLM applications, W&B covers both under one platform. LangSmith only addresses the LLM application layer.
The Bottom Line: LangSmith is the better LLM-specific observability tool. W&B is the better overall ML platform. Choose based on whether your work is purely LLM applications (LangSmith) or spans the full ML lifecycle (W&B).