LLM Evaluation Platforms & Tools
Compare evaluation platforms: LangSmith, Braintrust, Arize, W&B Weave, Langfuse for building evaluation infrastructure
What are LLM Evaluation Platforms?
LLM evaluation platforms are specialized tools that help you trace, evaluate, and monitor AI applications. They provide infrastructure for running evaluations, storing results, comparing experiments, and building dashboards—so you don't have to build these capabilities from scratch.
Why use a platform? Building evaluation infrastructure from scratch requires significant engineering effort: data pipelines, storage, UI, experiment tracking, and integration with CI/CD. Platforms provide this out-of-the-box, letting you focus on evaluation logic instead of infrastructure.
Key capabilities: Tracing, prompt management, evaluation datasets, LLM-as-Judge integration, A/B testing, monitoring dashboards, and CI/CD integration.
Platform Comparison
| Platform | Best For | Key Strength | Pricing |
|---|---|---|---|
| LangSmith | LangChain ecosystem users | Deep LangChain integration | Free tier + paid plans |
| Braintrust | Prompt experimentation | Evaluation-first design | Free tier + usage-based |
| Arize Phoenix | Enterprise data lakes | Zero-copy data integration | Open-source + enterprise |
| W&B Weave | ML teams with W&B | Auto-tracking @weave.op | W&B pricing |
| Langfuse | Privacy-first, open-source | Self-hostable, transparent | Open-source + cloud |
| Deepchecks | CI/CD integration | ML testing pipelines | Open-source + enterprise |
LangSmith
Key Features
- • Deep LangChain integration: Automatic tracing for chains, agents
- • Prompt hub: Version control and sharing for prompts
- • Datasets: Create and manage evaluation datasets
- • Online evaluation: Real-time production monitoring
- • Playground: Test prompts interactively
Best For
- • Teams already using LangChain/LangGraph
- • Complex chain and agent debugging
- • Prompt iteration with version control
- • Production monitoring for LangChain apps
Braintrust
Key Features
- • Evaluation-first: Built around experiment comparison
- • Side-by-side: Compare outputs across prompts/models
- • Auto-evaluators: Built-in LLM-as-Judge evaluators
- • Scoring functions: Custom evaluation metrics
- • CI integration: GitHub Actions support
Best For
- • Rapid prompt iteration and A/B testing
- • Teams focused on evaluation quality
- • Framework-agnostic LLM applications
- • Small-to-medium scale experimentation
Langfuse (Open Source)
Key Features
- • Self-hostable: Run on your own infrastructure
- • Open source: MIT license, transparent codebase
- • Framework agnostic: Works with any LLM setup
- • Prompt management: Version and track prompts
- • Cost tracking: Monitor LLM spend
Best For
- • Privacy-sensitive deployments
- • Teams wanting full control over data
- • Compliance requirements (HIPAA, GDPR)
- • Budget-conscious organizations
Platform Selection Decision Tree
Using LangChain/LangGraph?
→ LangSmith: Deep integration, automatic tracing, best debugging experience
Focus on prompt experimentation?
→ Braintrust: Best evaluation UX, side-by-side comparisons, built-in evaluators
Need self-hosting or open source?
→ Langfuse: Full feature parity, self-host option, MIT license
Already using Weights & Biases?
→ W&B Weave: Native integration, auto-tracking, familiar interface
Enterprise with existing data lake?
→ Arize Phoenix: Zero-copy integration, enterprise features, MLOps focus
Heavy CI/CD integration needs?
→ Deepchecks: Native CI/CD support, ML testing focus, pipeline integration
Feature Comparison Matrix
| Feature | LangSmith | Braintrust | Langfuse | Arize |
|---|---|---|---|---|
| Tracing | Full | Full | Full | Full |
| Prompt Management | Full | Full | Full | Partial |
| LLM-as-Judge | Built-in | Built-in | Built-in | Built-in |
| Self-hosting | No | No | Yes | Yes |
| Open Source | No | No | Yes | Phoenix |
| CI/CD Integration | Via SDK | Native | Via SDK | Native |
| Cost Tracking | Yes | Yes | Yes | Yes |
Migration & Integration Tips
Best Practices
- • Start with free tiers to evaluate fit
- • Use SDK abstractions for portability
- • Export evaluation data regularly
- • Document custom evaluators separately
- • Test migration path before committing
Common Pitfalls
- • Vendor lock-in from deep integrations
- • Data portability limitations
- • Pricing surprises at scale
- • Feature gaps discovered late
- • Underestimating migration effort