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Open Source
Tool Comparison
Ragas
Evaluate your RAG pipeline with LLM-as-judge metrics — faithfulness, relevance, recall.
VS
At a Glance
| Attribute | Ragas | Arize AI |
|---|---|---|
| License / Pricing | Open Source | Free-Limited |
| Type | ai | ai |
| GitHub Stars | — | — |
| Rating | 4.3/5 | 4.4/5 |
| Key Features | 6 listed | 6 listed |
| Integrations | 5 listed | 5 listed |
| Categories | RAG FrameworksAI Observability | AI Observability |
Key Features
Ragas
- Faithfulness — measures if the answer is grounded in retrieved context
- Answer Relevance — checks if the answer addresses the question
- Context Precision and Recall — evaluates retriever quality
- LLM-as-judge evaluation — no labeled ground truth needed
- Integrates with LangChain, LlamaIndex, and any RAG pipeline
- Testset generation — automatically create evaluation datasets
Arize AI
- Real-time feature and prediction drift detection
- LLM tracing and span monitoring via Phoenix
- Automated data quality checks and alerts
- Embedding visualization for NLP and CV models
- A/B model comparison in production
- Integrates with MLflow, SageMaker, Vertex AI
Real-World Use Cases
Ragas
Benchmark RAG pipeline before going to production
Generate a test set from your documents with Ragas TestsetGenerator
CI quality gate for RAG changes
Add a Ragas evaluation step to your GitHub Actions pipeline
Arize AI
Monitor an LLM app for quality degradation
Instrument your app with the Arize SDK or OpenInference
Integrations
Ragas
langchainllamaindexopenailangsmithwandb
Arize AI
mlflowwandblangchainopenaikubeflow
🏆 Which should you choose?
Choose Ragas if…
- → you need a fully open-source, self-hosted solution with no vendor lock-in
Choose Arize AI if…
- → you want a managed or commercial offering with enterprise support and SLAs