Arize AI
AI observability platform for LLM and ML monitoring with automatic drift detection, root cause analysis, and production debugging
Overview
Arize AI is a comprehensive ML and LLM observability platform that helps teams monitor, troubleshoot, and optimize AI applications in production. Founded in 2020 by former Uber and TubeMogul engineers, Arize provides automatic performance monitoring, drift detection, and root cause analysis for both traditional ML models and LLM applications. The platform offers production tracing, prompt engineering tools, retrieval analysis for RAG systems, and LLM-as-judge evaluations. Arize also maintains Phoenix, the open-source LLM observability library that has become a community standard for local development and testing before scaling to production.
The Verdict
Who Should Use Arize AI?
Best For
- Enterprise ML teams with production models
- LLM applications needing trace debugging
- RAG systems requiring retrieval analysis
- Teams needing automatic drift detection
- Organizations with compliance requirements
Not Ideal For
- Hobbyists or early prototypes (use Phoenix OSS)
- Pure LangChain shops (LangSmith more native)
- Budget-conscious startups (Langfuse cheaper)
- Simple single-model deployments
What's Great
- Unified platform for ML + LLM observability
- Automatic drift detection and alerting
- Powerful root cause analysis workflows
- OpenTelemetry-native tracing (OpenInference)
- Built-in LLM evaluation framework
- RAG-specific retrieval quality metrics
- Phoenix OSS for local development
- Strong enterprise security (SOC 2, HIPAA)
Watch Out For
- Higher pricing than OSS alternatives
- Learning curve for full platform features
- Phoenix and Arize cloud feature parity gaps
- ML-focused heritage may feel heavy for LLM-only teams
- Self-hosting requires enterprise plan
Pricing
View all features & details
LLM Observability
- Distributed tracing (OpenTelemetry)
- Prompt & response logging
- Token usage & cost tracking
- Latency monitoring
- Error rate analysis
- Session replay & debugging
- Span-level annotations
- Multi-model support
Evaluations
- LLM-as-judge evaluations
- Retrieval quality (MRR, NDCG)
- Hallucination detection
- Toxicity & safety checks
- Custom eval templates
- Human annotation workflows
- A/B experiment tracking
- Regression alerts
ML Monitoring
- Data drift detection
- Prediction drift alerts
- Feature importance
- Model performance tracking
- Root cause analysis
- Cohort analysis
- Fairness metrics
- Explainability (SHAP)
Integrations
- OpenAI / Azure OpenAI
- Anthropic Claude
- LangChain / LangGraph
- LlamaIndex
- AWS Bedrock
- Vertex AI
- DSPy
- AutoGen / CrewAI
Phoenix OSS
- Local trace visualization
- Notebook integration
- OpenInference instrumentation
- Evaluation harnesses
- Export to Arize cloud
- Docker/pip install
- Active community
Security & Compliance
- SOC 2 Type II
- HIPAA compliant (Enterprise)
- GDPR ready
- SSO / SAML
- RBAC permissions
- Data retention controls
- VPC deployment option
How It Compares
| Feature | Arize AI | Langfuse | W&B Weave | LangSmith |
|---|---|---|---|---|
| Open Source | Phoenix (MIT) | Yes (MIT) | No | No |
| Self-Hosted | Enterprise only | Yes (free) | No | No |
| ML Monitoring | Full suite | LLM only | ML + LLM | LLM only |
| Drift Detection | Automatic | Manual | Basic | No |
| LLM Tracing | OpenInference | Custom | Custom | Native |
| RAG Analysis | Deep retrieval | Basic | Basic | Good |
| Evaluations | LLM-as-judge | LLM-as-judge | Advanced | Online evals |
| Free Tier | 10K spans/mo | 50K obs/mo | Limited | 5K traces/mo |
| Starting Price | $150/mo | $59/mo | Contact | $39/mo |
| Best For | Enterprise ML+LLM | Full control | ML teams | LangChain users |