Phoenix
Open-source tool for ML observability that runs in your notebook environment, by Arize. Monitor and fine-tune LLM, CV, and tabular models.
What is Phoenix?
Arize Phoenix is an open-source ML observability and LLM tracing platform designed for data scientists and ML engineers who need real-time visibility into their AI applications. It enables users to instrument, experiment, and optimise large language models, computer vision models, and tabular machine learning models directly within notebook environments like Jupyter. Phoenix provides a vendor-agnostic, framework-neutral approach to monitoring AI systems without vendor lock-in, making it accessible for teams of any size. The tool help end-to-end visibility from model inputs through inference outputs, allowing users to identify performance issues, debug model behaviour, and evaluate LLM responses before deployment.
Key Features
LLM Tracing and Instrumentation
Capture detailed traces of LLM calls and interactions to understand model behaviour and identify bottlenecks
Notebook-based Evaluation
Run evaluations directly in Jupyter notebooks for smooth integration into existing ML workflows
Multi-model Support
Monitor and optimise LLMs, computer vision models, and tabular machine learning models from a single platform
Real-time Monitoring
Track model performance and behaviour in real-time with interactive dashboards and visualizations
Framework Agnostic
Works with any ML framework or LLM provider without requiring vendor-specific implementations
Experiment Tracking
Compare model versions and experiments to identify the best performing configurations
Pros & Cons
Advantages
- Completely open-source with no vendor lock-in, giving teams full control and transparency
- Runs directly in notebook environments, reducing setup friction and fitting naturally into existing workflows
- Framework-agnostic design supports diverse ML stacks and LLM providers
- Purpose-built for LLM observability with specialise features beyond general ML monitoring
- Active community and backing by Arize, a recognise leader in ML observability
Limitations
- As an open-source tool, support and documentation may be less thorough than commercial alternatives
- Requires self-hosting or management of infrastructure for production deployments at scale
- May require more technical configuration compared to fully managed SaaS observability platforms
Use Cases
Debugging and tracing LLM application issues during development and testing phases
Evaluating and comparing different LLM models or prompts before production deployment
Monitoring model performance and detecting data drift in real-time for production systems
Optimizing computer vision and tabular model performance through detailed performance analysis
Educational use for learning about ML observability and model behaviour analysis