Arize Phoenix
Open-source AI observability platform for tracing, evaluation, and prompt management
Founded
N/A
Starting Price
Free
Category
Monitoring & Observability
Last updated March 14, 2026
About Arize Phoenix
Arize Phoenix is an open-source AI observability platform designed for experimentation, evaluation, and troubleshooting of LLM applications. It provides OpenTelemetry-based tracing, LLM evaluation benchmarks, dataset versioning, experiment tracking, and prompt management with version control. Phoenix integrates with major AI frameworks including LangChain, LlamaIndex, DSPy, and direct API calls to OpenAI, Anthropic, and other providers. The platform helps teams identify issues in their AI pipelines, compare model performance, and iterate on prompts — all without vendor lock-in thanks to its OpenTelemetry foundation. Available as open-source self-hosted or via Arize AX managed SaaS.
Pros & Cons
Pros
- Open-source with no vendor lock-in
- OpenTelemetry foundation ensures portability
- Comprehensive tracing and evaluation in one tool
- Integrates with all major LLM frameworks
- Active development and community
Cons
Key Features
OpenTelemetry Tracing
Standards-based distributed tracing for LLM applications, ensuring portability across observability platforms
LLM Evaluation & Benchmarks
Built-in evaluation tools to benchmark model performance, measure accuracy, and compare outputs across different models
Prompt Management
Version-controlled prompt management with history tracking, comparison, and rollback capabilities
Dataset Versioning
Track and version datasets used for evaluation, enabling reproducible experiments and systematic improvement
Experiment Tracking
Track experiments across model versions, prompts, and configurations to identify optimal AI pipeline setups
Framework Integrations
Native integrations with LangChain, LlamaIndex, DSPy, OpenAI, Anthropic, and other major AI frameworks
Pricing
Open Source
Free
- Self-hosted deployment
- Unlimited spans
- Full tracing capabilities
- LLM evaluation
- Prompt management
Best For
LLM Application Debugging
Trace and debug complex LLM pipelines to identify latency bottlenecks, hallucinations, and prompt failures
Model Performance Comparison
Evaluate and compare outputs across different LLM providers and model versions to select the best fit for your use case
Prompt Engineering Workflow
Iterate on prompts with version control, A/B testing, and systematic evaluation to improve AI application quality
AI Pipeline Monitoring
Monitor production AI systems with real-time tracing, alerting on quality degradation, and tracking usage patterns
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