Confident AI
DeepEval-powered LLM evaluation platform to test, benchmark, and safeguard apps
Phoenix
AI observability platform for tracing, evaluation, and prompt management
Phoenix lets you trace LLM calls, benchmark performance, version datasets, run experiments, and manage prompts—all vendor‑agnostic and deployable locally, in containers, or in the cloud.
Overview
Overview
Phoenix is an AI observability platform that centralizes tracing, evaluation, dataset versioning, experiment tracking, and prompt management for LLM‑driven applications. It targets ML engineers, data scientists, and product teams who need reproducible experimentation and deep insight into model behavior.
Capabilities & Deployment
Built on OpenTelemetry and the OpenInference ecosystem, Phoenix automatically instruments popular frameworks such as LlamaIndex, LangChain, Haystack, and DSPy, while supporting a wide range of LLM providers (OpenAI, Bedrock, MistralAI, VertexAI, LiteLLM, Google GenAI, etc.). The platform can run on a local machine, within a Jupyter notebook, as a Docker container, or at scale on Kubernetes, and a hosted cloud instance is also available. Python and TypeScript SDKs provide lightweight clients and evaluation libraries, enabling seamless integration into existing pipelines.
Why Choose Phoenix
By offering a vendor‑agnostic, extensible observability stack, Phoenix helps teams iterate faster, compare model variants, and debug production issues without locking into a single provider or framework.
Highlights
Unified tracing via OpenTelemetry
LLM‑specific evaluation suite
Versioned dataset and experiment tracking
Prompt management with version control
Pros
- Vendor‑agnostic across frameworks and providers
- Extensible Python and TypeScript SDKs
- Runs anywhere from notebooks to Kubernetes
- Rich UI for playground and experiment comparison
Considerations
- Requires instrumentation of your code
- Full platform may need container/K8s setup for scaling
- Feature set still evolving (e.g., TypeScript evals in alpha)
- Learning curve for OpenTelemetry concepts
Managed products teams compare with
When teams consider Phoenix, these hosted platforms usually appear on the same shortlist.
Looking for a hosted option? These are the services engineering teams benchmark against before choosing open source.
Fit guide
Great for
- ML engineers building LLM applications needing observability
- Data scientists experimenting with prompts and models
- Teams that want reproducible evaluation pipelines
- Organizations preferring self‑hosted AI monitoring
Not ideal when
- Simple scripts with no tracing needs
- Projects locked to a single LLM provider
- Teams without capacity to manage container/K8s deployments
- Use cases requiring real‑time alerting out of the box
How teams use it
Prompt Optimization
Iteratively test prompt variations, compare model responses, and select the best performing version.
RAG Performance Benchmarking
Run retrieval and answer relevance evaluations to quantify improvements across index updates.
Dataset Versioning for Fine‑Tuning
Create immutable snapshots of training examples, track changes, and feed them into fine‑tuning pipelines.
End‑to‑End LLM Debugging
Trace runtime calls, view input/output spans, and replay failures directly in the Playground.
Tech snapshot
Jupyter Notebook56%
Python28%
TypeScript16%
Shell1%
JavaScript1%
PLpgSQL1%
Frequently asked questions
Do I need to run a Phoenix server?
You can use the hosted instance at app.phoenix.arize.com or self‑host via Docker/Kubernetes.
Which languages are supported?
Core SDKs are available for Python and TypeScript; tracing works for any language that can emit OpenTelemetry spans.
How does Phoenix integrate with existing LLM frameworks?
Instrumentation packages are provided for LlamaIndex, LangChain, Haystack, DSPy, and others via OpenInference.
Is there a cost to use Phoenix?
The platform is open source and free; cloud hosting by Arize AI is a paid service.
Can I export evaluation results?
Results can be accessed through the REST API or client libraries and exported to CSV/JSON for downstream analysis.
Project at a glance
Active- Stars
- 8,292
- Watchers
- 8,292
- Forks
- 684
Repo age3 years old
Last commit2 days ago
Primary languageJupyter Notebook
Last synced 2 days ago