Open-source LLM observability and evaluation platform built on OpenTelemetry. Self-host it free with no feature gates, or use Arize's managed cloud.
An open-source tool that helps you see inside your AI's thinking — debug and improve AI performance with visual tracing.
Arize Phoenix is the leading open-source option for teams that want to see exactly what their LLM applications are doing in production without paying per-trace fees or getting locked into a vendor. Built on OpenTelemetry, it works with any framework and any model provider.
Most LLM observability tools charge per trace or per seat. LangSmith, the most common alternative, has a free tier but pushes you toward paid plans as trace volume grows. Phoenix is fully open source with no feature gates. You self-host it, you own the data, and you pay nothing for the software itself.
The OpenTelemetry foundation matters. If you already instrument your services with OpenTelemetry (and most production teams do), Phoenix slots into your existing observability stack. You don't need a separate SDK or proprietary agent. Traces from your LLM calls flow through the same pipeline as your application metrics.
Phoenix captures traces from LLM applications: every prompt, completion, tool call, and retrieval step. You see latency breakdowns, token usage, error rates, and the actual content flowing through your system. When a user reports a bad response, you can trace back through the exact chain of events that produced it.
The evaluation framework lets you score outputs against test cases. Define what "good" looks like for your use case, run evaluations against production data, and track quality over time. This replaces the manual spot-checking that most teams rely on.
Experiments compare changes side by side. Swap a prompt, change a model, adjust retrieval parameters, and see how outputs change across the same set of inputs. This is where Phoenix saves the most time: instead of guessing whether a change improved quality, you get evidence.
Source: phoenix.arize.com
Self-hosting Phoenix on a $24/month cloud VM handles most teams' trace volumes. LangSmith charges based on traces: the Plus plan runs $39/seat/month. A 5-person team on LangSmith Plus pays $195/month. The same team running Phoenix on a $24 VM pays $24/month and keeps full data ownership. At 20 engineers, LangSmith costs $780/month; Phoenix still costs $24/month (or less if you're already running Kubernetes).
The tradeoff: Phoenix requires someone to maintain the deployment. If your team doesn't have DevOps capacity, the managed Arize Cloud option or LangSmith's hosted service saves that operational burden.
Phoenix runs anywhere: local Docker container for development, Kubernetes Helm chart for production clusters, or a simple pip install for quick experimentation. The Helm chart (added mid-2025) makes Kubernetes deployment straightforward with configurable resource limits and persistent storage.
For teams already running Kubernetes, Phoenix deploys as a standard service alongside your existing observability stack (Grafana, Prometheus, Jaeger). The OpenTelemetry compatibility means traces flow naturally through your existing collectors.
The documentation lags behind the feature set. Power users on GitHub and Reddit note that some newer features lack clear guides. You'll spend time reading source code and community discussions to understand advanced configuration.
The UI is functional but not polished compared to commercial tools. LangSmith's interface is more refined, with better collaboration features for teams reviewing traces together.
No built-in alerting. Phoenix shows you what happened but won't page you when something goes wrong. You'll need to connect it to your existing alerting system (PagerDuty, Slack webhooks) through custom integration.
Community support replaces dedicated customer success. For enterprise teams that need guaranteed response times, the managed Arize Cloud service or a commercial alternative may be worth the premium.
Developers on GitHub (12,000+ stars) praise Phoenix for its zero-cost entry and OpenTelemetry compatibility. A Kubernetes subreddit thread from June 2025 highlighted the Helm chart deployment as a welcome addition for teams wanting in-cluster observability without external SaaS dependencies.
Christopher Brown, CEO of Decision Patterns and former UC Berkeley CS lecturer, noted that "Phoenix integrated into our team's existing data science workflows and enabled the exploration of unstructured text data to identify root causes of unexpected user inputs."
The main complaint in community discussions: the learning curve is steeper than commercial alternatives that offer guided onboarding. Teams without existing observability experience may struggle with initial setup.
For tracing and evaluation, yes. Phoenix covers the core functionality. You'll miss LangSmith's polished UI, collaborative annotation features, and hosted convenience. If cost and data ownership matter more than UX polish, Phoenix is the better choice.
A single VM with 4GB RAM handles development and small production workloads. For high-volume production (millions of traces per day), deploy on Kubernetes with the Helm chart and allocate based on your trace volume. Storage is the main scaling concern.
Yes. The OpenTelemetry-based approach means Phoenix traces calls to OpenAI, Anthropic, Google, local models, or any provider. Framework integrations exist for LangChain, LlamaIndex, and most popular AI frameworks.
No feature gates on the open-source version. Arize Cloud adds managed hosting, enterprise SSO, team management, and dedicated support. The observability and evaluation features are identical.
Phoenix is the right choice for teams with DevOps capacity who want full LLM observability without per-trace fees or vendor lock-in. The OpenTelemetry foundation, zero-cost self-hosting, and no feature restrictions make it the most cost-effective option in the category. If you need managed hosting and polished UX, LangSmith is the commercial alternative. But for teams that value data ownership and cost control, Phoenix is hard to beat.
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The best open-source LLM observability tool for teams that want full tracing, evaluation, and experimentation without per-trace fees. Built on OpenTelemetry for vendor-neutral integration. Requires self-hosting and DevOps capacity.
Visualizes embedding spaces using UMAP dimensionality reduction, showing clusters of queries, retrieval results, and model outputs. Detects distribution drift between evaluation and production data, highlighting when new inputs diverge from training distribution.
Use Case:
Discovering that customer queries about a newly launched product create an embedding cluster far from your existing knowledge base, explaining poor retrieval quality.
Captures hierarchical traces using the OpenInference specification — an open standard for LLM observability. Auto-instrumentation for LangChain, LlamaIndex, OpenAI, and other frameworks captures LLM calls, retriever spans, tool executions, and custom spans.
Use Case:
Auto-instrumenting a LlamaIndex RAG pipeline to capture every retrieval, reranking, and generation step without modifying application code.
Includes pre-built evaluation functions for hallucination detection (using citation verification), QA correctness, chunk relevance, toxicity, and summarization quality. Each evaluator is based on published research methodologies and can run locally.
Use Case:
Running hallucination detection on every production trace to calculate a daily hallucination rate and track it over time as you iterate on your system.
Create versioned datasets from production traces or manual uploads. Run experiments that compare different configurations (models, prompts, retrieval strategies) against the same dataset with statistical significance testing.
Use Case:
Comparing three different chunking strategies for your RAG pipeline by running each against a golden dataset of 200 queries and measuring retrieval precision.
Specialized metrics for RAG systems including NDCG, precision@k, recall@k, and MRR computed from trace data. Visualizes retrieval performance over time and identifies queries where retrieval consistently fails.
Use Case:
Identifying that retrieval precision drops below 50% for queries containing technical acronyms, indicating a need for query expansion.
Phoenix launches as a local server directly from Jupyter or Colab notebooks with px.launch_app(). All data stays local. The UI opens in-browser alongside your notebook for an integrated analysis workflow.
Use Case:
Running a quick investigation in a Jupyter notebook to understand why a specific category of user queries produces low-quality responses.
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View Pricing Options →ML teams building RAG systems who need deep analytical visibility into retrieval quality, embedding distributions, and document relevance
Data scientists who want notebook-integrated LLM observability for iterative debugging and experimentation
Teams evaluating LLM application quality using research-grade evaluation methodologies with local data processing
Organizations needing to detect distribution drift between their evaluation datasets and actual production query patterns
Arize Phoenix works with these platforms and services:
We believe in transparent reviews. Here's what Arize Phoenix doesn't handle well:
No. Phoenix is Arize's open-source LLM observability tool that runs locally. The Arize platform is a separate commercial product for production ML monitoring. They share some concepts but Phoenix is standalone, free, and doesn't require an Arize account.
Phoenix can handle production workloads, but its local-first design means you need to set up persistent storage and infrastructure for team access. Arize offers a hosted version for production scale. Many teams use Phoenix locally for development/debugging and the Arize platform for production monitoring.
Phoenix is stronger in analytical depth — embedding visualization, drift detection, and ML-informed evaluation. Langfuse is stronger in operational workflows — prompt management, team collaboration, and production deployment. Phoenix is the better debugging and analysis tool; Langfuse is the better team platform.
Yes, the tracing and evaluation features work for any LLM application. However, Phoenix's most differentiated features — embedding visualization, retrieval metrics, drift detection — are specifically designed for RAG and retrieval-heavy applications. For pure chatbot monitoring, other tools may offer more relevant features.
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Kubernetes Helm chart deployment support added in mid-2025 for in-cluster AI observability. Active development continues with regular releases on GitHub.
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