---
title: "Deploy phoenix (by arize): LLM Evals and Observability - Open Source"
description: "Observe and Evaluate your models and agents. Self-Host it."
category: "AI/ML"
url: https://railway.com/deploy/phoenix-by-arize-llm-evals-and-observabi
---

# Deploy phoenix (by arize): LLM Evals and Observability - Open Source

Observe and Evaluate your models and agents. Self-Host it.

**[Deploy phoenix (by arize): LLM Evals and Observability - Open Source on Railway](https://railway.com/template/phoenix-by-arize-llm-evals-and-observabi)**

Machine-readable deploy manifest (JSON, validated by TemplateCI): https://railway.com/deploy/phoenix-by-arize-llm-evals-and-observabi/manifest.json

- **Creator:** impacte.tech
- **Category:** AI/ML

## Template content

### Phoenix https://raw.githubusercontent.com/Arize-ai/phoenix/refs/heads/main/packages/phoenix-otel/docs/source/_static/logo.png

- **Image:** arizephoenix/phoenix:15.1.0
- **Public domain:** Yes

### Postgres https://devicons.railway.app/i/postgresql.svg

- **Image:** ghcr.io/railwayapp-templates/postgres-ssl:18

## Documentation

# Deploy and Host phoenix (by arize): LLM Evals and Observability - Open Source on Railway

[![Deploy on Railway](https://railway.com/button.svg)](https://railway.com/deploy/phoenix-by-arize)

Phoenix is Arize's open-source LLM observability platform. It captures OpenTelemetry traces from every LLM call, runs LLM-as-judge evaluations and experiments over datasets, and gives you trace waterfalls, a prompt playground, and embeddings views — a self-hosted control plane for debugging and evaluating AI applications.

## About Hosting phoenix (by arize): LLM Evals and Observability - Open Source

Hosting Phoenix on Railway is a single-container deployment: the official `arizephoenix/phoenix` image serves the web UI and OpenTelemetry ingest endpoints (HTTP on port `6006`, gRPC on `4317`). A persistent volume stores your traces, evaluation runs, and datasets in SQLite out of the box — swap in PostgreSQL via environment variables when you outgrow it. Point your applications' OpenInference/OpenTelemetry instrumentors at your Railway URL and traces start flowing immediately. Optional authentication protects the UI and ingest. Scaling is a slider: more RAM/CPU for larger trace volumes, or a lean CPU-only footprint for small teams.

## Common Use Cases

- **Trace every LLM call** in OpenAI, LangChain, LlamaIndex, Vercel AI SDK, and custom agents via OpenTelemetry
- **Run LLM-as-judge evals and experiments** over datasets to catch quality regressions before shipping
- **Debug RAG pipelines span-by-span** — retrieval, reranking, and generation in one waterfall
- **Iterate on prompts** in the playground against saved datasets and versioned experiments
- **Analyze embeddings** — drift detection and failure clustering across model versions

## Dependencies for phoenix (by arize): LLM Evals and Observability - Open Source Hosting

- **Phoenix server** — official `arizephoenix/phoenix` image; web UI + OTLP HTTP on `6006`, OTLP gRPC on `4317`
- **Persistent volume** — SQLite storage for traces, evals, and datasets (`PHOENIX_WORKING_DIR=/data`)
- **Optional: PostgreSQL** — set `PHOENIX_SQL_DATABASE_URL` for production-scale storage instead of SQLite
- **App-side SDKs** — `arize-phoenix-otel` / `openinference-instrumentation-*` packages, or any OTLP-capable tracer

### Implementation Details

Instrument any Python application to stream traces to your Railway-hosted Phoenix:

```python
from phoenix.otel import register

tracer_provider = register(
    endpoint="https://phoenix-yourapp.up.railway.app/v1/traces",  # your Railway domain
    protocol="otlp/http",
)
# OpenInference instrumentors (OpenAI, LangChain, LlamaIndex, ...) plug into this provider
```

Self-hosting outside Railway with Docker:

```bash
docker run -d --name phoenix -p 6006:6006 -p 4317:4317 \
  -v ./phoenix-data:/data \
  -e PHOENIX_WORKING_DIR=/data \
  arizephoenix/phoenix:latest
```

Optional UI/ingest authentication:

```
PHOENIX_ENABLE_AUTH=true
PHOENIX_ADMIN_SECRET=
```

## Why Deploy phoenix (by arize): LLM Evals and Observability - Open Source on Railway?


Railway is a singular platform to deploy your infrastructure stack. Railway will host your infrastructure so you don't have to deal with configuration, while allowing you to vertically and horizontally scale it.

By deploying phoenix (by arize): LLM Evals and Observability - Open Source on Railway, you are one step closer to supporting a complete full-stack application with minimal burden. Host your servers, databases, AI agents, and more on Railway.



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