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Phoenix provides seamless integration with Portkey, the AI Gateway and observability platform that routes to 200+ LLMs with enterprise-grade features including guardrails, caching, and load balancing.

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Launch Phoenix

  • Phoenix Cloud
  • Command Line
  • Docker
  • Notebook
Sign up for Phoenix:
  1. Sign up for an Arize Phoenix account at https://app.phoenix.arize.com/login
  2. Click Create Space, then follow the prompts to create and launch your space.
Install packages:
pip install arize-phoenix-otel
Set your Phoenix endpoint and API Key:From your new Phoenix Space
  1. Create your API key from the Settings page
  2. Copy your Hostname from the Settings page
  3. In your code, set your endpoint and API key: 
import os

os.environ["PHOENIX_API_KEY"] = "ADD YOUR PHOENIX API KEY"
os.environ["PHOENIX_COLLECTOR_ENDPOINT"] = "ADD YOUR PHOENIX HOSTNAME"

# If you created your Phoenix Cloud instance before June 24th, 2025,
# you also need to set the API key as a header:
# os.environ["PHOENIX_CLIENT_HEADERS"] = f"api_key={os.getenv('PHOENIX_API_KEY')}"
Having trouble finding your endpoint? Check out Finding your Phoenix Endpoint

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Install

pip install openinference-instrumentation-portkey portkey-ai

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Setup

Use the register function to connect your application to Phoenix: 
from phoenix.otel import register

# configure the Phoenix tracer
tracer_provider = register(
  project_name="my-portkey-app", # Default is 'default'
  auto_instrument=True # Auto-instrument your app based on installed OI dependencies
)

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Run Portkey

By instrumenting Portkey, spans will be created whenever requests are made through the AI Gateway and will be sent to the Phoenix server for collection.

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Basic Usage with OpenAI

import os
from openai import OpenAI
from portkey_ai import PORTKEY_GATEWAY_URL, createHeaders

# Set up your API keys
os.environ["OPENAI_API_KEY"] = "your-openai-api-key"
os.environ["PORTKEY_API_KEY"] = "your-portkey-api-key"  # Optional for self-hosted

client = OpenAI(
    api_key=os.environ.get("OPENAI_API_KEY"),
    base_url=PORTKEY_GATEWAY_URL,
    default_headers=createHeaders(
        provider="openai",
        api_key=os.environ.get("PORTKEY_API_KEY")
    )
)

response = client.chat.completions.create(
    model="gpt-4o-mini",
    messages=[{"role": "user", "content": "What is artificial intelligence?"}]
)

print(response.choices[0].message.content)

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Using Portkey SDK Directly

from portkey_ai import Portkey

# Initialize Portkey client
portkey = Portkey(
    api_key="your-portkey-api-key",  # Optional for self-hosted
    virtual_key="your-openai-virtual-key"  # Or use provider-specific virtual keys
)

response = portkey.chat.completions.create(
    model="gpt-4o-mini",
    messages=[{"role": "user", "content": "Explain machine learning"}]
)

print(response.choices[0].message.content)

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Observe

Now that you have tracing setup, all requests through Portkey’s AI Gateway will be streamed to your running Phoenix instance for observability and evaluation. You’ll be able to see:
  • Request/Response Traces: Complete visibility into LLM interactions
  • Routing Decisions: Which provider was selected and why
  • Fallback Events: When and why fallbacks were triggered
  • Cache Performance: Hit/miss rates and response times
  • Cost Tracking: Token usage and costs across providers
  • Latency Metrics: Response times for each provider and route

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