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VertexAI

Vertex AI is a fully managed platform by Google Cloud for building, deploying, and scaling machine learning models.

Website: https://cloud.google.com/vertex-ai

VertexAI Tracing

Instrument LLM calls made using VertexAI's SDK via the VertexAIInstrumentor

The VertexAI SDK can be instrumented using the openinference-instrumentation-vertexai package.

Launch Phoenix

Install

pip install openinference-instrumentation-vertexai vertexai

Setup

See Google's guide on setting up your environment for the Google Cloud AI Platform. You can also store your Project ID in the CLOUD_ML_PROJECT_ID environment variable.

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-llm-app", # Default is 'default'
  auto_instrument=True # Auto-instrument your app based on installed OI dependencies
)

Run VertexAI

import vertexai
from vertexai.generative_models import GenerativeModel

vertexai.init(location="us-central1")
model = GenerativeModel("gemini-1.5-flash")

print(model.generate_content("Why is sky blue?").text)

Observe

Now that you have tracing setup, all invocations of Vertex models will be streamed to your running Phoenix for observability and evaluation.

Resources

VertexAI Evals

Configure and run VertexAI for evals

VertexAI

Need to install the extra dependencygoogle-cloud-aiplatform>=1.33.0

class VertexAIModel:
    project: Optional[str] = None
    location: Optional[str] = None
    credentials: Optional["Credentials"] = None
    model: str = "text-bison"
    tuned_model: Optional[str] = None
    temperature: float = 0.0
    max_tokens: int = 256
    top_p: float = 0.95
    top_k: int = 40

To authenticate with VertexAI, you must pass either your credentials or a project, location pair. In the following example, we quickly instantiate the VertexAI model as follows:

project = "my-project-id"
location = "us-central1" # as an example
model = VertexAIModel(project=project, location=location)
model("Hello there, this is a tesst if you are working?")
# Output: "Hello world, I am working!"

VertexAI Evals

Configure and run VertexAI for evals

VertexAI

Need to install the extra dependencygoogle-cloud-aiplatform>=1.33.0

class VertexAIModel:
    project: Optional[str] = None
    location: Optional[str] = None
    credentials: Optional["Credentials"] = None
    model: str = "text-bison"
    tuned_model: Optional[str] = None
    temperature: float = 0.0
    max_tokens: int = 256
    top_p: float = 0.95
    top_k: int = 40

To authenticate with VertexAI, you must pass either your credentials or a project, location pair. In the following example, we quickly instantiate the VertexAI model as follows:

project = "my-project-id"
location = "us-central1" # as an example
model = VertexAIModel(project=project, location=location)
model("Hello there, this is a tesst if you are working?")
# Output: "Hello world, I am working!"

VertexAI Tracing

Instrument LLM calls made using VertexAI's SDK via the VertexAIInstrumentor

The VertexAI SDK can be instrumented using the openinference-instrumentation-vertexai package.

Launch Phoenix

Install

pip install openinference-instrumentation-vertexai vertexai

Setup

See Google's guide on setting up your environment for the Google Cloud AI Platform. You can also store your Project ID in the CLOUD_ML_PROJECT_ID environment variable.

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-llm-app", # Default is 'default'
  auto_instrument=True # Auto-instrument your app based on installed OI dependencies
)

Run VertexAI

import vertexai
from vertexai.generative_models import GenerativeModel

vertexai.init(location="us-central1")
model = GenerativeModel("gemini-1.5-flash")

print(model.generate_content("Why is sky blue?").text)

Observe

Now that you have tracing setup, all invocations of Vertex models will be streamed to your running Phoenix for observability and evaluation.

Resources

Sign up for Phoenix:

Sign up for an Arize Phoenix account at https://app.phoenix.arize.com/login
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

Create your API key from the Settings page
Copy your Hostname from the Settings page
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

Launch your local Phoenix instance:

pip install arize-phoenix
phoenix serve

For details on customizing a local terminal deployment, see Terminal Setup.

Install packages:

pip install arize-phoenix-otel

Set your Phoenix endpoint:

import os

os.environ["PHOENIX_COLLECTOR_ENDPOINT"] = "http://localhost:6006"

See Terminal for more details.

Pull latest Phoenix image from Docker Hub:

docker pull arizephoenix/phoenix:latest

Run your containerized instance:

docker run -p 6006:6006 arizephoenix/phoenix:latest

This will expose the Phoenix on localhost:6006

Install packages:

pip install arize-phoenix-otel

Set your Phoenix endpoint:

import os

os.environ["PHOENIX_COLLECTOR_ENDPOINT"] = "http://localhost:6006"

For more info on using Phoenix with Docker, see Docker.

Install packages:

pip install arize-phoenix

Launch Phoenix:

import phoenix as px
px.launch_app()

By default, notebook instances do not have persistent storage, so your traces will disappear after the notebook is closed. See self-hosting or use one of the other deployment options to retain traces.