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This page covers Arize Python SDK version 8 for on-premise and self-hosted Arize AX. For SDK v7, see On-Premise SDK Usage — Version 7. For an overview of both versions, see On-Premise SDK Usage.

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Single Endpoint

With single endpoints, all services are available through a common host address and port:
HostFunction
arize-app.<domain>
  • Arize AX UI
  • SDK pandas uploads and logs
  • OTEL traces with GRPC
  • OTEL traces with HTTP
  • Flightserver import/export, datasets.

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Example with Arize Python SDK v8

Use ArizeClient with single_host so that all SDK endpoints (API, OTLP, and Flight) use the same host. You can omit single_port (default 443). Pass space_id and project_name on each operation (for example, when logging spans or listing datasets). 
from arize import ArizeClient

client = ArizeClient(
    api_key=API_KEY,
    single_host="arize-app.<domain>",
    single_port=443,  # optional; default 443
)

# Pass space_id and project_name per operation
client.spans.log(space_id=SPACE_ID, project_name=PROJECT_NAME, dataframe=spans_df)
client.datasets.list(space_id=SPACE_ID)
For single-host deployments you can also set SINGLE_HOST in the environment and construct the client with only single_host.

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Example with OTEL traces and GRPC

# gRPC
from arize.otel import register, Transport
tracer_provider = register(
    space_id=SPACE_ID,
    api_key=API_KEY,
    endpoint="https://arize-app.<domain>/v1",
    # transport=Transport.GRPC,  # default
    ...
)

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Example with OTEL traces and HTTP

from arize.otel import register, Transport
tracer_provider = register(
    space_id=SPACE_ID,
    api_key=API_KEY,
    endpoint="https://arize-app.<domain>/v1/traces",
    transport=Transport.HTTP,
    ...
)

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Example: ML inference logging (same client)

The same ArizeClient is used for ML inference logging. Call client.ml.log() with space_id, model_name, model_type, dataframe, schema, and environment. 
from arize import ArizeClient
from arize.ml.types import Schema, ModelTypes, Environments

client = ArizeClient(
    api_key=API_KEY,
    single_host="arize-app.<domain>",
    single_port=443,
)

schema = Schema(
    prediction_id_column_name="prediction_id",
    prediction_label_column_name="predicted_label",
    actual_label_column_name="actual_label",
    feature_column_names=["feature_1", "feature_2"],
)

client.ml.log(
    space_id=SPACE_ID,
    model_name="my-model",
    model_type=ModelTypes.BINARY_CLASSIFICATION,
    dataframe=predictions_df,
    schema=schema,
    environment=Environments.PRODUCTION,
)

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Example: Exporting spans (same client)

In SDK v8, export and dataset operations use the same ArizeClient instance. With a single endpoint, no extra configuration is needed. 
from arize import ArizeClient
from datetime import datetime

client = ArizeClient(
    api_key=API_KEY,
    single_host="arize-app.<domain>",
    single_port=443,
)

df = client.spans.export_to_df(
    space_id=SPACE_ID,
    project_name=PROJECT_NAME,
    start_time=datetime(2024, 1, 1),
    end_time=datetime(2026, 1, 1),
)

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Example: Datasets and experiments (same client)

The same client is used for datasets and experiments. Create a dataset with name, space_id, and examples (a DataFrame); retrieve it with datasets.get(dataset_id=...). 
from arize import ArizeClient
import pandas as pd

client = ArizeClient(
    api_key=API_KEY,
    single_host="arize-app.<domain>",
    single_port=443,
)

# Datasets: create and get
created = client.datasets.create(
    name="my-dataset",
    space_id=SPACE_ID,
    examples=examples_df,
)
dataset = client.datasets.get(dataset_id=created.id)

# List and experiments
client.datasets.list(space_id=SPACE_ID)
client.experiments.run(name="my-experiment", dataset_id=DATASET_ID, task=my_task)

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Four Endpoints

If your deployment is configured with four endpoints, it requires different ingress configurations and host addresses:
HostFunction
arize-app.<domain>
  • Arize AX UI
  • Auth
  • Copilot
arize-api.<domain>
  • SDK pandas uploads and logs
arize-otlp.<domain>
  • OTEL traces (GRPC/HTTP)
arize-flight.<domain>
  • Flightserver import/export, datasets.

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Example with Arize Python SDK v8

Use ArizeClient with separate hosts for API, OTLP, and Flight when your deployment uses four endpoints. Pass space_id on each operation. You can also use environment variables ARIZE_API_HOST, ARIZE_OTLP_HOST, ARIZE_FLIGHT_HOST, ARIZE_FLIGHT_PORT, and ARIZE_FLIGHT_SCHEME and construct the client with ArizeClient(api_key=API_KEY). 
from arize import ArizeClient

client = ArizeClient(
    api_key=API_KEY,
    api_host="arize-api.<domain>",
    api_scheme="https",
    otlp_host="arize-otlp.<domain>",
    otlp_scheme="https",
    flight_host="arize-flight.<domain>",
    flight_port=443,
    flight_scheme="grpc+tls",
)

client.spans.log(space_id=SPACE_ID, project_name=PROJECT_NAME, dataframe=spans_df)
client.spans.export_to_df(space_id=SPACE_ID, project_name=PROJECT_NAME, start_time=..., end_time=...)
client.datasets.list(space_id=SPACE_ID)
client.experiments.run(name="my-experiment", dataset_id=DATASET_ID, task=my_task)

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Example with OTEL traces and GRPC

from arize.otel import register, Transport
tracer_provider = register(
    space_id=SPACE_ID,
    api_key=API_KEY,
    endpoint="https://arize-otlp.<domain>/v1",
    # transport=Transport.GRPC,  # default
    ...
)

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Example with OTEL traces and HTTP

from arize.otel import register, Transport
tracer_provider = register(
    space_id=SPACE_ID,
    api_key=API_KEY,
    endpoint="https://arize-otlp.<domain>/v1/traces",
    transport=Transport.HTTP,
    ...
)

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Using an Enterprise-Issued Certificate

If your deployment uses a certificate signed by your own enterprise private CA or a self-signed certificate, follow the relevant instructions below for your deployment type and use case. You can set ARIZE_SSL_CA_CERT to a certificate path (or colon/semicolon-separated list of paths) and, in your code, set REQUESTS_CA_BUNDLE and SSL_CERT_FILE to that bundle before importing arize. To disable verification in development only: ARIZE_REQUEST_VERIFY=false.

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Obtaining the Root CA Certificate

First, obtain the root CA certificate that was used to sign your endpoint’s certificate. This root certificate is typically managed by the security team and is common across environments.

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Extracting the Certificate (if root CA is not available)

If providing the root certificate doesn’t resolve the issue or is not an option, extract the certificate directly from the endpoint: 
echo | openssl s_client -showcerts -state -connect <host>:443 -prexit > cert.pem

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Environment-based CA bundle and certificate options

Environment-based CA bundle — Set ARIZE_SSL_CA_CERT to the path to your .crt or .pem file. For multiple certs, use a colon-separated list (Unix) or semicolon-separated (Windows), e.g. ARIZE_SSL_CA_CERT="/path/cert1.crt:/path/cert2.crt". In your code, before importing arize, set REQUESTS_CA_BUNDLE and SSL_CERT_FILE to that path (or to a single merged bundle file). You can merge multiple certs into one bundle and set these env vars so the SDK and OTLP exporter use your CA(s).
  • Temporarily disable certificate validation (not recommended for production): Set ARIZE_REQUEST_VERIFY=false in the environment, or: 
    from arize import ArizeClient
client = ArizeClient(api_key=API_KEY, single_host="arize-app.<domain>", single_port=443, request_verify=False)
  • Provide the root certificate file via client: 
    from arize import ArizeClient
client = ArizeClient(api_key=API_KEY, single_host="arize-app.<domain>", single_port=443, request_verify="cert.pem")
    Or use your system CA bundle: 
    import certifi
from arize import ArizeClient
client = ArizeClient(api_key=API_KEY, single_host="arize-app.<domain>", single_port=443, request_verify=certifi.where())
    For four-endpoint deployments, pass the same request_verify value with api_host, otlp_host, and flight_host instead of single_host.

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OTEL Traces (GRPC/HTTP)

  • Set the OTEL_EXPORTER_OTLP_CERTIFICATE environment variable to your root certificate: 
    os.environ["OTEL_EXPORTER_OTLP_CERTIFICATE"] = "cert.pem"
    Or 
    # if root cert has already been added to your system CA bundle
import certifi
os.environ['OTEL_EXPORTER_OTLP_CERTIFICATE'] = certifi.where()

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Flight (gRPC) connections

When using ArizeClient for export, datasets, or experiments, bulk data may be sent over Arrow Flight (gRPC). If you use a custom or enterprise certificate, set GRPC_DEFAULT_SSL_ROOTS_FILE_PATH so the gRPC layer can verify the server:
  • Set the GRPC_DEFAULT_SSL_ROOTS_FILE_PATH environment variable to your root certificate: 
    import os
os.environ["GRPC_DEFAULT_SSL_ROOTS_FILE_PATH"] = "cert.pem"
    Or use your system CA bundle: 
    import os
import certifi
os.environ["GRPC_DEFAULT_SSL_ROOTS_FILE_PATH"] = certifi.where()