Passthrough

Forward provider-native requests through FinOps with full core pipeline processing, including logs and observability.

Overview

Passthrough integrations let you call provider-native API paths and payloads through FinOps without route-level request/response conversion.

When you use passthrough endpoints, the request still flows through FinOps core logic. You keep FinOps features such as logging and observability while sending provider-native paths and bodies.

Endpoints

/openai_passthrough Default provider: openai
/anthropic_passthrough Default provider: anthropic
/azure_passthrough Default provider: azure
/genai_passthrough Default provider: gemini (with automatic Vertex detection for clients configured to use Vertex)

How It Works

Send your request to a passthrough endpoint (OpenAI, Anthropic, Azure, or GenAI passthrough).
The integration strips the passthrough prefix and forwards the remaining provider-native path/body.
FinOps handles provider execution through core inference and plugin pipelines.
Response status, headers, and body are returned as passthrough output (for both stream and non-stream requests).

Provider Selection Rules

OpenAI Passthrough

Uses openai as the default provider.

Anthropic Passthrough

Uses anthropic as the default provider.

Azure Passthrough

Uses azure as the default provider.
Requires an Azure key with endpoint configured. Query parameters (including any api-version) are forwarded as-is from the caller — FinOps does not inject any version.

GenAI Passthrough

Uses gemini by default.
Automatically switches to vertex when Vertex patterns are detected, such as:
URL path containing /projects/{PROJECT_ID}/locations/{LOCATION}/
Request body model containing a Vertex resource path
OAuth token pattern typically used for Vertex (Bearer ya29...)

Usage Examples

OpenAI Passthrough

Python SDK

import openai

client = openai.OpenAI(
 base_url="{AI_GATEWAY_URL}/openai_passthrough/v1",
 api_key="dummy-key"
)

response = client.chat.completions.create(
 model="gpt-4o-mini",
 messages=[{"role": "user", "content": "hello from passthrough"}]
)

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

cURL

curl -X POST "{AI_GATEWAY_URL}/openai_passthrough/v1/chat/completions" \
 -H "content-type: application/json" \
 -H "authorization: Bearer sk-your-openai-key" \
 -d '{
 "model": "gpt-4o-mini",
 "messages": [{"role":"user","content":"hello from passthrough"}]
 }'

Anthropic Passthrough

Python SDK

import anthropic

client = anthropic.Anthropic(
 base_url="{AI_GATEWAY_URL}/anthropic_passthrough",
 api_key="dummy-key"
)

response = client.messages.create(
 model="claude-sonnet-4-20250514",
 max_tokens=1024,
 messages=[{"role": "user", "content": "hello from passthrough"}]
)

print(response.content[0].text)

cURL

curl -X POST "{AI_GATEWAY_URL}/anthropic_passthrough/v1/messages" \
 -H "content-type: application/json" \
 -H "x-api-key: your-anthropic-key" \
 -H "anthropic-version: 2023-06-01" \
 -d '{
 "model": "claude-sonnet-4-20250514",
 "max_tokens": 1024,
 "messages": [{"role":"user","content":"hello from passthrough"}]
 }'

Azure Passthrough

Azure OpenAI SDK

from openai import AzureOpenAI

client = AzureOpenAI(
 azure_endpoint="{AI_GATEWAY_URL}/azure_passthrough",
 api_key="dummy-key",
 api_version="2024-10-21", # passed through as-is in the query string
)

response = client.chat.completions.create(
 model="gpt-4o", # your Azure deployment name
 messages=[{"role": "user", "content": "hello from azure passthrough"}]
)

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

OpenAI SDK

from openai import OpenAI

client = OpenAI(
 base_url="{AI_GATEWAY_URL}/azure_passthrough/openai/v1/",
 api_key="dummy-key",
)

response = client.responses.create(
 model="gpt-4.1", # your Azure deployment name
 input="hello from azure passthrough",
)

print(response.output_text)

Anthropic SDK (Anthropic on Azure)

import anthropic

client = anthropic.Anthropic(
 base_url="{AI_GATEWAY_URL}/azure_passthrough",
 api_key="dummy-key",
)

response = client.messages.create(
 model="claude-sonnet-4-20250514",
 max_tokens=1024,
 messages=[{"role": "user", "content": "hello from azure passthrough"}]
)

print(response.content[0].text)

cURL

curl -X POST "{AI_GATEWAY_URL}/azure_passthrough/openai/deployments/gpt-4o/chat/completions" \
 -H "content-type: application/json" \
 -d '{
 "messages": [{"role": "user", "content": "hello from azure passthrough"}]
 }'

GenAI Passthrough (Gemini)

Python SDK

from google import genai
from google.genai.types import HttpOptions

client = genai.Client(
 api_key="dummy-key",
 http_options=HttpOptions(base_url="{AI_GATEWAY_URL}/genai_passthrough")
)

response = client.models.generate_content(
 model="gemini-2.5-flash",
 contents="hello from passthrough"
)

print(response.text)

cURL

curl -X POST "{AI_GATEWAY_URL}/genai_passthrough/v1beta/models/gemini-2.5-flash:generateContent" \
 -H "content-type: application/json" \
 -H "x-goog-api-key: your-gemini-key" \
 -d '{
 "contents":[{"parts":[{"text":"hello from passthrough"}]}]
 }'

GenAI Passthrough (Vertex-style request)

Python SDK

from google import genai
from google.genai.types import HttpOptions

client = genai.Client(
 vertexai=True,
 api_key="dummy-key",
 http_options=HttpOptions(base_url="{AI_GATEWAY_URL}/genai_passthrough")
)

response = client.models.generate_content(
 model="gemini-2.5-flash",
 contents="hello from vertex passthrough"
)

print(response.text)

cURL

curl -X POST "{AI_GATEWAY_URL}/genai_passthrough/v1/projects/my-project/locations/us-central1/publishers/google/models/gemini-2.5-flash:generateContent" \
 -H "content-type: application/json" \
 -H "authorization: Bearer ya29.your-vertex-token" \
 -d '{
 "contents":[{"parts":[{"text":"hello from vertex passthrough"}]}]
 }'

Notes

Use passthrough when you need a provider endpoint that is not directly supported by FinOps integration routes yet.
For Azure passthrough, auth headers (api-key, x-api-key, OAuth token) are always sourced from the FinOps key config and never forwarded from the client request.

Pydantic AI SDK Overview

Passthrough

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