Monitor model router in Microsoft Foundry

Learn how to inspect preview model router metadata for routing attempts, fallback, latency, and Chat Completions session affinity in Microsoft Foundry.

Model router observability helps you verify routing behavior and investigate latency, errors, fallback, and session affinity for individual requests. The preview metadata includes a routing_trace that describes model attempts and a session_affinity object that describes whether model router initialized, retained, or switched a model association.

Both signals appear in the optional model_selection_details response object for the Chat Completions API. These request-level signals complement aggregate metrics and logs. For aggregate monitoring, see Monitor model deployments.

Prerequisites

• Python 3.9 or later.
• The openai>=1.75.0 and python-dotenv packages. Install them by running pip install "openai>=1.75.0" python-dotenv.
• A model router deployment that you can access through an Azure OpenAI endpoint.
• The endpoint and API key for your Azure OpenAI resource. The complete sample reads them from the AZURE_OPENAI_ENDPOINT and AZURE_OPENAI_API_KEY environment variables.
• Azure OpenAI API version 2024-10-21.

Enable request-level metadata

After your application reads the endpoint and API key into endpoint and api_key, create the client with the preview feature header:

"""
Foundry Model Router - Chat Completions Observability Example

This example demonstrates how to use Azure OpenAI's Chat Completions API
with a Foundry Model Router deployment and inspect the selected model,
routing attempts, latency, and status. Model Router automatically
selects the best underlying LLM for each prompt based on your routing mode
(Balanced, Quality, or Cost).

Prerequisites:
  - An Azure OpenAI resource with a "model-router" deployment
    - A .env file beside this script with AZURE_OPENAI_ENDPOINT,
        AZURE_OPENAI_API_KEY, and MODEL_DEPLOYMENT_NAME

Usage:
    pip install -r requirements.txt
    python model-router-chat-completions-observability.py
"""

import os
from pathlib import Path

from dotenv import load_dotenv
from openai import AzureOpenAI

# Load environment variables from .env beside this script
load_dotenv(Path(__file__).resolve().parent / ".env", override=True)

endpoint = os.environ["AZURE_OPENAI_ENDPOINT"]
api_key = os.environ["AZURE_OPENAI_API_KEY"]
deployment = os.environ["MODEL_DEPLOYMENT_NAME"]

# <response_observability_enable>
client = AzureOpenAI(
    azure_endpoint=endpoint,
    api_key=api_key,
    api_version="2024-10-21",
    default_headers={"Foundry-Features": "ModelRouterControls=V1Preview"},
)
# </response_observability_enable>

response = client.chat.completions.create(
    model=deployment,
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {
            "role": "user",
            "content": "In one sentence, name the most popular tourist destination in Seattle.",
        },
    ],
)

print("--- Chat Completions Response ---")
print(f"Response:{response.choices[0].message.content}")
print(
    f"Usage: {response.usage.prompt_tokens} prompt + {response.usage.completion_tokens} completion = {response.usage.total_tokens} total tokens"
)

# <response_observability_extract>
print(f"\nRouted to model: {response.model}")
print("--- Model Selection Details ---")
model_selection_details = getattr(response, "model_selection_details", None)
if not model_selection_details:
    print("No model selection details were returned.")
else:
    model_router_details = model_selection_details.get("model_router_details", {})
    print(f"Routing mode: {model_router_details.get('mode', 'unknown')}")

    routing_trace = model_router_details.get("routing_trace", [])
    if not routing_trace:
        print("No routing trace was returned.")

    for decision_number, routing_decision in enumerate(routing_trace, start=1):
        latency_ms = routing_decision.get("latency_ms")
        latency = f"{latency_ms} ms" if latency_ms is not None else "not reported"
        print(f"Routing decision {decision_number} (latency: {latency})")

        for attempt_number, attempt in enumerate(
            routing_decision.get("attempts", []), start=1
        ):
            result = attempt.get("result", {})
            status = result.get("status", "unknown")
            outcome = (
                "selected"
                if isinstance(status, int) and 200 <= status < 300
                else "failed"
            )
            print(
                f"  Attempt {attempt_number}: {attempt.get('model', 'unknown')} - HTTP {status} ({outcome})"
            )

            error = result.get("error")
            if error:
                print(
                    f"    Error: {error.get('code', 'unknown')} - {error.get('message', 'No message')}"
                )
    print("\n")
# </response_observability_extract>

The Foundry-Features: ModelRouterControls=V1Preview header requests per-request routing metadata. Because this feature is in preview, the metadata presence and response schema can vary by request and service version.

• Reference: AzureOpenAI class

Understand the response envelope

Use the top-level model field and the optional model_selection_details object together to understand how the model router handled a request:

Field	What it describes
model	The underlying model that served the response.
model_selection_details.model_router_details.mode	The routing mode used for the request.
model_selection_details.model_router_details.routing_trace	Ordered model attempts, routing latency, status, and optional errors.
model_selection_details.model_router_details.session_affinity	The affinity mode, session ID source, and final association decision.

The model_selection_details envelope and each child field are optional during preview. Parse fields defensively, and tolerate additional fields in future service versions. Don’t infer routing or affinity details when the corresponding field is absent.

Inspect routing attempts and fallback

Send a Chat Completions request through the model router deployment. The response includes the completion and, when available, routing metadata:

"""
Foundry Model Router - Chat Completions Observability Example

This example demonstrates how to use Azure OpenAI's Chat Completions API
with a Foundry Model Router deployment and inspect the selected model,
routing attempts, latency, and status. Model Router automatically
selects the best underlying LLM for each prompt based on your routing mode
(Balanced, Quality, or Cost).

Prerequisites:
  - An Azure OpenAI resource with a "model-router" deployment
    - A .env file beside this script with AZURE_OPENAI_ENDPOINT,
        AZURE_OPENAI_API_KEY, and MODEL_DEPLOYMENT_NAME

Usage:
    pip install -r requirements.txt
    python model-router-chat-completions-observability.py
"""

import os
from pathlib import Path

from dotenv import load_dotenv
from openai import AzureOpenAI

# Load environment variables from .env beside this script
load_dotenv(Path(__file__).resolve().parent / ".env", override=True)

endpoint = os.environ["AZURE_OPENAI_ENDPOINT"]
api_key = os.environ["AZURE_OPENAI_API_KEY"]
deployment = os.environ["MODEL_DEPLOYMENT_NAME"]

# <response_observability_enable>
client = AzureOpenAI(
    azure_endpoint=endpoint,
    api_key=api_key,
    api_version="2024-10-21",
    default_headers={"Foundry-Features": "ModelRouterControls=V1Preview"},
)
# </response_observability_enable>

response = client.chat.completions.create(
    model=deployment,
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {
            "role": "user",
            "content": "In one sentence, name the most popular tourist destination in Seattle.",
        },
    ],
)

print("--- Chat Completions Response ---")
print(f"Response:{response.choices[0].message.content}")
print(
    f"Usage: {response.usage.prompt_tokens} prompt + {response.usage.completion_tokens} completion = {response.usage.total_tokens} total tokens"
)

# <response_observability_extract>
print(f"\nRouted to model: {response.model}")
print("--- Model Selection Details ---")
model_selection_details = getattr(response, "model_selection_details", None)
if not model_selection_details:
    print("No model selection details were returned.")
else:
    model_router_details = model_selection_details.get("model_router_details", {})
    print(f"Routing mode: {model_router_details.get('mode', 'unknown')}")

    routing_trace = model_router_details.get("routing_trace", [])
    if not routing_trace:
        print("No routing trace was returned.")

    for decision_number, routing_decision in enumerate(routing_trace, start=1):
        latency_ms = routing_decision.get("latency_ms")
        latency = f"{latency_ms} ms" if latency_ms is not None else "not reported"
        print(f"Routing decision {decision_number} (latency: {latency})")

        for attempt_number, attempt in enumerate(
            routing_decision.get("attempts", []), start=1
        ):
            result = attempt.get("result", {})
            status = result.get("status", "unknown")
            outcome = (
                "selected"
                if isinstance(status, int) and 200 <= status < 300
                else "failed"
            )
            print(
                f"  Attempt {attempt_number}: {attempt.get('model', 'unknown')} - HTTP {status} ({outcome})"
            )

            error = result.get("error")
            if error:
                print(
                    f"    Error: {error.get('code', 'unknown')} - {error.get('message', 'No message')}"
                )
    print("\n")
# </response_observability_extract>

• Reference: Chat Completions API

Review the routing trace

The following illustrative model_selection_details fragment contains two ordered model attempts:

{
   "model_selection_details": {
      "model_router_details": {
         "mode": "balanced",
         "routing_trace": [
            {
               "latency_ms": 19,
               "attempts": [
                  {
                     "model": "example-model-a",
                     "result": {
                        "status": 404,
                        "error": {
                           "code": "NotFound",
                           "message": "The request failed."
                        }
                     }
                  },
                  {
                     "model": "example-model-b",
                     "result": {
                        "status": 200
                     }
                  }
               ]
            }
         ]
      }
   }
}

• mode is the routing mode returned for the request.
• routing_trace contains the routing entries returned for the request.
• latency_ms is the latency reported for a routing-trace entry.
• attempts lists model attempts in order.
• Each attempt contains a model and an HTTP status in result.status.
• A failed attempt can include an optional error with a code and message.

Parse routing and fallback information

After the Chat Completions request returns response, inspect the serving model and model selection details:

"""
Foundry Model Router - Chat Completions Observability Example

This example demonstrates how to use Azure OpenAI's Chat Completions API
with a Foundry Model Router deployment and inspect the selected model,
routing attempts, latency, and status. Model Router automatically
selects the best underlying LLM for each prompt based on your routing mode
(Balanced, Quality, or Cost).

Prerequisites:
  - An Azure OpenAI resource with a "model-router" deployment
    - A .env file beside this script with AZURE_OPENAI_ENDPOINT,
        AZURE_OPENAI_API_KEY, and MODEL_DEPLOYMENT_NAME

Usage:
    pip install -r requirements.txt
    python model-router-chat-completions-observability.py
"""

import os
from pathlib import Path

from dotenv import load_dotenv
from openai import AzureOpenAI

# Load environment variables from .env beside this script
load_dotenv(Path(__file__).resolve().parent / ".env", override=True)

endpoint = os.environ["AZURE_OPENAI_ENDPOINT"]
api_key = os.environ["AZURE_OPENAI_API_KEY"]
deployment = os.environ["MODEL_DEPLOYMENT_NAME"]

# <response_observability_enable>
client = AzureOpenAI(
    azure_endpoint=endpoint,
    api_key=api_key,
    api_version="2024-10-21",
    default_headers={"Foundry-Features": "ModelRouterControls=V1Preview"},
)
# </response_observability_enable>

response = client.chat.completions.create(
    model=deployment,
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {
            "role": "user",
            "content": "In one sentence, name the most popular tourist destination in Seattle.",
        },
    ],
)

print("--- Chat Completions Response ---")
print(f"Response:{response.choices[0].message.content}")
print(
    f"Usage: {response.usage.prompt_tokens} prompt + {response.usage.completion_tokens} completion = {response.usage.total_tokens} total tokens"
)

# <response_observability_extract>
print(f"\nRouted to model: {response.model}")
print("--- Model Selection Details ---")
model_selection_details = getattr(response, "model_selection_details", None)
if not model_selection_details:
    print("No model selection details were returned.")
else:
    model_router_details = model_selection_details.get("model_router_details", {})
    print(f"Routing mode: {model_router_details.get('mode', 'unknown')}")

    routing_trace = model_router_details.get("routing_trace", [])
    if not routing_trace:
        print("No routing trace was returned.")

    for decision_number, routing_decision in enumerate(routing_trace, start=1):
        latency_ms = routing_decision.get("latency_ms")
        latency = f"{latency_ms} ms" if latency_ms is not None else "not reported"
        print(f"Routing decision {decision_number} (latency: {latency})")

        for attempt_number, attempt in enumerate(
            routing_decision.get("attempts", []), start=1
        ):
            result = attempt.get("result", {})
            status = result.get("status", "unknown")
            outcome = (
                "selected"
                if isinstance(status, int) and 200 <= status < 300
                else "failed"
            )
            print(
                f"  Attempt {attempt_number}: {attempt.get('model', 'unknown')} - HTTP {status} ({outcome})"
            )

            error = result.get("error")
            if error:
                print(
                    f"    Error: {error.get('code', 'unknown')} - {error.get('message', 'No message')}"
                )
    print("\n")
# </response_observability_extract>

The parsing code produces output similar to the following example for the illustrative routing trace:

--- Chat Completions Response ---
Response:Pike Place Market is Seattle's most popular tourist destination.
Usage: 29 prompt + 278 completion = 307 total tokens

Routed to model: example-model-b
--- Model Selection Details ---
Routing mode: balanced
Routing decision 1 (latency: 19 ms)
   Attempt 1: example-model-a - HTTP 404 (failed)
      Error: NotFound - The request failed.
   Attempt 2: example-model-b - HTTP 200 (selected)

Ordered attempts can reveal automatic fallback for an individual request. In this example, the failed attempt followed by a successful attempt is evidence of fallback. Requests don’t always include multiple attempts, and an attempt can omit error. Model names, HTTP statuses, attempt counts, and latency can vary by request and service version.

Interpret session affinity metadata

Session affinity asks the model router to try the same eligible model for related Chat Completions requests. Configure an opaque, application-owned session ID and use the same value across conversation turns:

"""
Foundry Model Router - Chat Completions Session Affinity Example

This example demonstrates how to use one session ID across separate
Chat Completions requests so Model Router can keep using the associated
eligible model while preserving normal fallback behavior.

Prerequisites:
  - An Azure OpenAI resource with a "model-router" deployment
  - A .env file beside this script with AZURE_OPENAI_ENDPOINT,
    AZURE_OPENAI_API_KEY, and MODEL_DEPLOYMENT_NAME

Usage:
    pip install -r requirements.txt
    python model-router-chat-completions-session-affinity.py
"""

import os
import uuid
from pathlib import Path

from dotenv import load_dotenv
from openai import AzureOpenAI

load_dotenv(Path(__file__).resolve().parent / ".env", override=True)

endpoint = os.environ["AZURE_OPENAI_ENDPOINT"]
api_key = os.environ["AZURE_OPENAI_API_KEY"]
deployment = os.environ["MODEL_DEPLOYMENT_NAME"]

# <session_affinity_enable>
client = AzureOpenAI(
    azure_endpoint=endpoint,
    api_key=api_key,
    api_version="2024-10-21",
    default_headers={"Foundry-Features": "ModelRouterControls=V1Preview"},
)

# Use an opaque application-owned ID without secrets or personal information.
# A fresh value makes the first request in each run establish a new association.
session_id = f"trip-planner-{uuid.uuid4()}"
session_affinity = {
    "routing_config": {
        "session_affinity": {
            "mode": "sticky",
            "session_id": session_id,
        }
    }
}
# </session_affinity_enable>

messages = [
    {"role": "system", "content": "You are a helpful travel planner."},
    {"role": "user", "content": "Plan a one-day family trip to Seattle."},
]

# <session_affinity_turns>
first_response = client.chat.completions.create(
    model=deployment,
    messages=messages,
    extra_body=session_affinity,
)

messages.extend(
    [
        {"role": "assistant", "content": first_response.choices[0].message.content},
        {
            "role": "user",
            "content": "Add restaurant suggestions and indoor activities.",
        },
    ]
)

second_response = client.chat.completions.create(
    model=deployment,
    messages=messages,
    extra_body=session_affinity,
)
# </session_affinity_turns>

def print_response(turn, response):
    print(f"\n--- {turn} ---")
    print(f"Serving model: {response.model}")

    model_selection_details = getattr(response, "model_selection_details", None)
    if not model_selection_details:
        print("No model selection details were returned.")
    else:
        model_router_details = model_selection_details.get("model_router_details", {})

        # <session_affinity_extract>
        affinity_details = model_router_details.get("session_affinity")
        if not affinity_details:
            print("No session affinity details were returned.")
        else:
            print(f"Affinity mode: {affinity_details.get('mode', 'unknown')}")
            print(f"Affinity source: {affinity_details.get('source', 'unknown')}")
            print(f"Affinity decision: {affinity_details.get('decision', 'not reported')}")
        # </session_affinity_extract>

    print(f"Response:\n{response.choices[0].message.content}")

print_response("First turn", first_response)
print_response("Second turn", second_response)

For session ID validation, expiration, and disable behavior, see Keep Chat Completions requests on the same model.

• Reference: AzureOpenAI class

Send related conversation turns

Send the first request, append its response and a new user message to the conversation history, and send the next request with the same session affinity configuration:

"""
Foundry Model Router - Chat Completions Session Affinity Example

This example demonstrates how to use one session ID across separate
Chat Completions requests so Model Router can keep using the associated
eligible model while preserving normal fallback behavior.

Prerequisites:
  - An Azure OpenAI resource with a "model-router" deployment
  - A .env file beside this script with AZURE_OPENAI_ENDPOINT,
    AZURE_OPENAI_API_KEY, and MODEL_DEPLOYMENT_NAME

Usage:
    pip install -r requirements.txt
    python model-router-chat-completions-session-affinity.py
"""

import os
import uuid
from pathlib import Path

from dotenv import load_dotenv
from openai import AzureOpenAI

load_dotenv(Path(__file__).resolve().parent / ".env", override=True)

endpoint = os.environ["AZURE_OPENAI_ENDPOINT"]
api_key = os.environ["AZURE_OPENAI_API_KEY"]
deployment = os.environ["MODEL_DEPLOYMENT_NAME"]

# <session_affinity_enable>
client = AzureOpenAI(
    azure_endpoint=endpoint,
    api_key=api_key,
    api_version="2024-10-21",
    default_headers={"Foundry-Features": "ModelRouterControls=V1Preview"},
)

# Use an opaque application-owned ID without secrets or personal information.
# A fresh value makes the first request in each run establish a new association.
session_id = f"trip-planner-{uuid.uuid4()}"
session_affinity = {
    "routing_config": {
        "session_affinity": {
            "mode": "sticky",
            "session_id": session_id,
        }
    }
}
# </session_affinity_enable>

messages = [
    {"role": "system", "content": "You are a helpful travel planner."},
    {"role": "user", "content": "Plan a one-day family trip to Seattle."},
]

# <session_affinity_turns>
first_response = client.chat.completions.create(
    model=deployment,
    messages=messages,
    extra_body=session_affinity,
)

messages.extend(
    [
        {"role": "assistant", "content": first_response.choices[0].message.content},
        {
            "role": "user",
            "content": "Add restaurant suggestions and indoor activities.",
        },
    ]
)

second_response = client.chat.completions.create(
    model=deployment,
    messages=messages,
    extra_body=session_affinity,
)
# </session_affinity_turns>

def print_response(turn, response):
    print(f"\n--- {turn} ---")
    print(f"Serving model: {response.model}")

    model_selection_details = getattr(response, "model_selection_details", None)
    if not model_selection_details:
        print("No model selection details were returned.")
    else:
        model_router_details = model_selection_details.get("model_router_details", {})

        # <session_affinity_extract>
        affinity_details = model_router_details.get("session_affinity")
        if not affinity_details:
            print("No session affinity details were returned.")
        else:
            print(f"Affinity mode: {affinity_details.get('mode', 'unknown')}")
            print(f"Affinity source: {affinity_details.get('source', 'unknown')}")
            print(f"Affinity decision: {affinity_details.get('decision', 'not reported')}")
        # </session_affinity_extract>

    print(f"Response:\n{response.choices[0].message.content}")

print_response("First turn", first_response)
print_response("Second turn", second_response)

• Reference: Chat Completions API

Review session affinity metadata

The first successful request for a new session ID typically returns an initialize decision. The following illustrative response shows that result:

{
   "model": "example-model-a",
   "model_selection_details": {
      "model_router_details": {
         "mode": "balanced",
         "session_affinity": {
            "mode": "sticky",
            "source": "session_id_payload",
            "decision": "initialize"
         }
      }
   }
}

Interpret the feature-specific fields as follows:

Field	Value	Meaning
mode	sticky	Model router attempts the associated eligible model first.
source	session_id_payload or session_id_header	The request body or header supplied the session ID. The response doesn’t return the identifier.
decision	initialize	No previous association was available, and the initially selected model served the response.
decision	retain	The associated model served the response.
decision	switch	A different model served because of eligibility or fallback.

Parse the affinity decision

Inspect the optional session_affinity object for each response:

"""
Foundry Model Router - Chat Completions Session Affinity Example

This example demonstrates how to use one session ID across separate
Chat Completions requests so Model Router can keep using the associated
eligible model while preserving normal fallback behavior.

Prerequisites:
  - An Azure OpenAI resource with a "model-router" deployment
  - A .env file beside this script with AZURE_OPENAI_ENDPOINT,
    AZURE_OPENAI_API_KEY, and MODEL_DEPLOYMENT_NAME

Usage:
    pip install -r requirements.txt
    python model-router-chat-completions-session-affinity.py
"""

import os
import uuid
from pathlib import Path

from dotenv import load_dotenv
from openai import AzureOpenAI

load_dotenv(Path(__file__).resolve().parent / ".env", override=True)

endpoint = os.environ["AZURE_OPENAI_ENDPOINT"]
api_key = os.environ["AZURE_OPENAI_API_KEY"]
deployment = os.environ["MODEL_DEPLOYMENT_NAME"]

# <session_affinity_enable>
client = AzureOpenAI(
    azure_endpoint=endpoint,
    api_key=api_key,
    api_version="2024-10-21",
    default_headers={"Foundry-Features": "ModelRouterControls=V1Preview"},
)

# Use an opaque application-owned ID without secrets or personal information.
# A fresh value makes the first request in each run establish a new association.
session_id = f"trip-planner-{uuid.uuid4()}"
session_affinity = {
    "routing_config": {
        "session_affinity": {
            "mode": "sticky",
            "session_id": session_id,
        }
    }
}
# </session_affinity_enable>

messages = [
    {"role": "system", "content": "You are a helpful travel planner."},
    {"role": "user", "content": "Plan a one-day family trip to Seattle."},
]

# <session_affinity_turns>
first_response = client.chat.completions.create(
    model=deployment,
    messages=messages,
    extra_body=session_affinity,
)

messages.extend(
    [
        {"role": "assistant", "content": first_response.choices[0].message.content},
        {
            "role": "user",
            "content": "Add restaurant suggestions and indoor activities.",
        },
    ]
)

second_response = client.chat.completions.create(
    model=deployment,
    messages=messages,
    extra_body=session_affinity,
)
# </session_affinity_turns>

def print_response(turn, response):
    print(f"\n--- {turn} ---")
    print(f"Serving model: {response.model}")

    model_selection_details = getattr(response, "model_selection_details", None)
    if not model_selection_details:
        print("No model selection details were returned.")
    else:
        model_router_details = model_selection_details.get("model_router_details", {})

        # <session_affinity_extract>
        affinity_details = model_router_details.get("session_affinity")
        if not affinity_details:
            print("No session affinity details were returned.")
        else:
            print(f"Affinity mode: {affinity_details.get('mode', 'unknown')}")
            print(f"Affinity source: {affinity_details.get('source', 'unknown')}")
            print(f"Affinity decision: {affinity_details.get('decision', 'not reported')}")
        # </session_affinity_extract>

    print(f"Response:\n{response.choices[0].message.content}")

print_response("First turn", first_response)
print_response("Second turn", second_response)

A typical two-turn run produces output similar to the following example:

--- First turn ---
Serving model: example-model-a
Routing mode: balanced
Affinity mode: sticky
Affinity source: session_id_payload
Affinity decision: initialize
Response:
<first-response>

--- Second turn ---
Serving model: example-model-a
Routing mode: balanced
Affinity mode: sticky
Affinity source: session_id_payload
Affinity decision: retain
Response:
<second-response>

If affinity lookup or persistence isn’t available, inference continues through normal routing and the response omits the complete session_affinity object. Don’t infer an affinity decision when the object or decision field is absent.

Diagnose an affinity switch

The session_affinity and routing_trace signals complement each other and can appear in the same response. When decision is switch, check the top-level model field to identify the serving model and routing_trace to understand the attempts or fallback that caused the change:

{
   "model": "example-model-b",
   "model_selection_details": {
      "model_router_details": {
         "mode": "balanced",
         "session_affinity": {
            "mode": "sticky",
            "source": "session_id_payload",
            "decision": "switch"
         },
         "routing_trace": [
            {
               "latency_ms": 51,
               "attempts": [
                  {
                     "model": "example-model-a",
                     "result": { "status": 429 }
                  },
                  {
                     "model": "example-model-b",
                     "result": { "status": 200 }
                  }
               ]
            }
         ]
      }
   }
}

In this example, the associated model returns a retryable response before fallback selects another model. Session affinity works on a best-effort basis: it doesn’t prevent normal fallback or guarantee that related requests use the same model.

For complete application setup and runnable examples, see the Foundry Model Router samples.

Related content

• Use model router
• How model router works
• Monitor model deployments