Configure AI Gateway on model serving endpoints

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In this article, you learn how to configure Mosaic AI Gateway on a model serving endpoint.

Requirements

Configure AI Gateway using the UI

The following shows how to configure AI Gateway during endpoint creation using the Serving UI.

In the AI Gateway section of the endpoint creation page, you can individually configure the following AI Gateway features:

Feature How to enable Details
Usage tracking Select Enable usage tracking to enable tracking and monitoring of data usage metrics. - You must have Unity Catalog enabled.
- Account admins must enable the serving system table schema before using the system tables: system.serving.endpoint_usage which captures token counts for each request to the endpoint and system.serving.served_entities which stores metadata for each external model.
- See Usage tracking table schemas
- Only account admins have permission to view or query the served_entities table or endpoint_usage table, even though the user that manages the endpoint must enable usage tracking. See Grant access to system tables
- The input and output token count are estimated as (text_length+1)/4 if the token count is not returned by the model.
Payload logging Select Enable inference tables to automatically log requests and responses from your endpoint into Delta tables managed by Unity Catalog. - You must have Unity Catalog enabled and CREATE_TABLE access in the specified catalog schema.
- Inference tables enabled by AI Gateway have a different schema than those created for model serving endpoints that serve custom models. See AI Gateway-enabled inference table schema.
- Payload logging data populates these tables less than hour after querying the endpoint.
- Payloads larger than 1 MB are not logged.
- The response payload aggregates the response of all of the returned chunks.
- Streaming is supported. In streaming scenarios the response payload aggregates the response of returned chunks.
AI Guardrails See Configure AI Guardrails in the UI. - Guardrails prevent the model from interacting with unsafe and harmful content that is detected in model inputs and outputs.
- Output guardrails are not supported for embeddings models or for streaming.
Rate limits You can enforce request rate limits to manage traffic for your endpoint on a per user and per endpoint basis - Rate limits are defined in queries per minute (QPM).
- The default is No limit for both per user and per endpoint.
Traffic routing To configure traffic routing on your endpoint, see Serve multiple external models to an endpoint.

Configure AI Gateway features

Configure AI Guardrails in the UI

The following table shows how to configure supported guardrails.

Guardrail How to enable Details
Safety Select Safety to enable safeguards to prevent your model from interacting with unsafe and harmful content.
Personally identifiable information (PII) detection Select PII detection to detect PII data such as names, addresses, credit card numbers.
Valid topics You can type topics directly into this field. If you have multiple entries, be sure to press enter after each topic. Alternatively, you can upload a .csv or .txt file. A maximum of 50 valid topics can be specified. Each topic cannot exceed 100 characters
Invalid keywords You can type topics directly into this field. If you have multiple entries, be sure to press enter after each topic. Alternatively, you can upload a .csv or .txt file. A maximum of 50 invalid keywords can be specified. Each keyword cannot exceed 100 characters.

Configure AI Guardrail features

Usage tracking table schemas

The system.serving.served_entities usage tracking system table has the following schema:

Column name Description Type
served_entity_id The unique ID of the served entity. STRING
account_id The customer account ID for Delta Sharing. STRING
workspace_id The customer workspace ID of the serving endpoint. STRING
created_by The ID of the creator. STRING
endpoint_name The name of the serving endpoint. STRING
endpoint_id The unique ID of the serving endpoint. STRING
served_entity_name The name of the served entity. STRING
entity_type Type of the entity that is served. Can be FEATURE_SPEC, EXTERNAL_MODEL, FOUNDATION_MODEL, or CUSTOM_MODEL STRING
entity_name The underlying name of the entity. Different from the served_entity_name which is a user provided name. For example, entity_name is the name of the Unity Catalog model. STRING
entity_version The version of the served entity. STRING
endpoint_config_version The version of the endpoint configuration. INT
task The task type. Can be llm/v1/chat, llm/v1/completions, or llm/v1/embeddings. STRING
external_model_config Configurations for external models. For example, {Provider: OpenAI} STRUCT
foundation_model_config Configurations for foundation models. For example,{min_provisioned_throughput: null, max_provisioned_throughput: null, pt_model_type: mpt-7b} STRUCT
custom_model_config Configurations for custom models. For example,{ min_concurrency: 0, max_concurrency: 4, compute_type: CPU } STRUCT
feature_spec_config Configurations for feature specifications. For example, { min_concurrency: 0, max_concurrency: 4, compute_type: CPU } STRUCT
change_time Timestamp of change for the served entity. TIMESTAMP
endpoint_delete_time Timestamp of entity deletion. The endpoint is the container for the served entity. After the endpoint is deleted, the served entity is also deleted. TIMESTAMP

The system.serving.endpoint_usage usage tracking system table has the following schema:

Column name Description Type
account_id The customer account ID. STRING
workspace_id The customer workspace id of the serving endpoint. STRING
client_request_id The user provided request identifier that can be specified in the model serving request body. STRING
databricks_request_id A Azure Databricks generated request identifier attached to all model serving requests. STRING
requester The ID of the user or service principal whose permissions are used for the invocation request of the serving endpoint. STRING
status_code The HTTP status code that was returned from the model. INTEGER
request_time The timestamp at which the request is received. TIMESTAMP
input_token_count The token count of the input. LONG
output_token_count The token count of the output. LONG
input_character_count The character count of the input string or prompt. LONG
output_character_count The character count of the output string of the response. LONG
usage_context The user provided map containing identifiers of the end user or the customer application that makes the call to the endpoint. See Further define usage with usage_context. MAP
request_streaming Whether the request is in stream mode. BOOLEAN
served_entity_id The unique ID used to join with the system.serving.served_entities dimension table to lookup information about the endpoint and served entity. STRING

Further define usage with usage_context

When you query an external model with usage tracking enabled, you can provide the usage_context parameter with type Map[String, String]. The usage context mapping appears in the usage tracking table in the usage_context column. Account admins can aggregate different rows based on the usage context to get insights and can join this information with the information in the payload logging table. For example, you can add end_user_to_charge to the usage_context for tracking cost attribution for end users.

{
  "messages": [
    {
      "role": "user",
      "content": "What is Databricks?"
    }
  ],
  "max_tokens": 128,
  "usage_context":
    {
      "use_case": "external",
      "project": "project1",
      "priority": "high",
      "end_user_to_charge": "abcde12345",
      "a_b_test_group": "group_a"
    }
}

AI Gateway-enabled inference table schema

Inference tables enabled using AI Gateway have the following schema:

Column name Description Type
request_date The UTC date on which the model serving request was received. DATE
databricks_request_id A Azure Databricks generated request identifier attached to all model serving requests. STRING
client_request_id An optional client generated request identifier that can be specified in the model serving request body. STRING
request_time The timestamp at which the request is received. TIMESTAMP
status_code The HTTP status code that was returned from the model. INT
sampling_fraction The sampling fraction used in the event that the request was down-sampled. This value is between 0 and 1, where 1 represents that 100% of incoming requests were included. DOUBLE
execution_duration_ms The time in milliseconds for which the model performed inference. This does not include overhead network latencies and only represents the time it took for the model to generate predictions. BIGINT
request The raw request JSON body that was sent to the model serving endpoint. STRING
response The raw response JSON body that was returned by the model serving endpoint. STRING
served_entity_id The unique ID of the served entity. STRING
logging_error_codes ARRAY
requester The ID of the user or service principal whose permissions are used for the invocation request of the serving endpoint. STRING

Update AI Gateway features on endpoints

You can update AI Gateway features on model serving endpoints that had them previously enabled and endpoints that did not. Updates to AI Gateway configurations take about 20-40 seconds to be applied, however rate limiting updates can take up to 60 seconds.

The following shows how to update AI Gateway features on a model serving endpoint using the Serving UI.

In the Gateway section of the endpoint page, you can see which features are enabled. To update these features, click Edit AI Gateway.

Update AI Gateway features

Notebook example

The following notebook shows how to programmatically enable and use Databricks Mosaic AI Gateway features to manage and govern models from providers.

Enable Databricks Mosaic AI Gateway features notebook

Get notebook

Additional resources