Configure AI Gateway on model serving endpoints

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

Requirements

• A Databricks workspace in Model Serving supported region.

• Complete steps 1 and 2 of Create an external model serving endpoint.

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 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.

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 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.	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

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 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.

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

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Additional resources

• Mosaic AI Gateway.

• Inference tables for monitoring and debugging models.