# Create a new PT serving endpoint Launch stage: Public Preview `POST /api/2.0/serving-endpoints/pt` Create a new PT serving endpoint. API scopes: model-serving ## Request body - `name` (string, required) The name of the serving endpoint. This field is required and must be unique across a . An endpoint name can consist of alphanumeric characters, dashes, and underscores. Example: `feed-ads` - `config` (object, required) The core config of the serving endpoint. - `served_entities` (array of object, optional) The list of served entities under the serving endpoint config. - `name` (string, optional) The name of a served entity. It must be unique across an endpoint. A served entity name can consist of alphanumeric characters, dashes, and underscores. If not specified for an external model, this field defaults to external_model.name, with '.' and ':' replaced with '-', and if not specified for other entities, it defaults to entity_name-entity_version. Example: `ads-model-3` - `entity_name` (string, required) The name of the entity to be served. The entity may be a model in the Databricks Model Registry, a model in the Unity Catalog (UC), or a function of type FEATURE_SPEC in the UC. If it is a UC object, the full name of the object should be given in the form of **catalog_name.schema_name.model_name**. Example: `ads-model` - `entity_version` (string, optional) Example: `3` - `provisioned_model_units` (int64, required) The number of model units to be provisioned. Example: `1000` - `burst_scaling_enabled` (boolean, optional) Whether burst scaling is enabled. When enabled (default), the endpoint can automatically scale up beyond provisioned capacity to handle traffic spikes. When disabled, the endpoint maintains fixed capacity at provisioned_model_units. Example: `true` - `traffic_config` (object, optional) - `routes` (array of object, optional) The list of routes that define traffic to each served entity. - `served_model_name` (string, optional) The name of the served model this route configures traffic for. Example: `ads-model-3` - `traffic_percentage` (int32, required) The percentage of endpoint traffic to send to this route. It must be an integer between 0 and 100 inclusive. Example: `100` - `served_entity_name` (string, optional) Example: `ads-model-3` - `tags` (array of object, optional) Tags to be attached to the serving endpoint and automatically propagated to billing logs. - `key` (string, required) Key field for a serving endpoint tag. Example: `team` - `value` (string, optional) Optional value field for a serving endpoint tag. Example: `data science` - `ai_gateway` (object, optional) The AI Gateway configuration for the serving endpoint. - `usage_tracking_config` (object, optional) Configuration to enable usage tracking using system tables. These tables allow you to monitor operational usage on endpoints and their associated costs. - `enabled` (boolean, optional) Whether to enable usage tracking. Example: `true` - `inference_table_config` (object, optional) Configuration for payload logging using inference tables. Use these tables to monitor and audit data being sent to and received from model APIs and to improve model quality. - `catalog_name` (string, optional) The name of the catalog in Unity Catalog. Required when enabling inference tables. NOTE: On update, you have to disable inference table first in order to change the catalog name. Example: `my-catalog` - `schema_name` (string, optional) The name of the schema in Unity Catalog. Required when enabling inference tables. NOTE: On update, you have to disable inference table first in order to change the schema name. Example: `my-schema` - `table_name_prefix` (string, optional) The prefix of the table in Unity Catalog. NOTE: On update, you have to disable inference table first in order to change the prefix name. Example: `my-prefix` - `enabled` (boolean, optional) Indicates whether the inference table is enabled. Example: `true` - `rate_limits` (array of object, optional) Configuration for rate limits which can be set to limit endpoint traffic. - `calls` (int64, optional) Used to specify how many calls are allowed for a key within the renewal_period. Example: `15` - `key` (string, optional) Key field for a rate limit. Currently, 'user', 'user_group, 'service_principal', and 'endpoint' are supported, with 'endpoint' being the default if not specified. - `renewal_period` (string, required) Renewal period field for a rate limit. Currently, only 'minute' is supported. - `principal` (string, optional) Principal field for a user, user group, or service principal to apply rate limiting to. Accepts a user email, group name, or service principal application ID. Example: `user@test.com` - `tokens` (int64, optional) Used to specify how many tokens are allowed for a key within the renewal_period. Example: `10000` - `guardrails` (object, optional) Configuration for AI Guardrails to prevent unwanted data and unsafe data in requests and responses. - `input` (object, optional) Configuration for input guardrail filters. - `safety` (boolean, optional) Indicates whether the safety filter is enabled. Example: `true` - `pii` (object, optional) Configuration for guardrail PII filter. - `behavior` (string, optional) Configuration for input guardrail filters. Possible values: - `BEHAVIOR_UNSPECIFIED` - `NONE` - `BLOCK` - `MASK` Example: `BLOCK` - `valid_topics` (array of string, optional, Deprecated) The list of allowed topics. Given a chat request, this guardrail flags the request if its topic is not in the allowed topics. - `invalid_keywords` (array of string, optional, Deprecated) List of invalid keywords. AI guardrail uses keyword or string matching to decide if the keyword exists in the request or response content. - `output` (object, optional) Configuration for output guardrail filters. - `safety` (boolean, optional) Indicates whether the safety filter is enabled. Example: `true` - `pii` (object, optional) Configuration for guardrail PII filter. - `behavior` (string, optional) Configuration for input guardrail filters. Possible values: - `BEHAVIOR_UNSPECIFIED` - `NONE` - `BLOCK` - `MASK` Example: `BLOCK` - `valid_topics` (array of string, optional, Deprecated) The list of allowed topics. Given a chat request, this guardrail flags the request if its topic is not in the allowed topics. - `invalid_keywords` (array of string, optional, Deprecated) List of invalid keywords. AI guardrail uses keyword or string matching to decide if the keyword exists in the request or response content. - `fallback_config` (object, optional) Configuration for traffic fallback which auto fallbacks to other served entities if the request to a served entity fails with certain error codes, to increase availability. - `enabled` (boolean, required) Whether to enable traffic fallback. When a served entity in the serving endpoint returns specific error codes (e.g. 500), the request will automatically be round-robin attempted with other served entities in the same endpoint, following the order of served entity list, until a successful response is returned. If all attempts fail, return the last response with the error code. Example: `true` - `budget_policy_id` (string, optional) The budget policy associated with the endpoint. - `email_notifications` (object, optional) Email notification settings. - `on_update_success` (array of string, optional) A list of email addresses to be notified when an endpoint successfully updates its configuration or state. Example: `user.name@databricks.com` - `on_update_failure` (array of string, optional) A list of email addresses to be notified when an endpoint fails to update its configuration or state. Example: `user.name@databricks.com` ## Returns Returns the InferenceEndpointDetailed object. ## Response ```json { "name": "feed-ads", "creator": "alice@company.com", "creation_timestamp": 0, "last_updated_timestamp": 0, "state": { "ready": "READY", "config_update": "NOT_UPDATING" }, "config": { "config_version": 0, "served_entities": [ {} ], "served_models": [ {} ], "traffic_config": {}, "auto_capture_config": {} }, "pending_config": { "served_entities": [ {} ], "served_models": [ {} ], "traffic_config": {}, "config_version": 0, "start_time": 0, "auto_capture_config": {} }, "id": "88fd3f75a0d24b0380ddc40484d7a31b", "permission_level": "CAN_MANAGE", "tags": [ { "key": "team", "value": "data science" } ], "task": "model-serving-task", "route_optimized": true, "endpoint_url": "string", "data_plane_info": { "query_info": {} }, "ai_gateway": { "usage_tracking_config": {}, "inference_table_config": {}, "rate_limits": [ {} ], "guardrails": {}, "fallback_config": {} }, "budget_policy_id": "string", "email_notifications": { "on_update_success": [ "user.name@databricks.com" ], "on_update_failure": [ "user.name@databricks.com" ] }, "description": "string", "telemetry_config": { "telemetry_profile_id": "string", "table_names": {}, "inference_table_config": {}, "enabled_telemetry_features": [ "TELEMETRY_FEATURE_LOGS" ] } } ```