# Update `PUT /api/2.0/serving-endpoints/{name}/config` Updates any combination of the serving endpoint's served entities, the compute configuration of those served entities, and the endpoint's traffic config. An endpoint that already has an update in progress can not be updated until the current update completes or fails. API scopes: model-serving ## Path parameters - `name` (string, optional) The name of the serving endpoint to update. This field is required. Example: `feed-ads` Constraints: `[ 1 .. 63 ] characters` ## Request body - `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` - `external_model` (object, optional) The external model to be served. NOTE: Only one of external_model and (entity_name, entity_version, workload_size, workload_type, and scale_to_zero_enabled) can be specified with the latter set being used for custom model serving for a registered model. For an existing endpoint with external_model, it cannot be updated to an endpoint without external_model. If the endpoint is created without external_model, users cannot update it to add external_model later. The task type of all external models within an endpoint must be the same. - `provider` (string, optional) The name of the provider for the external model. Currently, the supported providers are 'ai21labs', 'anthropic', 'amazon-bedrock', 'cohere', 'databricks-model-serving', 'google-cloud-vertex-ai', 'openai', 'palm', and 'custom'. - `name` (string, optional) The name of the external model. Example: `gpt-4` - `task` (string, optional) The task type of the external model. Example: `llm/v1/chat` - `ai21labs_config` (object, required) AI21Labs Config. Only required if the provider is 'ai21labs'. - `ai21labs_api_key` (string, optional) The secret key reference for an AI21 Labs API key. If you prefer to paste your API key directly, see `ai21labs_api_key_plaintext`. You must provide an API key using one of the following fields: `ai21labs_api_key` or `ai21labs_api_key_plaintext`. Example: `{{secrets/my_scope/my_ai21labs_api_key}}` - `ai21labs_api_key_plaintext` (string, optional) An AI21 Labs API key provided as a plaintext string. If you prefer to reference your key using Databricks Secrets, see `ai21labs_api_key`. You must provide an API key using one of the following fields: `ai21labs_api_key` or `ai21labs_api_key_plaintext`. Example: `Your API Key` - `anthropic_config` (object, required) Anthropic Config. Only required if the provider is 'anthropic'. - `anthropic_api_key` (string, optional) The secret key reference for an Anthropic API key. If you prefer to paste your API key directly, see `anthropic_api_key_plaintext`. You must provide an API key using one of the following fields: `anthropic_api_key` or `anthropic_api_key_plaintext`. Example: `{{secrets/my_scope/my_anthropic_api_key}}` - `anthropic_api_key_plaintext` (string, optional) The Anthropic API key provided as a plaintext string. If you prefer to reference your key using Databricks Secrets, see `anthropic_api_key`. You must provide an API key using one of the following fields: `anthropic_api_key` or `anthropic_api_key_plaintext`. Example: `Your API Key` - `amazon_bedrock_config` (object, required) Amazon Bedrock Config. Only required if the provider is 'amazon-bedrock'. - `aws_region` (string, optional) The AWS region to use. Bedrock has to be enabled there. Example: `myAwsRegion` - `aws_access_key_id` (string, optional) The secret key reference for an AWS access key ID with permissions to interact with Bedrock services. If you prefer to paste your API key directly, see `aws_access_key_id_plaintext`. You must provide an API key using one of the following fields: `aws_access_key_id` or `aws_access_key_id_plaintext`. Example: `{{secrets/my_scope/my_aws_access_key_id}}` - `aws_secret_access_key` (string, optional) The secret key reference for an AWS secret access key paired with the access key ID, with permissions to interact with Bedrock services. If you prefer to paste your API key directly, see `aws_secret_access_key_plaintext`. You must provide an API key using one of the following fields: `aws_secret_access_key` or `aws_secret_access_key_plaintext`. Example: `{{secrets/my_scope/my_aws_secret_access_key}}` - `bedrock_provider` (string, optional) The underlying provider in Amazon Bedrock. Supported values (case insensitive) include: Anthropic, Cohere, AI21Labs, Amazon. - `aws_access_key_id_plaintext` (string, optional) An AWS access key ID with permissions to interact with Bedrock services provided as a plaintext string. If you prefer to reference your key using Databricks Secrets, see `aws_access_key_id`. You must provide an API key using one of the following fields: `aws_access_key_id` or `aws_access_key_id_plaintext`. Example: `Your AWS Access Key ID` - `aws_secret_access_key_plaintext` (string, optional) An AWS secret access key paired with the access key ID, with permissions to interact with Bedrock services provided as a plaintext string. If you prefer to reference your key using Databricks Secrets, see `aws_secret_access_key`. You must provide an API key using one of the following fields: `aws_secret_access_key` or `aws_secret_access_key_plaintext`. Example: `Your AWS Secret Access Key` - `instance_profile_arn` (string, optional) ARN of the instance profile that the external model will use to access AWS resources. You must authenticate using an instance profile or access keys. If you prefer to authenticate using access keys, see `aws_access_key_id`, `aws_access_key_id_plaintext`, `aws_secret_access_key` and `aws_secret_access_key_plaintext`. Example: `arn:aws:iam::123456789012:instance-profile/my-instance-profile` - `cohere_config` (object, required) Cohere Config. Only required if the provider is 'cohere'. - `cohere_api_key` (string, optional) The secret key reference for a Cohere API key. If you prefer to paste your API key directly, see `cohere_api_key_plaintext`. You must provide an API key using one of the following fields: `cohere_api_key` or `cohere_api_key_plaintext`. Example: `{{secrets/my_scope/my_cohere_api_key}}` - `cohere_api_key_plaintext` (string, optional) The Cohere API key provided as a plaintext string. If you prefer to reference your key using Databricks Secrets, see `cohere_api_key`. You must provide an API key using one of the following fields: `cohere_api_key` or `cohere_api_key_plaintext`. Example: `Your API Key` - `cohere_api_base` (string, optional) This is an optional field to provide a customized base URL for the Cohere API. If left unspecified, the standard Cohere base URL is used. Example: `https://api.cohere.ai/v1` - `google_cloud_vertex_ai_config` (object, required) Google Cloud Vertex AI Config. Only required if the provider is 'google-cloud-vertex-ai'. - `private_key` (string, optional) The secret key reference for a private key for the service account which has access to the Google Cloud Vertex AI Service. See [Best practices for managing service account keys]. If you prefer to paste your API key directly, see `private_key_plaintext`. You must provide an API key using one of the following fields: `private_key` or `private_key_plaintext` [Best practices for managing service account keys]: https://cloud.google.com/iam/docs/best-practices-for-managing-service-account-keys Example: `{{secrets/my_scope/my_google_cloud_vertex_ai_api_key}}` - `project_id` (string, optional) This is the Google Cloud project id that the service account is associated with. Example: `your-project-id` - `region` (string, optional) This is the region for the Google Cloud Vertex AI Service. See [supported regions] for more details. Some models are only available in specific regions. [supported regions]: https://cloud.google.com/vertex-ai/docs/general/locations Example: `us-central1` - `private_key_plaintext` (string, optional) The private key for the service account which has access to the Google Cloud Vertex AI Service provided as a plaintext secret. See [Best practices for managing service account keys]. If you prefer to reference your key using Databricks Secrets, see `private_key`. You must provide an API key using one of the following fields: `private_key` or `private_key_plaintext`. [Best practices for managing service account keys]: https://cloud.google.com/iam/docs/best-practices-for-managing-service-account-keys Example: `Your API Key` - `databricks_model_serving_config` (object, required) Databricks Model Serving Config. Only required if the provider is 'databricks-model-serving'. - `databricks_api_token` (string, optional) The secret key reference for a Databricks API token that corresponds to a user or service principal with Can Query access to the model serving endpoint pointed to by this external model. If you prefer to paste your API key directly, see `databricks_api_token_plaintext`. You must provide an API key using one of the following fields: `databricks_api_token` or `databricks_api_token_plaintext`. Example: `{{secrets/my_scope/my_databricks_api_token}}` - `databricks_workspace_url` (string, optional) The URL of the workspace containing the model serving endpoint pointed to by this external model. Example: `https://my-databricks-workspace.com` - `databricks_api_token_plaintext` (string, optional) The Databricks API token that corresponds to a user or service principal with Can Query access to the model serving endpoint pointed to by this external model provided as a plaintext string. If you prefer to reference your key using Databricks Secrets, see `databricks_api_token`. You must provide an API key using one of the following fields: `databricks_api_token` or `databricks_api_token_plaintext`. Example: `Your Databricks API Token` - `openai_config` (object, required) OpenAI Config. Only required if the provider is 'openai'. - `openai_api_key` (string, optional) The secret key reference for an OpenAI API key using the OpenAI or Azure service. If you prefer to paste your API key directly, see `openai_api_key_plaintext`. You must provide an API key using one of the following fields: `openai_api_key` or `openai_api_key_plaintext`. Example: `{{secrets/my_scope/my_openai_api_key}}` - `openai_api_type` (string, optional) This is an optional field to specify the type of OpenAI API to use. For Azure OpenAI, this field is required, and adjust this parameter to represent the preferred security access validation protocol. For access token validation, use azure. For authentication using Azure Active Directory (Azure AD) use, azuread. Example: `azure` - `openai_api_base` (string, optional) This is a field to provide a customized base URl for the OpenAI API. For Azure OpenAI, this field is required, and is the base URL for the Azure OpenAI API service provided by Azure. For other OpenAI API types, this field is optional, and if left unspecified, the standard OpenAI base URL is used. Example: `https://api.openai.com/v1` - `openai_api_version` (string, optional) This is an optional field to specify the OpenAI API version. For Azure OpenAI, this field is required, and is the version of the Azure OpenAI service to utilize, specified by a date. Example: `2023-11-01` - `openai_deployment_name` (string, optional) This field is only required for Azure OpenAI and is the name of the deployment resource for the Azure OpenAI service. Example: `my_deployment_resource` - `openai_organization` (string, optional) This is an optional field to specify the organization in OpenAI or Azure OpenAI. Example: `Databricks` - `microsoft_entra_tenant_id` (string, optional) This field is only required for Azure AD OpenAI and is the Microsoft Entra Tenant ID. Example: `12345678-abcd-1234-5678-12345678abcd` - `microsoft_entra_client_id` (string, optional) This field is only required for Azure AD OpenAI and is the Microsoft Entra Client ID. Example: `12345678-abcd-1234-5678-12345678abcd` - `microsoft_entra_client_secret` (string, optional) The secret key reference for a client secret used for Microsoft Entra ID authentication. If you prefer to paste your client secret directly, see `microsoft_entra_client_secret_plaintext`. You must provide an API key using one of the following fields: `microsoft_entra_client_secret` or `microsoft_entra_client_secret_plaintext`. Example: `{{secrets/my_scope/my_microsoft_entra_client_secret}}` - `openai_api_key_plaintext` (string, optional) The OpenAI API key using the OpenAI or Azure service provided as a plaintext string. If you prefer to reference your key using Databricks Secrets, see `openai_api_key`. You must provide an API key using one of the following fields: `openai_api_key` or `openai_api_key_plaintext`. Example: `Your API Key` - `microsoft_entra_client_secret_plaintext` (string, optional) The client secret used for Microsoft Entra ID authentication provided as a plaintext string. If you prefer to reference your key using Databricks Secrets, see `microsoft_entra_client_secret`. You must provide an API key using one of the following fields: `microsoft_entra_client_secret` or `microsoft_entra_client_secret_plaintext`. Example: `Your Microsoft Entra Client Secret` - `palm_config` (object, required) PaLM Config. Only required if the provider is 'palm'. - `palm_api_key` (string, optional) The secret key reference for a PaLM API key. If you prefer to paste your API key directly, see `palm_api_key_plaintext`. You must provide an API key using one of the following fields: `palm_api_key` or `palm_api_key_plaintext`. Example: `{{secrets/my_scope/my_palm_api_key}}` - `palm_api_key_plaintext` (string, optional) The PaLM API key provided as a plaintext string. If you prefer to reference your key using Databricks Secrets, see `palm_api_key`. You must provide an API key using one of the following fields: `palm_api_key` or `palm_api_key_plaintext`. Example: `Your API Key` - `custom_provider_config` (object, required) Custom Provider Config. Only required if the provider is 'custom'. - `custom_provider_url` (string, optional) This is a field to provide the URL of the custom provider API. Example: `https://custom-provider.com` - `bearer_token_auth` (object, optional) This is a field to provide bearer token authentication for the custom provider API. You can only specify one authentication method. - `api_key_auth` (object, optional) This is a field to provide API key authentication for the custom provider API. You can only specify one authentication method. - `entity_name` (string, optional) 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` - `min_provisioned_throughput` (int32, optional) The minimum tokens per second that the endpoint can scale down to. Example: `970` - `max_provisioned_throughput` (int32, optional) The maximum tokens per second that the endpoint can scale up to. Example: `1960` - `min_provisioned_concurrency` (int32, optional) The minimum provisioned concurrency that the endpoint can scale down to. Do not use if workload_size is specified. Example: `8` - `max_provisioned_concurrency` (int32, optional) The maximum provisioned concurrency that the endpoint can scale up to. Do not use if workload_size is specified. Example: `32` - `workload_size` (string, optional) The workload size of the served entity. The workload size corresponds to a range of provisioned concurrency that the compute autoscales between. A single unit of provisioned concurrency can process one request at a time. Valid workload sizes are "Small" (4 - 4 provisioned concurrency), "Medium" (8 - 16 provisioned concurrency), and "Large" (16 - 64 provisioned concurrency). Additional custom workload sizes can also be used when available in the workspace. If scale-to-zero is enabled, the lower bound of the provisioned concurrency for each workload size is 0. Do not use if min_provisioned_concurrency and max_provisioned_concurrency are specified. - `provisioned_model_units` (int64, optional) The number of model units provisioned. Example: `100` - `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` - `scale_to_zero_enabled` (boolean, optional) Whether the compute resources for the served entity should scale down to zero. Example: `false` - `model_name` (string, optional) - `model_version` (string, optional) - `environment_vars` (object, optional) An object containing a set of optional, user-specified environment variable key-value pairs used for serving this entity. Note: this is an experimental feature and subject to change. Example entity environment variables that refer to secrets: `{"OPENAI_API_KEY": "{{secrets/my_scope/my_key}}", "DATABRICKS_TOKEN": "{{secrets/my_scope2/my_key2}}"}` - `instance_profile_arn` (string, optional) ARN of the instance profile that the served entity uses to access AWS resources. - `foundation_model` (object, optional) - `name` (string, optional) - `display_name` (string, optional) - `docs` (string, optional) - `description` (string, optional) - `state` (object, optional) - `deployment` (string, optional) Possible values: `DEPLOYMENT_UNKNOWN`, `DEPLOYMENT_CREATING`, `DEPLOYMENT_RECOVERING`, `DEPLOYMENT_READY`, `DEPLOYMENT_FAILED`, `DEPLOYMENT_ABORTED`, `DEPLOYMENT_STOPPED` - `deployment_state_message` (string, optional) - `creator` (string, optional) - `creation_timestamp` (int64, optional) - `served_models` (array of object, optional) (Deprecated, use served_entities instead) The list of served models 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` - `external_model` (object, optional) The external model to be served. NOTE: Only one of external_model and (entity_name, entity_version, workload_size, workload_type, and scale_to_zero_enabled) can be specified with the latter set being used for custom model serving for a registered model. For an existing endpoint with external_model, it cannot be updated to an endpoint without external_model. If the endpoint is created without external_model, users cannot update it to add external_model later. The task type of all external models within an endpoint must be the same. - `provider` (string, optional) The name of the provider for the external model. Currently, the supported providers are 'ai21labs', 'anthropic', 'amazon-bedrock', 'cohere', 'databricks-model-serving', 'google-cloud-vertex-ai', 'openai', 'palm', and 'custom'. - `name` (string, optional) The name of the external model. Example: `gpt-4` - `task` (string, optional) The task type of the external model. Example: `llm/v1/chat` - `ai21labs_config` (object, required) AI21Labs Config. Only required if the provider is 'ai21labs'. - `ai21labs_api_key` (string, optional) The secret key reference for an AI21 Labs API key. If you prefer to paste your API key directly, see `ai21labs_api_key_plaintext`. You must provide an API key using one of the following fields: `ai21labs_api_key` or `ai21labs_api_key_plaintext`. Example: `{{secrets/my_scope/my_ai21labs_api_key}}` - `ai21labs_api_key_plaintext` (string, optional) An AI21 Labs API key provided as a plaintext string. If you prefer to reference your key using Databricks Secrets, see `ai21labs_api_key`. You must provide an API key using one of the following fields: `ai21labs_api_key` or `ai21labs_api_key_plaintext`. Example: `Your API Key` - `anthropic_config` (object, required) Anthropic Config. Only required if the provider is 'anthropic'. - `anthropic_api_key` (string, optional) The secret key reference for an Anthropic API key. If you prefer to paste your API key directly, see `anthropic_api_key_plaintext`. You must provide an API key using one of the following fields: `anthropic_api_key` or `anthropic_api_key_plaintext`. Example: `{{secrets/my_scope/my_anthropic_api_key}}` - `anthropic_api_key_plaintext` (string, optional) The Anthropic API key provided as a plaintext string. If you prefer to reference your key using Databricks Secrets, see `anthropic_api_key`. You must provide an API key using one of the following fields: `anthropic_api_key` or `anthropic_api_key_plaintext`. Example: `Your API Key` - `amazon_bedrock_config` (object, required) Amazon Bedrock Config. Only required if the provider is 'amazon-bedrock'. - `aws_region` (string, optional) The AWS region to use. Bedrock has to be enabled there. Example: `myAwsRegion` - `aws_access_key_id` (string, optional) The secret key reference for an AWS access key ID with permissions to interact with Bedrock services. If you prefer to paste your API key directly, see `aws_access_key_id_plaintext`. You must provide an API key using one of the following fields: `aws_access_key_id` or `aws_access_key_id_plaintext`. Example: `{{secrets/my_scope/my_aws_access_key_id}}` - `aws_secret_access_key` (string, optional) The secret key reference for an AWS secret access key paired with the access key ID, with permissions to interact with Bedrock services. If you prefer to paste your API key directly, see `aws_secret_access_key_plaintext`. You must provide an API key using one of the following fields: `aws_secret_access_key` or `aws_secret_access_key_plaintext`. Example: `{{secrets/my_scope/my_aws_secret_access_key}}` - `bedrock_provider` (string, optional) The underlying provider in Amazon Bedrock. Supported values (case insensitive) include: Anthropic, Cohere, AI21Labs, Amazon. - `aws_access_key_id_plaintext` (string, optional) An AWS access key ID with permissions to interact with Bedrock services provided as a plaintext string. If you prefer to reference your key using Databricks Secrets, see `aws_access_key_id`. You must provide an API key using one of the following fields: `aws_access_key_id` or `aws_access_key_id_plaintext`. Example: `Your AWS Access Key ID` - `aws_secret_access_key_plaintext` (string, optional) An AWS secret access key paired with the access key ID, with permissions to interact with Bedrock services provided as a plaintext string. If you prefer to reference your key using Databricks Secrets, see `aws_secret_access_key`. You must provide an API key using one of the following fields: `aws_secret_access_key` or `aws_secret_access_key_plaintext`. Example: `Your AWS Secret Access Key` - `instance_profile_arn` (string, optional) ARN of the instance profile that the external model will use to access AWS resources. You must authenticate using an instance profile or access keys. If you prefer to authenticate using access keys, see `aws_access_key_id`, `aws_access_key_id_plaintext`, `aws_secret_access_key` and `aws_secret_access_key_plaintext`. Example: `arn:aws:iam::123456789012:instance-profile/my-instance-profile` - `cohere_config` (object, required) Cohere Config. Only required if the provider is 'cohere'. - `cohere_api_key` (string, optional) The secret key reference for a Cohere API key. If you prefer to paste your API key directly, see `cohere_api_key_plaintext`. You must provide an API key using one of the following fields: `cohere_api_key` or `cohere_api_key_plaintext`. Example: `{{secrets/my_scope/my_cohere_api_key}}` - `cohere_api_key_plaintext` (string, optional) The Cohere API key provided as a plaintext string. If you prefer to reference your key using Databricks Secrets, see `cohere_api_key`. You must provide an API key using one of the following fields: `cohere_api_key` or `cohere_api_key_plaintext`. Example: `Your API Key` - `cohere_api_base` (string, optional) This is an optional field to provide a customized base URL for the Cohere API. If left unspecified, the standard Cohere base URL is used. Example: `https://api.cohere.ai/v1` - `google_cloud_vertex_ai_config` (object, required) Google Cloud Vertex AI Config. Only required if the provider is 'google-cloud-vertex-ai'. - `private_key` (string, optional) The secret key reference for a private key for the service account which has access to the Google Cloud Vertex AI Service. See [Best practices for managing service account keys]. If you prefer to paste your API key directly, see `private_key_plaintext`. You must provide an API key using one of the following fields: `private_key` or `private_key_plaintext` [Best practices for managing service account keys]: https://cloud.google.com/iam/docs/best-practices-for-managing-service-account-keys Example: `{{secrets/my_scope/my_google_cloud_vertex_ai_api_key}}` - `project_id` (string, optional) This is the Google Cloud project id that the service account is associated with. Example: `your-project-id` - `region` (string, optional) This is the region for the Google Cloud Vertex AI Service. See [supported regions] for more details. Some models are only available in specific regions. [supported regions]: https://cloud.google.com/vertex-ai/docs/general/locations Example: `us-central1` - `private_key_plaintext` (string, optional) The private key for the service account which has access to the Google Cloud Vertex AI Service provided as a plaintext secret. See [Best practices for managing service account keys]. If you prefer to reference your key using Databricks Secrets, see `private_key`. You must provide an API key using one of the following fields: `private_key` or `private_key_plaintext`. [Best practices for managing service account keys]: https://cloud.google.com/iam/docs/best-practices-for-managing-service-account-keys Example: `Your API Key` - `databricks_model_serving_config` (object, required) Databricks Model Serving Config. Only required if the provider is 'databricks-model-serving'. - `databricks_api_token` (string, optional) The secret key reference for a Databricks API token that corresponds to a user or service principal with Can Query access to the model serving endpoint pointed to by this external model. If you prefer to paste your API key directly, see `databricks_api_token_plaintext`. You must provide an API key using one of the following fields: `databricks_api_token` or `databricks_api_token_plaintext`. Example: `{{secrets/my_scope/my_databricks_api_token}}` - `databricks_workspace_url` (string, optional) The URL of the workspace containing the model serving endpoint pointed to by this external model. Example: `https://my-databricks-workspace.com` - `databricks_api_token_plaintext` (string, optional) The Databricks API token that corresponds to a user or service principal with Can Query access to the model serving endpoint pointed to by this external model provided as a plaintext string. If you prefer to reference your key using Databricks Secrets, see `databricks_api_token`. You must provide an API key using one of the following fields: `databricks_api_token` or `databricks_api_token_plaintext`. Example: `Your Databricks API Token` - `openai_config` (object, required) OpenAI Config. Only required if the provider is 'openai'. - `openai_api_key` (string, optional) The secret key reference for an OpenAI API key using the OpenAI or Azure service. If you prefer to paste your API key directly, see `openai_api_key_plaintext`. You must provide an API key using one of the following fields: `openai_api_key` or `openai_api_key_plaintext`. Example: `{{secrets/my_scope/my_openai_api_key}}` - `openai_api_type` (string, optional) This is an optional field to specify the type of OpenAI API to use. For Azure OpenAI, this field is required, and adjust this parameter to represent the preferred security access validation protocol. For access token validation, use azure. For authentication using Azure Active Directory (Azure AD) use, azuread. Example: `azure` - `openai_api_base` (string, optional) This is a field to provide a customized base URl for the OpenAI API. For Azure OpenAI, this field is required, and is the base URL for the Azure OpenAI API service provided by Azure. For other OpenAI API types, this field is optional, and if left unspecified, the standard OpenAI base URL is used. Example: `https://api.openai.com/v1` - `openai_api_version` (string, optional) This is an optional field to specify the OpenAI API version. For Azure OpenAI, this field is required, and is the version of the Azure OpenAI service to utilize, specified by a date. Example: `2023-11-01` - `openai_deployment_name` (string, optional) This field is only required for Azure OpenAI and is the name of the deployment resource for the Azure OpenAI service. Example: `my_deployment_resource` - `openai_organization` (string, optional) This is an optional field to specify the organization in OpenAI or Azure OpenAI. Example: `Databricks` - `microsoft_entra_tenant_id` (string, optional) This field is only required for Azure AD OpenAI and is the Microsoft Entra Tenant ID. Example: `12345678-abcd-1234-5678-12345678abcd` - `microsoft_entra_client_id` (string, optional) This field is only required for Azure AD OpenAI and is the Microsoft Entra Client ID. Example: `12345678-abcd-1234-5678-12345678abcd` - `microsoft_entra_client_secret` (string, optional) The secret key reference for a client secret used for Microsoft Entra ID authentication. If you prefer to paste your client secret directly, see `microsoft_entra_client_secret_plaintext`. You must provide an API key using one of the following fields: `microsoft_entra_client_secret` or `microsoft_entra_client_secret_plaintext`. Example: `{{secrets/my_scope/my_microsoft_entra_client_secret}}` - `openai_api_key_plaintext` (string, optional) The OpenAI API key using the OpenAI or Azure service provided as a plaintext string. If you prefer to reference your key using Databricks Secrets, see `openai_api_key`. You must provide an API key using one of the following fields: `openai_api_key` or `openai_api_key_plaintext`. Example: `Your API Key` - `microsoft_entra_client_secret_plaintext` (string, optional) The client secret used for Microsoft Entra ID authentication provided as a plaintext string. If you prefer to reference your key using Databricks Secrets, see `microsoft_entra_client_secret`. You must provide an API key using one of the following fields: `microsoft_entra_client_secret` or `microsoft_entra_client_secret_plaintext`. Example: `Your Microsoft Entra Client Secret` - `palm_config` (object, required) PaLM Config. Only required if the provider is 'palm'. - `palm_api_key` (string, optional) The secret key reference for a PaLM API key. If you prefer to paste your API key directly, see `palm_api_key_plaintext`. You must provide an API key using one of the following fields: `palm_api_key` or `palm_api_key_plaintext`. Example: `{{secrets/my_scope/my_palm_api_key}}` - `palm_api_key_plaintext` (string, optional) The PaLM API key provided as a plaintext string. If you prefer to reference your key using Databricks Secrets, see `palm_api_key`. You must provide an API key using one of the following fields: `palm_api_key` or `palm_api_key_plaintext`. Example: `Your API Key` - `custom_provider_config` (object, required) Custom Provider Config. Only required if the provider is 'custom'. - `custom_provider_url` (string, optional) This is a field to provide the URL of the custom provider API. Example: `https://custom-provider.com` - `bearer_token_auth` (object, optional) This is a field to provide bearer token authentication for the custom provider API. You can only specify one authentication method. - `api_key_auth` (object, optional) This is a field to provide API key authentication for the custom provider API. You can only specify one authentication method. - `entity_name` (string, optional) 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` - `min_provisioned_throughput` (int32, optional) The minimum tokens per second that the endpoint can scale down to. Example: `970` - `max_provisioned_throughput` (int32, optional) The maximum tokens per second that the endpoint can scale up to. Example: `1960` - `min_provisioned_concurrency` (int32, optional) The minimum provisioned concurrency that the endpoint can scale down to. Do not use if workload_size is specified. Example: `8` - `max_provisioned_concurrency` (int32, optional) The maximum provisioned concurrency that the endpoint can scale up to. Do not use if workload_size is specified. Example: `32` - `workload_size` (string, optional) The workload size of the served entity. The workload size corresponds to a range of provisioned concurrency that the compute autoscales between. A single unit of provisioned concurrency can process one request at a time. Valid workload sizes are "Small" (4 - 4 provisioned concurrency), "Medium" (8 - 16 provisioned concurrency), and "Large" (16 - 64 provisioned concurrency). Additional custom workload sizes can also be used when available in the workspace. If scale-to-zero is enabled, the lower bound of the provisioned concurrency for each workload size is 0. Do not use if min_provisioned_concurrency and max_provisioned_concurrency are specified. - `provisioned_model_units` (int64, optional) The number of model units provisioned. Example: `100` - `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` - `scale_to_zero_enabled` (boolean, optional) Whether the compute resources for the served entity should scale down to zero. Example: `false` - `model_name` (string, optional) - `model_version` (string, optional) - `environment_vars` (object, optional) An object containing a set of optional, user-specified environment variable key-value pairs used for serving this entity. Note: this is an experimental feature and subject to change. Example entity environment variables that refer to secrets: `{"OPENAI_API_KEY": "{{secrets/my_scope/my_key}}", "DATABRICKS_TOKEN": "{{secrets/my_scope2/my_key2}}"}` - `instance_profile_arn` (string, optional) ARN of the instance profile that the served entity uses to access AWS resources. - `foundation_model` (object, optional) - `name` (string, optional) - `display_name` (string, optional) - `docs` (string, optional) - `description` (string, optional) - `state` (object, optional) - `deployment` (string, optional) Possible values: `DEPLOYMENT_UNKNOWN`, `DEPLOYMENT_CREATING`, `DEPLOYMENT_RECOVERING`, `DEPLOYMENT_READY`, `DEPLOYMENT_FAILED`, `DEPLOYMENT_ABORTED`, `DEPLOYMENT_STOPPED` - `deployment_state_message` (string, optional) - `creator` (string, optional) - `creation_timestamp` (int64, optional) - `traffic_config` (object, optional) The traffic configuration associated with the serving endpoint config. - `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, optional) 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` - `auto_capture_config` (object, optional) Configuration for legacy Inference Tables which automatically log requests and responses to Unity Catalog. Deprecated: please use AI Gateway inference tables instead. See https://docs.databricks.com/aws/en/ai-gateway/inference-tables. - `catalog_name` (string, optional) The name of the catalog in Unity Catalog. NOTE: On update, you cannot change the catalog name if the inference table is already enabled. Example: `my-catalog` - `schema_name` (string, optional) The name of the schema in Unity Catalog. NOTE: On update, you cannot change the schema name if the inference table is already enabled. Example: `my-schema` - `table_name_prefix` (string, optional) The prefix of the table in Unity Catalog. NOTE: On update, you cannot change the prefix name if the inference table is already enabled. Example: `my-prefix-` - `state` (object, optional) - `payload_table` (object, optional) - `name` (string, optional) - `status` (string, optional) - `status_message` (string, optional) - `enabled` (boolean, optional) Indicates whether the inference table is enabled. Example: `true` ## Returns - `name` (string, optional) The name of the serving endpoint. Example: `feed-ads` - `creator` (string, optional) The email of the user who created the serving endpoint. Example: `alice@company.com` - `creation_timestamp` (int64, optional) The timestamp when the endpoint was created in Unix time. - `last_updated_timestamp` (int64, optional) The timestamp when the endpoint was last updated by a user in Unix time. - `state` (object, optional) Information corresponding to the state of the serving endpoint. - `ready` (string, optional) The state of an endpoint, indicating whether or not the endpoint is queryable. An endpoint is READY if all of the served entities in its active configuration are ready. If any of the actively served entities are in a non-ready state, the endpoint state will be NOT_READY. Possible values: `READY_STATE_UNSPECIFIED`, `READY`, `NOT_READY` - `config_update` (string, optional) The state of an endpoint's config update. This informs the user if the pending_config is in progress, if the update failed, or if there is no update in progress. Note that if the endpoint's config_update state value is IN_PROGRESS, another update can not be made until the update completes or fails. Possible values: `CONFIG_UPDATE_STATE_UNSPECIFIED`, `NOT_UPDATING`, `IN_PROGRESS`, `UPDATE_FAILED`, `UPDATE_CANCELED` - `config` (object, optional) The config that is currently being served by the endpoint. - `config_version` (int64, optional) The config version that the serving endpoint is currently serving. - `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` - `external_model` (object, optional) The external model to be served. NOTE: Only one of external_model and (entity_name, entity_version, workload_size, workload_type, and scale_to_zero_enabled) can be specified with the latter set being used for custom model serving for a registered model. For an existing endpoint with external_model, it cannot be updated to an endpoint without external_model. If the endpoint is created without external_model, users cannot update it to add external_model later. The task type of all external models within an endpoint must be the same. - `provider` (string, optional) The name of the provider for the external model. Currently, the supported providers are 'ai21labs', 'anthropic', 'amazon-bedrock', 'cohere', 'databricks-model-serving', 'google-cloud-vertex-ai', 'openai', 'palm', and 'custom'. - `name` (string, optional) The name of the external model. Example: `gpt-4` - `task` (string, optional) The task type of the external model. Example: `llm/v1/chat` - `ai21labs_config` (object, required) AI21Labs Config. Only required if the provider is 'ai21labs'. - `anthropic_config` (object, required) Anthropic Config. Only required if the provider is 'anthropic'. - `amazon_bedrock_config` (object, required) Amazon Bedrock Config. Only required if the provider is 'amazon-bedrock'. - `cohere_config` (object, required) Cohere Config. Only required if the provider is 'cohere'. - `google_cloud_vertex_ai_config` (object, required) Google Cloud Vertex AI Config. Only required if the provider is 'google-cloud-vertex-ai'. - `databricks_model_serving_config` (object, required) Databricks Model Serving Config. Only required if the provider is 'databricks-model-serving'. - `openai_config` (object, required) OpenAI Config. Only required if the provider is 'openai'. - `palm_config` (object, required) PaLM Config. Only required if the provider is 'palm'. - `custom_provider_config` (object, required) Custom Provider Config. Only required if the provider is 'custom'. - `entity_name` (string, optional) 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` - `min_provisioned_throughput` (int32, optional) The minimum tokens per second that the endpoint can scale down to. Example: `970` - `max_provisioned_throughput` (int32, optional) The maximum tokens per second that the endpoint can scale up to. Example: `1960` - `min_provisioned_concurrency` (int32, optional) The minimum provisioned concurrency that the endpoint can scale down to. Do not use if workload_size is specified. Example: `8` - `max_provisioned_concurrency` (int32, optional) The maximum provisioned concurrency that the endpoint can scale up to. Do not use if workload_size is specified. Example: `32` - `workload_size` (string, optional) The workload size of the served entity. The workload size corresponds to a range of provisioned concurrency that the compute autoscales between. A single unit of provisioned concurrency can process one request at a time. Valid workload sizes are "Small" (4 - 4 provisioned concurrency), "Medium" (8 - 16 provisioned concurrency), and "Large" (16 - 64 provisioned concurrency). Additional custom workload sizes can also be used when available in the workspace. If scale-to-zero is enabled, the lower bound of the provisioned concurrency for each workload size is 0. Do not use if min_provisioned_concurrency and max_provisioned_concurrency are specified. - `provisioned_model_units` (int64, optional) The number of model units provisioned. Example: `100` - `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` - `scale_to_zero_enabled` (boolean, optional) Whether the compute resources for the served entity should scale down to zero. Example: `false` - `model_name` (string, optional) - `model_version` (string, optional) - `environment_vars` (object, optional) An object containing a set of optional, user-specified environment variable key-value pairs used for serving this entity. Note: this is an experimental feature and subject to change. Example entity environment variables that refer to secrets: `{"OPENAI_API_KEY": "{{secrets/my_scope/my_key}}", "DATABRICKS_TOKEN": "{{secrets/my_scope2/my_key2}}"}` - `instance_profile_arn` (string, optional) ARN of the instance profile that the served entity uses to access AWS resources. - `foundation_model` (object, optional) - `name` (string, optional) - `display_name` (string, optional) - `docs` (string, optional) - `description` (string, optional) - `state` (object, optional) - `deployment` (string, optional) Possible values: `DEPLOYMENT_UNKNOWN`, `DEPLOYMENT_CREATING`, `DEPLOYMENT_RECOVERING`, `DEPLOYMENT_READY`, `DEPLOYMENT_FAILED`, `DEPLOYMENT_ABORTED`, `DEPLOYMENT_STOPPED` - `deployment_state_message` (string, optional) - `creator` (string, optional) - `creation_timestamp` (int64, optional) - `served_models` (array of object, optional) (Deprecated, use served_entities instead) The list of served models 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` - `external_model` (object, optional) The external model to be served. NOTE: Only one of external_model and (entity_name, entity_version, workload_size, workload_type, and scale_to_zero_enabled) can be specified with the latter set being used for custom model serving for a registered model. For an existing endpoint with external_model, it cannot be updated to an endpoint without external_model. If the endpoint is created without external_model, users cannot update it to add external_model later. The task type of all external models within an endpoint must be the same. - `provider` (string, optional) The name of the provider for the external model. Currently, the supported providers are 'ai21labs', 'anthropic', 'amazon-bedrock', 'cohere', 'databricks-model-serving', 'google-cloud-vertex-ai', 'openai', 'palm', and 'custom'. - `name` (string, optional) The name of the external model. Example: `gpt-4` - `task` (string, optional) The task type of the external model. Example: `llm/v1/chat` - `ai21labs_config` (object, required) AI21Labs Config. Only required if the provider is 'ai21labs'. - `anthropic_config` (object, required) Anthropic Config. Only required if the provider is 'anthropic'. - `amazon_bedrock_config` (object, required) Amazon Bedrock Config. Only required if the provider is 'amazon-bedrock'. - `cohere_config` (object, required) Cohere Config. Only required if the provider is 'cohere'. - `google_cloud_vertex_ai_config` (object, required) Google Cloud Vertex AI Config. Only required if the provider is 'google-cloud-vertex-ai'. - `databricks_model_serving_config` (object, required) Databricks Model Serving Config. Only required if the provider is 'databricks-model-serving'. - `openai_config` (object, required) OpenAI Config. Only required if the provider is 'openai'. - `palm_config` (object, required) PaLM Config. Only required if the provider is 'palm'. - `custom_provider_config` (object, required) Custom Provider Config. Only required if the provider is 'custom'. - `entity_name` (string, optional) 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` - `min_provisioned_throughput` (int32, optional) The minimum tokens per second that the endpoint can scale down to. Example: `970` - `max_provisioned_throughput` (int32, optional) The maximum tokens per second that the endpoint can scale up to. Example: `1960` - `min_provisioned_concurrency` (int32, optional) The minimum provisioned concurrency that the endpoint can scale down to. Do not use if workload_size is specified. Example: `8` - `max_provisioned_concurrency` (int32, optional) The maximum provisioned concurrency that the endpoint can scale up to. Do not use if workload_size is specified. Example: `32` - `workload_size` (string, optional) The workload size of the served entity. The workload size corresponds to a range of provisioned concurrency that the compute autoscales between. A single unit of provisioned concurrency can process one request at a time. Valid workload sizes are "Small" (4 - 4 provisioned concurrency), "Medium" (8 - 16 provisioned concurrency), and "Large" (16 - 64 provisioned concurrency). Additional custom workload sizes can also be used when available in the workspace. If scale-to-zero is enabled, the lower bound of the provisioned concurrency for each workload size is 0. Do not use if min_provisioned_concurrency and max_provisioned_concurrency are specified. - `provisioned_model_units` (int64, optional) The number of model units provisioned. Example: `100` - `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` - `scale_to_zero_enabled` (boolean, optional) Whether the compute resources for the served entity should scale down to zero. Example: `false` - `model_name` (string, optional) - `model_version` (string, optional) - `environment_vars` (object, optional) An object containing a set of optional, user-specified environment variable key-value pairs used for serving this entity. Note: this is an experimental feature and subject to change. Example entity environment variables that refer to secrets: `{"OPENAI_API_KEY": "{{secrets/my_scope/my_key}}", "DATABRICKS_TOKEN": "{{secrets/my_scope2/my_key2}}"}` - `instance_profile_arn` (string, optional) ARN of the instance profile that the served entity uses to access AWS resources. - `foundation_model` (object, optional) - `name` (string, optional) - `display_name` (string, optional) - `docs` (string, optional) - `description` (string, optional) - `state` (object, optional) - `deployment` (string, optional) Possible values: `DEPLOYMENT_UNKNOWN`, `DEPLOYMENT_CREATING`, `DEPLOYMENT_RECOVERING`, `DEPLOYMENT_READY`, `DEPLOYMENT_FAILED`, `DEPLOYMENT_ABORTED`, `DEPLOYMENT_STOPPED` - `deployment_state_message` (string, optional) - `creator` (string, optional) - `creation_timestamp` (int64, optional) - `traffic_config` (object, optional) The traffic configuration associated with the serving endpoint config. - `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, optional) 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` - `auto_capture_config` (object, optional) Configuration for legacy Inference Tables which automatically log requests and responses to Unity Catalog. Deprecated: please use AI Gateway inference tables instead. See https://docs.databricks.com/aws/en/ai-gateway/inference-tables. - `catalog_name` (string, optional) The name of the catalog in Unity Catalog. NOTE: On update, you cannot change the catalog name if the inference table is already enabled. Example: `my-catalog` - `schema_name` (string, optional) The name of the schema in Unity Catalog. NOTE: On update, you cannot change the schema name if the inference table is already enabled. Example: `my-schema` - `table_name_prefix` (string, optional) The prefix of the table in Unity Catalog. NOTE: On update, you cannot change the prefix name if the inference table is already enabled. Example: `my-prefix-` - `state` (object, optional) - `payload_table` (object, optional) - `enabled` (boolean, optional) Indicates whether the inference table is enabled. Example: `true` - `pending_config` (object, optional) The config that the endpoint is attempting to update to. - `served_entities` (array of object, optional) The list of served entities belonging to the last issued update to the serving endpoint. - `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` - `external_model` (object, optional) The external model to be served. NOTE: Only one of external_model and (entity_name, entity_version, workload_size, workload_type, and scale_to_zero_enabled) can be specified with the latter set being used for custom model serving for a registered model. For an existing endpoint with external_model, it cannot be updated to an endpoint without external_model. If the endpoint is created without external_model, users cannot update it to add external_model later. The task type of all external models within an endpoint must be the same. - `provider` (string, optional) The name of the provider for the external model. Currently, the supported providers are 'ai21labs', 'anthropic', 'amazon-bedrock', 'cohere', 'databricks-model-serving', 'google-cloud-vertex-ai', 'openai', 'palm', and 'custom'. - `name` (string, optional) The name of the external model. Example: `gpt-4` - `task` (string, optional) The task type of the external model. Example: `llm/v1/chat` - `ai21labs_config` (object, required) AI21Labs Config. Only required if the provider is 'ai21labs'. - `anthropic_config` (object, required) Anthropic Config. Only required if the provider is 'anthropic'. - `amazon_bedrock_config` (object, required) Amazon Bedrock Config. Only required if the provider is 'amazon-bedrock'. - `cohere_config` (object, required) Cohere Config. Only required if the provider is 'cohere'. - `google_cloud_vertex_ai_config` (object, required) Google Cloud Vertex AI Config. Only required if the provider is 'google-cloud-vertex-ai'. - `databricks_model_serving_config` (object, required) Databricks Model Serving Config. Only required if the provider is 'databricks-model-serving'. - `openai_config` (object, required) OpenAI Config. Only required if the provider is 'openai'. - `palm_config` (object, required) PaLM Config. Only required if the provider is 'palm'. - `custom_provider_config` (object, required) Custom Provider Config. Only required if the provider is 'custom'. - `entity_name` (string, optional) 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` - `min_provisioned_throughput` (int32, optional) The minimum tokens per second that the endpoint can scale down to. Example: `970` - `max_provisioned_throughput` (int32, optional) The maximum tokens per second that the endpoint can scale up to. Example: `1960` - `min_provisioned_concurrency` (int32, optional) The minimum provisioned concurrency that the endpoint can scale down to. Do not use if workload_size is specified. Example: `8` - `max_provisioned_concurrency` (int32, optional) The maximum provisioned concurrency that the endpoint can scale up to. Do not use if workload_size is specified. Example: `32` - `workload_size` (string, optional) The workload size of the served entity. The workload size corresponds to a range of provisioned concurrency that the compute autoscales between. A single unit of provisioned concurrency can process one request at a time. Valid workload sizes are "Small" (4 - 4 provisioned concurrency), "Medium" (8 - 16 provisioned concurrency), and "Large" (16 - 64 provisioned concurrency). Additional custom workload sizes can also be used when available in the workspace. If scale-to-zero is enabled, the lower bound of the provisioned concurrency for each workload size is 0. Do not use if min_provisioned_concurrency and max_provisioned_concurrency are specified. - `provisioned_model_units` (int64, optional) The number of model units provisioned. Example: `100` - `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` - `scale_to_zero_enabled` (boolean, optional) Whether the compute resources for the served entity should scale down to zero. Example: `false` - `model_name` (string, optional) - `model_version` (string, optional) - `environment_vars` (object, optional) An object containing a set of optional, user-specified environment variable key-value pairs used for serving this entity. Note: this is an experimental feature and subject to change. Example entity environment variables that refer to secrets: `{"OPENAI_API_KEY": "{{secrets/my_scope/my_key}}", "DATABRICKS_TOKEN": "{{secrets/my_scope2/my_key2}}"}` - `instance_profile_arn` (string, optional) ARN of the instance profile that the served entity uses to access AWS resources. - `foundation_model` (object, optional) - `name` (string, optional) - `display_name` (string, optional) - `docs` (string, optional) - `description` (string, optional) - `state` (object, optional) - `deployment` (string, optional) Possible values: `DEPLOYMENT_UNKNOWN`, `DEPLOYMENT_CREATING`, `DEPLOYMENT_RECOVERING`, `DEPLOYMENT_READY`, `DEPLOYMENT_FAILED`, `DEPLOYMENT_ABORTED`, `DEPLOYMENT_STOPPED` - `deployment_state_message` (string, optional) - `creator` (string, optional) - `creation_timestamp` (int64, optional) - `served_models` (array of object, optional) (Deprecated, use served_entities instead) The list of served models belonging to the last issued update to the serving endpoint. - `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` - `external_model` (object, optional) The external model to be served. NOTE: Only one of external_model and (entity_name, entity_version, workload_size, workload_type, and scale_to_zero_enabled) can be specified with the latter set being used for custom model serving for a registered model. For an existing endpoint with external_model, it cannot be updated to an endpoint without external_model. If the endpoint is created without external_model, users cannot update it to add external_model later. The task type of all external models within an endpoint must be the same. - `provider` (string, optional) The name of the provider for the external model. Currently, the supported providers are 'ai21labs', 'anthropic', 'amazon-bedrock', 'cohere', 'databricks-model-serving', 'google-cloud-vertex-ai', 'openai', 'palm', and 'custom'. - `name` (string, optional) The name of the external model. Example: `gpt-4` - `task` (string, optional) The task type of the external model. Example: `llm/v1/chat` - `ai21labs_config` (object, required) AI21Labs Config. Only required if the provider is 'ai21labs'. - `anthropic_config` (object, required) Anthropic Config. Only required if the provider is 'anthropic'. - `amazon_bedrock_config` (object, required) Amazon Bedrock Config. Only required if the provider is 'amazon-bedrock'. - `cohere_config` (object, required) Cohere Config. Only required if the provider is 'cohere'. - `google_cloud_vertex_ai_config` (object, required) Google Cloud Vertex AI Config. Only required if the provider is 'google-cloud-vertex-ai'. - `databricks_model_serving_config` (object, required) Databricks Model Serving Config. Only required if the provider is 'databricks-model-serving'. - `openai_config` (object, required) OpenAI Config. Only required if the provider is 'openai'. - `palm_config` (object, required) PaLM Config. Only required if the provider is 'palm'. - `custom_provider_config` (object, required) Custom Provider Config. Only required if the provider is 'custom'. - `entity_name` (string, optional) 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` - `min_provisioned_throughput` (int32, optional) The minimum tokens per second that the endpoint can scale down to. Example: `970` - `max_provisioned_throughput` (int32, optional) The maximum tokens per second that the endpoint can scale up to. Example: `1960` - `min_provisioned_concurrency` (int32, optional) The minimum provisioned concurrency that the endpoint can scale down to. Do not use if workload_size is specified. Example: `8` - `max_provisioned_concurrency` (int32, optional) The maximum provisioned concurrency that the endpoint can scale up to. Do not use if workload_size is specified. Example: `32` - `workload_size` (string, optional) The workload size of the served entity. The workload size corresponds to a range of provisioned concurrency that the compute autoscales between. A single unit of provisioned concurrency can process one request at a time. Valid workload sizes are "Small" (4 - 4 provisioned concurrency), "Medium" (8 - 16 provisioned concurrency), and "Large" (16 - 64 provisioned concurrency). Additional custom workload sizes can also be used when available in the workspace. If scale-to-zero is enabled, the lower bound of the provisioned concurrency for each workload size is 0. Do not use if min_provisioned_concurrency and max_provisioned_concurrency are specified. - `provisioned_model_units` (int64, optional) The number of model units provisioned. Example: `100` - `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` - `scale_to_zero_enabled` (boolean, optional) Whether the compute resources for the served entity should scale down to zero. Example: `false` - `model_name` (string, optional) - `model_version` (string, optional) - `environment_vars` (object, optional) An object containing a set of optional, user-specified environment variable key-value pairs used for serving this entity. Note: this is an experimental feature and subject to change. Example entity environment variables that refer to secrets: `{"OPENAI_API_KEY": "{{secrets/my_scope/my_key}}", "DATABRICKS_TOKEN": "{{secrets/my_scope2/my_key2}}"}` - `instance_profile_arn` (string, optional) ARN of the instance profile that the served entity uses to access AWS resources. - `foundation_model` (object, optional) - `name` (string, optional) - `display_name` (string, optional) - `docs` (string, optional) - `description` (string, optional) - `state` (object, optional) - `deployment` (string, optional) Possible values: `DEPLOYMENT_UNKNOWN`, `DEPLOYMENT_CREATING`, `DEPLOYMENT_RECOVERING`, `DEPLOYMENT_READY`, `DEPLOYMENT_FAILED`, `DEPLOYMENT_ABORTED`, `DEPLOYMENT_STOPPED` - `deployment_state_message` (string, optional) - `creator` (string, optional) - `creation_timestamp` (int64, optional) - `traffic_config` (object, optional) The traffic config defining how invocations to the serving endpoint should be routed. - `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, optional) 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` - `config_version` (int32, optional) The config version that the serving endpoint is currently serving. - `start_time` (int64, optional) The timestamp when the update to the pending config started. - `auto_capture_config` (object, optional) Configuration for legacy Inference Tables which automatically log requests and responses to Unity Catalog. Deprecated: please use AI Gateway inference tables instead. See https://docs.databricks.com/aws/en/ai-gateway/inference-tables. - `catalog_name` (string, optional) The name of the catalog in Unity Catalog. NOTE: On update, you cannot change the catalog name if the inference table is already enabled. Example: `my-catalog` - `schema_name` (string, optional) The name of the schema in Unity Catalog. NOTE: On update, you cannot change the schema name if the inference table is already enabled. Example: `my-schema` - `table_name_prefix` (string, optional) The prefix of the table in Unity Catalog. NOTE: On update, you cannot change the prefix name if the inference table is already enabled. Example: `my-prefix-` - `state` (object, optional) - `payload_table` (object, optional) - `enabled` (boolean, optional) Indicates whether the inference table is enabled. Example: `true` - `id` (string, optional) System-generated ID of the endpoint. This is used to refer to the endpoint in the Permissions API Example: `88fd3f75a0d24b0380ddc40484d7a31b` - `permission_level` (string, optional) The permission level of the principal making the request. Possible values: `CAN_MANAGE`, `CAN_QUERY`, `CAN_VIEW` Example: `CAN_MANAGE` - `tags` (array of object, optional) Tags attached to the serving endpoint. - `key` (string, optional) Key field for a serving endpoint tag. Example: `team` - `value` (string, optional) Optional value field for a serving endpoint tag. Example: `data science` - `task` (string, optional) The task type of the serving endpoint. Example: `model-serving-task` - `route_optimized` (boolean, optional) Boolean representing if route optimization has been enabled for the endpoint Example: `true` - `endpoint_url` (string, optional) Endpoint invocation url if route optimization is enabled for endpoint - `data_plane_info` (object, optional) Information required to query DataPlane APIs. - `query_info` (object, optional) Information required to query DataPlane API 'query' endpoint. - `endpoint_url` (string, optional) The URL of the endpoint for this operation in the dataplane. - `authorization_details` (string, optional) Authorization details as a string. - `ai_gateway` (object, optional) The AI Gateway configuration for the serving endpoint. NOTE: External model, provisioned throughput, and pay-per-token endpoints are fully supported; agent endpoints currently only support inference tables. - `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, optional) 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. - `valid_topics` (array of string, optional) 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) 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. - `valid_topics` (array of string, optional) 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) 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, optional) 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` - `description` (string, optional) Description of the serving model - `telemetry_config` (object, optional) Telemetry configuration for the endpoint, including inference-table payload logging. - `telemetry_profile_id` (string, required) The ID of an existing telemetry profile to apply to this endpoint. Provide this to reuse a telemetry profile that has already been created, instead of specifying table_names. - `table_names` (object, required) The Unity Catalog tables to which endpoint telemetry (logs, traces, and metrics) is exported. Provide this to create a new telemetry profile for the endpoint from the given tables. - `logs_table` (string, optional) The full three-level Unity Catalog name (catalog.schema.table) of the table that receives exported logs. - `metrics_table` (string, optional) The full three-level Unity Catalog name (catalog.schema.table) of the table that receives exported metrics. - `traces_table` (string, optional) The full three-level Unity Catalog name (catalog.schema.table) of the table that receives exported traces (spans). - `annotations_table` (string, optional) The full three-level Unity Catalog name (catalog.schema.table) of the table that receives exported annotations. - `inference_table_config` (object, optional) Configuration for inference table payload logging, including sampling. - `sampling_fraction` (double, optional) Fraction of requests sampled for payload logging, in the range [0.0, 1.0], where 1.0 logs all requests. - `name` (string, optional) The full name of the inference table created for this endpoint. - `enabled_telemetry_features` (array of string, optional) The telemetry signals to enable for this endpoint. If empty or omitted, all signals are enabled; otherwise only the listed signals are enabled. Possible values: `TELEMETRY_FEATURE_LOGS`, `TELEMETRY_FEATURE_TRACES`, `TELEMETRY_FEATURE_METRICS`, `TELEMETRY_FEATURE_INFERENCE_TABLE` ## Request ### External model endpoint ```json { "served_entities": [ { "external_model": { "name": "gpt-4", "openai_config": { "openai_api_key": "{{secrets/my_scope/my_openai_api_key}}" }, "provider": "openai", "task": "llm/v1/chat" }, "name": "openapi_chat" } ] } ``` ### Feature Serving endpoint ```json { "served_entities": [ { "entity_name": "feature_store.me.transport_spec", "name": "feature-spec-transport", "scale_to_zero_enabled": true, "workload_size": "Small" } ] } ``` ### Models in Unity Catalog endpoint ```json { "served_entities": [ { "entity_name": "myCatalog.mySchema.my-ads-model", "entity_version": "3", "scale_to_zero_enabled": true, "workload_size": "Small" }, { "entity_name": "myCatalog.mySchema.my-ads-model", "entity_version": "4", "scale_to_zero_enabled": true, "workload_size": "Small" } ], "traffic_config": { "routes": [ { "served_model_name": "my-ads-model-3", "traffic_percentage": 80 }, { "served_model_name": "my-ads-model-4", "traffic_percentage": 20 } ] } } ``` ### Workspace model endpoint ```json { "served_entities": [ { "entity_name": "my-ads-model", "entity_version": "5", "scale_to_zero_enabled": true, "workload_size": "Small" } ], "traffic_config": { "routes": [ { "served_model_name": "my-ads-model-5", "traffic_percentage": 100 } ] } } ``` ## Response ### External model endpoint ```json { "config": { "config_version": 1, "served_entities": [ { "creation_timestamp": 1699617587000, "creator": "user@email.com", "external_model": { "name": "gpt-4", "openai_config": { "openai_api_key": "{{secrets/my_scope/my_openai_api_key}}" }, "provider": "openai", "task": "llm/v1/chat" }, "name": "openai_chat", "state": { "deployment": "DEPLOYMENT_READY", "deployment_state_message": "" } } ], "traffic_config": { "routes": [ { "served_model_name": "openai_chat", "traffic_percentage": 100 } ] } }, "creation_timestamp": 1699617587000, "creator": "user@email.com", "id": "69962db6b9db47c4a8a222d2ac79d7f8", "last_updated_timestamp": 1699617587000, "name": "openai_endpoint", "permission_level": "CAN_MANAGE", "route_optimized": false, "state": { "ready": "READY" } } ``` ### Feature Serving endpoint ```json { "config": { "config_version": 1, "served_entities": [ { "creation_timestamp": 1700089760000, "creator": "user@email.com", "entity_name": "feature_store.me.travel_spec", "name": "feature-spec-travel", "scale_to_zero_enabled": true, "state": { "deployment": "DEPLOYMENT_READY", "deployment_state_message": "" }, "workload_size": "Small", "workload_type": "CPU" } ], "traffic_config": { "routes": [ { "served_model_name": "feature-spec-travel", "traffic_percentage": 100 } ] } }, "creation_timestamp": 1700089637000, "creator": "user@email.com", "id": "e3bd3e471d6045d6b75f384279e4b6ab", "last_updated_timestamp": 1700089760000, "name": "feature-spec-endpoint", "permission_level": "CAN_MANAGE", "route_optimized": false, "state": { "config_update": "NOT_UPDATING", "ready": "READY" }, "tags": [ { "key": "team", "value": "data science" } ] } ``` ### Models in Unity Catalog endpoint ```json { "config": { "config_version": 1, "served_entities": [ { "creation_timestamp": 1700089760000, "creator": "user@email.com", "entity_name": "myCatalog.mySchema.my-ads-model", "entity_version": "10", "name": "my-ads-model-10", "scale_to_zero_enabled": true, "state": { "deployment": "DEPLOYMENT_READY", "deployment_state_message": "" }, "workload_size": "Small", "workload_type": "CPU" } ], "traffic_config": { "routes": [ { "served_model_name": "my-ads-model-10", "traffic_percentage": 100 } ] } }, "creation_timestamp": 1700089637000, "creator": "user@email.com", "id": "e3bd3e471d6045d6b75f384279e4b6ab", "last_updated_timestamp": 1700089760000, "name": "uc-model-endpoint", "permission_level": "CAN_MANAGE", "route_optimized": false, "state": { "config_update": "NOT_UPDATING", "ready": "READY" }, "tags": [ { "key": "team", "value": "data science" } ] } ``` ### Workspace model endpoint ```json { "config": { "config_version": 1, "served_entities": [ { "creation_timestamp": 1700089760000, "creator": "user@email.com", "entity_name": "my-ads-model", "entity_version": "3", "name": "my-ads-model-3", "scale_to_zero_enabled": true, "state": { "deployment": "DEPLOYMENT_READY", "deployment_state_message": "" }, "workload_size": "Small", "workload_type": "CPU" }, { "creation_timestamp": 1700089760000, "creator": "user@email.com", "entity_name": "my-ads-model", "entity_version": "4", "name": "my-ads-model-4", "scale_to_zero_enabled": true, "state": { "deployment": "DEPLOYMENT_READY", "deployment_state_message": "" }, "workload_size": "Small", "workload_type": "CPU" } ], "traffic_config": { "routes": [ { "served_model_name": "my-ads-model-3", "traffic_percentage": 80 }, { "served_model_name": "my-ads-model-4", "traffic_percentage": 20 } ] } }, "creation_timestamp": 1700089637000, "creator": "user@email.com", "id": "e3bd3e471d6045d6b75f384279e4b6ab", "last_updated_timestamp": 1700089760000, "name": "workspace-model-endpoint", "permission_level": "CAN_MANAGE", "route_optimized": false, "state": { "config_update": "NOT_UPDATING", "ready": "READY" }, "tags": [ { "key": "team", "value": "data science" } ] } ```