Create and manage external model providers (model provider services)

Register an external model provider as a model provider service, grant access to it, configure Unity AI Gateway features, and delete it.

Requirements

CREATE SERVICE on the schema where you create the model provider service, plus USE CATALOG and USE SCHEMA on its catalog and schema.
The credentials for the external provider you want to register (for example, an OpenAI API key or an AWS access key pair).
To authenticate Azure OpenAI or Microsoft Foundry with a service credential instead of a key or secret, you need an existing service credential and ACCESS on it. See Authenticate Azure OpenAI or Microsoft Foundry with a service credential.

Create a model provider service

Model provider services and model services share a single name namespace within a Unity Catalog schema. You can't use a name for a model provider service if a model service in the schema already uses it, and vice versa.

You can create a model provider service in the Unity AI Gateway UI or in Catalog Explorer, or programmatically with the REST API, the Azure Databricks SDKs, the Azure Databricks CLI, or Terraform.

UI

Do one of the following:
- In the workspace sidebar, click AI Gateway, then open the Providers tab and click Provider.
- In Catalog Explorer, go to the schema where you want to create the model provider service, click Create > Service, then select Model provider service in the Create a service dialog.
Enter a name for the model provider service, and select the catalog and schema to create it in. If you start from Catalog Explorer, Catalog Explorer prefills the catalog and schema.
Select the provider type, and enter the provider's connection details and credentials.
Click Create. Azure Databricks encrypts and stores the credentials. The UI does not display them after this point.

REST API

Send a POST to /api/2.1/unity-catalog/model-provider-services, passing parent and model_provider_service_id as query parameters. Set provider_type and exactly one matching provider block; targets allowlists the reachable upstream models, and secrets are supplied inline as plaintext:

databricks api post \
  "/api/2.1/unity-catalog/model-provider-services?parent=schemas/main.default&model_provider_service_id=my_provider" \
  --json '{
  "comment": "Routes to a custom OpenAI-compatible provider",
  "config": {
    "provider_type": "EXTERNAL_MODEL_PROVIDER_TYPE_CUSTOM",
    "targets": [
      { "model": "gpt-4o", "native_api_types": ["openai/v1/chat/completions"] }
    ],
    "custom": {
      "direct": {
        "base_url": "https://api.example.com/v1",
        "api_key": { "plaintext": "dummy-api-key" }
      }
    }
  }
}'

CLI

Pass the parent schema and a leaf name, and supply the config with --json. Set provider_type and exactly one matching provider block; targets allowlists the reachable upstream models, and secrets are supplied inline as plaintext. To install the CLI, see Install or update the Databricks CLI.

databricks ai-gateway create-model-provider-service schemas/main.default my_provider --json '{
  "comment": "Routes to a custom OpenAI-compatible provider",
  "config": {
    "provider_type": "EXTERNAL_MODEL_PROVIDER_TYPE_CUSTOM",
    "targets": [
      { "model": "gpt-4o", "native_api_types": ["openai/v1/chat/completions"] }
    ],
    "custom": {
      "direct": {
        "base_url": "https://api.example.com/v1",
        "api_key": { "plaintext": "dummy-api-key" }
      }
    }
  }
}'

Terraform

Create and manage a model provider service with the Databricks Terraform provider and the databricks_ai_gateway_model_provider_service resource. Keep real keys out of source control by passing the API key through a sensitive = true variable (set it with -var or a TF_VAR_provider_api_key environment variable):

variable "provider_api_key" {
  type      = string
  sensitive = true
}

resource "databricks_ai_gateway_model_provider_service" "example" {
  parent                    = "schemas/main.default"
  model_provider_service_id = "my_provider"
  comment                   = "Routes to a custom OpenAI-compatible provider"

  config = {
    provider_type = "EXTERNAL_MODEL_PROVIDER_TYPE_CUSTOM"

    targets = [{
      model            = "gpt-4o"
      native_api_types = ["openai/v1/chat/completions"]
    }]

    custom = {
      direct = {
        base_url = "https://api.example.com/v1"
        api_key  = { plaintext = var.provider_api_key }
      }
    }
  }
}

Python SDK

Create and manage a model provider service with the Databricks SDK for Python:

from databricks.sdk.service import catalog as c

model_provider_service = w.ai_gateway.create_model_provider_service(
    parent="schemas/main.default",
    model_provider_service_id="my_provider",
    model_provider_service=c.ModelProviderService(
        comment="Routes to a custom OpenAI-compatible provider",
        config=c.ModelProviderServiceConfig(
            provider_type=(
                c.ModelProviderServiceConfigExternalModelProviderType
                .EXTERNAL_MODEL_PROVIDER_TYPE_CUSTOM
            ),
            targets=[
                c.ModelProviderServiceConfigModelTargetConfig(
                    model="gpt-4o",
                    native_api_types=["openai/v1/chat/completions"],
                )
            ],
            custom=c.ModelProviderServiceConfigCustomProviderConfig(
                direct=c.ModelProviderServiceConfigCustomProviderDirectConfig(
                    base_url="https://api.example.com/v1",
                    api_key=c.ModelProviderServiceConfigProviderSecret(
                        plaintext="dummy-api-key"
                    ),
                )
            ),
        ),
    ),
)

Go SDK

Create and manage a model provider service with the Databricks SDK for Go:

modelProviderService, err := w.AiGateway.CreateModelProviderService(ctx,
	catalog.CreateModelProviderServiceRequest{
		Parent:                 "schemas/main.default",
		ModelProviderServiceId: "my_provider",
		ModelProviderService: catalog.ModelProviderService{
			Comment: "Routes to a custom OpenAI-compatible provider",
			Config: &catalog.ModelProviderServiceConfig{
				ProviderType: catalog.ModelProviderServiceConfigExternalModelProviderTypeExternalModelProviderTypeCustom,
				Targets: []catalog.ModelProviderServiceConfigModelTargetConfig{{
					Model:          "gpt-4o",
					NativeApiTypes: []string{"openai/v1/chat/completions"},
				}},
				Custom: &catalog.ModelProviderServiceConfigCustomProviderConfig{
					Direct: &catalog.ModelProviderServiceConfigCustomProviderDirectConfig{
						BaseUrl: "https://api.example.com/v1",
						ApiKey: &catalog.ModelProviderServiceConfigProviderSecret{
							Plaintext: "dummy-api-key",
						},
					},
				},
			},
		},
	})

Java SDK

Create and manage a model provider service with the Databricks SDK for Java:

ModelProviderServiceConfig config =
    new ModelProviderServiceConfig()
        .setProviderType(
            ModelProviderServiceConfigExternalModelProviderType
                .EXTERNAL_MODEL_PROVIDER_TYPE_CUSTOM)
        .setTargets(
            Collections.singletonList(
                new ModelProviderServiceConfigModelTargetConfig()
                    .setModel("gpt-4o")
                    .setNativeApiTypes(
                        Collections.singletonList("openai/v1/chat/completions"))))
        .setCustom(
            new ModelProviderServiceConfigCustomProviderConfig()
                .setDirect(
                    new ModelProviderServiceConfigCustomProviderDirectConfig()
                        .setBaseUrl("https://api.example.com/v1")
                        .setApiKey(
                            new ModelProviderServiceConfigProviderSecret()
                                .setPlaintext("dummy-api-key"))));

ModelProviderService modelProviderService =
    w.aiGateway()
        .createModelProviderService(
            new CreateModelProviderServiceRequest()
                .setParent("schemas/main.default")
                .setModelProviderServiceId("my_provider")
                .setModelProviderService(
                    new ModelProviderService()
                        .setComment("Routes to a custom OpenAI-compatible provider")
                        .setConfig(config)));

JS SDK

Create and manage a model provider service with the Databricks AI Gateway SDK for JavaScript:

import { ModelProviderServiceConfig_ExternalModelProviderType as ProviderType } from '@databricks/sdk-aigateway/v1';

const created = await client.createModelProviderService({
  parent: 'schemas/main.default',
  modelProviderServiceId: 'my_provider',
  modelProviderService: {
    comment: 'Routes to a custom OpenAI-compatible provider',
    config: {
      providerType: ProviderType.EXTERNAL_MODEL_PROVIDER_TYPE_CUSTOM,
      targets: [{ model: 'gpt-4o', nativeApiTypes: ['openai/v1/chat/completions'] }],
      provider: {
        $case: 'custom',
        custom: {
          providerMode: {
            $case: 'direct',
            direct: {
              baseUrl: 'https://api.example.com/v1',
              authMode: {
                $case: 'apiKey',
                apiKey: {
                  value: { $case: 'plaintext', plaintext: 'dummy-api-key' },
                },
              },
            },
          },
        },
      },
    },
  },
});

For the full list of providers and their authentication methods, see Govern external model providers (model provider services).

Authenticate Azure OpenAI or Microsoft Foundry with a service credential

You can authenticate an Azure OpenAI or Microsoft Foundry provider with a service credential instead of storing an API key or a Microsoft Entra ID service principal client secret. A service credential holds an Azure identity that Unity Catalog governs, so no long-lived secret is copied into the model provider service: Azure Databricks obtains short-lived tokens from that identity to authenticate each request.

Create the model provider service as described in Create a model provider service. Select Azure OpenAI or Microsoft Foundry as the provider type and enter its connection details, including the endpoint base URL. Then set Auth method to Service credential and select the credential instead of entering an API key or client secret. A service credential replaces only the secret, so the endpoint base URL is still required.

Confirm the following requirements:

The owner of the model provider service has ACCESS on the service credential. Because Azure Databricks re-checks the owner's access when serving requests, the owner must keep it for as long as the provider is in use. Revoking it stops queries for everyone, even callers who hold EXECUTE on the provider. To grant the owner access to the credential:
```
GRANT ACCESS ON SERVICE CREDENTIAL <service-credential-name> TO `<model-provider-service-owner>`;
```
The credential's purpose is service, not storage.
The credential is available in the workspaces requests come from. Its workspace bindings still apply, so a request from a workspace the credential isn't bound to fails there, even though the model provider service itself is reachable from any workspace that shares the metastore.
The service credential's Azure identity is authorized to call the Azure OpenAI or Microsoft Foundry deployments you plan to query. To create a service credential, see Create service credentials.

Callers who query the provider need the same grants as for any other provider. They don't need any privilege on the service credential, which is what keeps the credential itself out of their reach.

The model provider service tracks a credential by its internal identifier, so you can rename a credential without query failure.

If you delete a credential, queries fail and there is no warning that a model provider service references it. Confirm that there are no references to this credential before you delete it.

You can't switch an existing model provider service between service credential and API key or client secret authentication. Create a new model provider service instead.

Send a custom provider API key in a header

A custom provider sends its API key as a bearer token by default. When your endpoint expects the key in a specific header instead, use API key header authentication and name the header yourself. Azure Databricks then sends the key on each outbound request as <header name>: <header value>.

Create the model provider service as described in Create a model provider service. Select Custom as the provider type, then set Auth method to API key header and supply the Header name your endpoint expects (such as X-API-Key or Ocp-Apim-Subscription-Key) along with the Header value.

The two methods are mutually exclusive: a custom provider uses either a bearer token or a named header, not both. Header authentication takes exactly one header.

The header name must be a valid HTTP header name: letters, digits, and the characters !#$%&'*+-.^_`|~, up to 255 characters. Any other character is rejected, including spaces, colons, slashes, and line breaks.

Grant access to a model provider service

By default, only the model provider service owner can query it. To let others query a model provider service, grant them EXECUTE on it, plus USE CATALOG and USE SCHEMA on its catalog and schema. If the model provider service logs to an inference table, grant SELECT on the table to let them read the logged requests and responses.

UI

Open the model provider service in Catalog Explorer, or go to AI Gateway and select the service.
Go to the Permissions tab.
Click Grant.
Select the users, groups, or service principals to give access to.
Select the EXECUTE privilege.
Click Grant.

REST API

databricks api patch \
  "/api/2.1/unity-catalog/permissions/model_provider_service/main.default.my_provider" \
  --json '{
    "changes": [
      { "principal": "data-team", "add": ["EXECUTE"] }
    ]
  }'

CLI

Grant EXECUTE with the Databricks CLI. To install the CLI, see Install or update the Databricks CLI.

databricks grants update model_provider_service main.default.my_provider \
  --json '{"changes": [{"principal": "data-team", "add": ["EXECUTE"]}]}'

Terraform

Grant EXECUTE with the Databricks Terraform provider and the databricks_grant resource:

resource "databricks_grant" "example" {
  model_provider_service = "main.default.my_provider"
  principal              = "data-team"
  privileges             = ["EXECUTE"]
}

Python SDK

Grant EXECUTE with the Databricks SDK for Python:

from databricks.sdk.service import catalog as c

w.grants.update(
    securable_type="model_provider_service",
    full_name="main.default.my_provider",
    changes=[c.PermissionsChange(principal="data-team", add=[c.Privilege.EXECUTE])],
)

Go SDK

Grant EXECUTE with the Databricks SDK for Go:

_, err := w.Grants.Update(ctx, catalog.UpdatePermissions{
	SecurableType: "model_provider_service",
	FullName:      "main.default.my_provider",
	Changes: []catalog.PermissionsChange{{
		Principal: "data-team",
		Add:       []catalog.Privilege{catalog.PrivilegeExecute},
	}},
})

Java SDK

Grant EXECUTE with the Databricks SDK for Java:

w.grants().update(
    new UpdatePermissions()
        .setSecurableType("model_provider_service")
        .setFullName("main.default.my_provider")
        .setChanges(Arrays.asList(
            new PermissionsChange().setPrincipal("data-team").setAdd(Arrays.asList(Privilege.EXECUTE)))));

For more about granting and discovering access, see Discover and govern access to external model providers (model provider services).

Configure features

Because a model provider service routes through Unity AI Gateway, apply the same governance and observability features you use for other Unity AI Gateway traffic:

Inference logging. Log requests and responses to a Unity Catalog table. See Log requests and responses to inference tables.
Rate limits. Cap queries per minute to manage capacity and cost. See Apply rate limits to model and MCP services.
Service policies. Govern the content of each interaction, such as blocking unsafe content or redacting sensitive data, by attaching a service policy. See Service policies for AI securables and Create and attach a service policy.

Update a model provider service

You must be an owner or have MANAGE. The provider type is immutable.

UI

Edit the model provider service's configuration from the Unity AI Gateway UI or Catalog Explorer. Changes apply in place.

REST API

databricks api patch \
  "/api/2.1/unity-catalog/model-provider-services/main.default.my_provider?update_mask=comment" \
  --json '{"comment": "Updated: routes to a custom provider"}'

CLI

databricks ai-gateway update-model-provider-service model-provider-services/main.default.my_provider comment \
  --json '{"comment": "Updated: routes to a custom provider"}'

Terraform

Edit comment (or any other mutable field) on the databricks_ai_gateway_model_provider_service resource and re-apply. Changes apply in place.

Python SDK

from databricks.sdk.service import catalog as c
from google.protobuf.field_mask_pb2 import FieldMask

updated = w.ai_gateway.update_model_provider_service(
    name="model-provider-services/main.default.my_provider",
    update_mask=FieldMask(paths=["comment"]),
    model_provider_service=c.ModelProviderService(
        comment="Updated: routes to a custom provider"
    ),
)

Go SDK

updated, err := w.AiGateway.UpdateModelProviderService(ctx,
	catalog.UpdateModelProviderServiceRequest{
		Name:       "model-provider-services/main.default.my_provider",
		UpdateMask: *fieldmask.New([]string{"comment"}),
		ModelProviderService: catalog.ModelProviderService{
			Comment: "Updated: routes to a custom provider",
		},
	})

Java SDK

ModelProviderService updated =
    w.aiGateway()
        .updateModelProviderService(
            new UpdateModelProviderServiceRequest()
                .setName("model-provider-services/main.default.my_provider")
                .setUpdateMask(FieldMask.newBuilder().addPaths("comment").build())
                .setModelProviderService(
                    new ModelProviderService()
                        .setComment("Updated: routes to a custom provider")));

JS SDK

import { modelProviderServiceFieldMask } from '@databricks/sdk-aigateway/v1';

const updated = await client.updateModelProviderService({
  modelProviderService: {
    name: 'model-provider-services/main.default.my_provider',
    comment: 'Updated: routes to a custom provider',
  },
  updateMask: modelProviderServiceFieldMask('comment'),
});

Delete a model provider service

You must be an owner or have MANAGE.

UI

Open the model provider service in the Unity AI Gateway UI or Catalog Explorer and select Delete from the kebab menu.

REST API

databricks api delete "/api/2.1/unity-catalog/model-provider-services/main.default.my_provider"

CLI

databricks ai-gateway delete-model-provider-service model-provider-services/main.default.my_provider

Terraform

Run terraform destroy, or remove the resource block and re-apply.

Python SDK

w.ai_gateway.delete_model_provider_service(
    name="model-provider-services/main.default.my_provider"
)

Go SDK

err := w.AiGateway.DeleteModelProviderService(ctx,
	catalog.DeleteModelProviderServiceRequest{
		Name: "model-provider-services/main.default.my_provider",
	})

Java SDK

w.aiGateway()
    .deleteModelProviderService(
        new DeleteModelProviderServiceRequest()
            .setName("model-provider-services/main.default.my_provider"));

JS SDK

await client.deleteModelProviderService({
  name: 'model-provider-services/main.default.my_provider',
});

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Last updated on 2026-09-02

Create and manage external model providers (model provider services)

Requirements

Create a model provider service

UI

REST API

CLI

Terraform

Python SDK

Go SDK

Java SDK

JS SDK

Authenticate Azure OpenAI or Microsoft Foundry with a service credential

Send a custom provider API key in a header

Grant access to a model provider service

UI

REST API

CLI

Terraform

Python SDK

Go SDK

Java SDK

Configure features

Update a model provider service

UI

REST API

CLI

Terraform

Python SDK

Go SDK

Java SDK

JS SDK

Delete a model provider service

UI

REST API

CLI

Terraform

Python SDK

Go SDK

Java SDK

JS SDK

Next steps

Feedback

Additional resources