Error: "Invalid model name" in Databricks AI Gateway when setting up Vertex AI endpoint

martkev — Fri, 10 Apr 2026 13:40:24 GMT

Hi everyone,

I'm trying to set up a new serving endpoint in Databricks using Google Cloud Vertex AI as the provider. I want to route it to claude-opus-4-6.

However, as soon as I try to create it, I get the following UI error (see screenshot):

"Invalid model name: claude-opus-4-6. Please refer to docs for supported models for VertexAI."

Has anyone successfully deployed this specific model via Databricks Model Serving? Do I need to use a specific versioned string (like @2026...), or is this model simply not yet supported by the Databricks AI Gateway / Serving allowlist? Could you add it to the allowed list? 😉

Any hints on the correct naming convention would be greatly appreciated! Thanks!

Re: Error: "Invalid model name" in Databricks AI Gateway when setting up Vertex AI endpoin

anuj_lathi — Sat, 11 Apr 2026 03:44:44 GMT

Hi @martkev -- good news: your model name is correct. The Vertex AI model ID for Claude Opus 4.6 is indeed claude-opus-4-6 (per Anthropic's official documentation). The issue is on the Databricks side -- the UI enforces an internal allowlist of recognized model names for each external provider, and that allowlist can lag behind what the providers actually support.

Here are some approaches to work around this.

———

Understanding the Problem

When you create an external model endpoint in Databricks, the UI validates the model name against a hardcoded list of known model IDs for each provider. For the google-cloud-vertex-ai provider, the allowlist may not yet include the latest Claude models. The model name itself is valid on Vertex AI -- Databricks just doesn't know about it yet.

For reference, here are the current Vertex AI model IDs from Anthropic's documentation:

Model	Vertex AI Model ID
Claude Opus 4.6	claude-opus-4-6
Claude Sonnet 4.6	claude-sonnet-4-6
Claude Sonnet 4.5	claude-sonnet-4-5@20250929
Claude Opus 4.5	claude-opus-4-5@20251101
Claude Opus 4.1	claude-opus-4-1@20250805
Claude Sonnet 4	claude-sonnet-4@20250514
Claude Opus 4	claude-opus-4@20250514
Claude Haiku 4.5	claude-haiku-4-5@20251001

———

Workaround 1: Create the Endpoint via REST API (Bypasses UI Validation)

The UI validation is client-side. You can often bypass it by creating the endpoint directly through the Databricks REST API or the MLflow Deployments SDK, which may not enforce the same allowlist:

import mlflow.deployments

client = mlflow.deployments.get_deploy_client("databricks")

endpoint = client.create_endpoint(

name="claude-opus-vertex",

config={

"served_entities": [

{

"external_model": {

"name": "claude-opus-4-6",

"provider": "google-cloud-vertex-ai",

"task": "llm/v1/chat",

"google_cloud_vertex_ai_config": {

"private_key": "{{secrets/your-scope/vertex-private-key}}",

"region": "us-east1",

"project_id": "your-gcp-project-id",

}

)

Or via curl:

curl -X POST "https://<your-databricks-host>/api/2.0/serving-endpoints" \

-H "Authorization: Bearer <your-token>" \

-H "Content-Type: application/json" \

-d '{

"name": "claude-opus-vertex",

"config": {

"served_entities": [{

"external_model": {

"name": "claude-opus-4-6",

"provider": "google-cloud-vertex-ai",

"task": "llm/v1/chat",

"google_cloud_vertex_ai_config": {

"private_key": "{{secrets/your-scope/vertex-private-key}}",

"region": "us-east1",

"project_id": "your-gcp-project-id"

}

}]

}

Note: If the API also rejects the model name, move on to Workaround 2.

———

Workaround 2: Use the "anthropic" Provider Directly (Instead of Vertex AI)

If your primary goal is to use Claude Opus 4.6 through Databricks AI Gateway and you have an Anthropic API key, you can skip Vertex AI entirely and use the anthropic provider directly:

import mlflow.deployments

client = mlflow.deployments.get_deploy_client("databricks")

endpoint = client.create_endpoint(

name="claude-opus-direct",

config={

"served_entities": [

{

"external_model": {

"name": "claude-opus-4-6",

"provider": "anthropic",

"task": "llm/v1/chat",

"anthropic_config": {

"anthropic_api_key": "{{secrets/your-scope/anthropic-key}}",

}

)

The anthropic provider allowlist tends to be updated faster than the Vertex AI one, since the model names come directly from Anthropic. This also gives you access to the global endpoint with no region restrictions.

———

Workaround 3: Use the "custom" Provider with Vertex AI's OpenAI-Compatible Endpoint

Databricks supports a custom provider type that can point to any OpenAI-compatible API. Since Vertex AI now offers an OpenAI-compatible endpoint for Claude models, you can use this to bypass the allowlist entirely:

import mlflow.deployments

client = mlflow.deployments.get_deploy_client("databricks")

endpoint = client.create_endpoint(

name="claude-opus-custom-vertex",

config={

"served_entities": [

{

"external_model": {

"name": "claude-opus-4-6",

"provider": "custom",

"task": "llm/v1/chat",

"custom_provider_config": {

"url": "https://us-east1-aiplatform.googleapis.com/v1/projects/YOUR_PROJECT/locations/us-east1/publishers/anthropic/models/claude-opus-4-6:streamRawPredict",

"bearer_token_auth": "{{secrets/your-scope/gcp-access-token}}",

}

)

Caveat: The custom provider approach requires managing the GCP access token yourself (OAuth tokens expire), so this is more of a temporary workaround than a production solution.

———

Workaround 4: Try a Versioned Model String

Some Databricks allowlists recognize versioned model strings that include the @date suffix. Try these variations in the UI:

claude-opus-4-6 (what you already tried)
claude-opus-4-6@20250205
claude-opus-4-6@latest

The @date suffix is how Vertex AI pins model versions internally. For example, claude-sonnet-4-5@20250929 is the pinned version of Claude Sonnet 4.5. However, Claude Opus 4.6 and Sonnet 4.6 appear to use the bare name (without @date) in Vertex AI itself.

———

The Real Fix: File a Support Ticket

Since the model name is valid on Vertex AI but rejected by Databricks, this is an allowlist gap that Databricks needs to fix on their side. I would recommend:

Filing a Databricks support ticket requesting that claude-opus-4-6 and claude-sonnet-4-6 be added to the google-cloud-vertex-ai provider's model allowlist.
Reference this Anthropic documentation for the official model IDs: https://platform.claude.com/docs/en/build-with-claude/claude-on-vertex-ai

Allowlist updates are typically released in platform patches and don't require a full version upgrade.

———

Summary

Approach	Effort	Production-Ready
REST API / SDK (bypass UI)	Low	Yes, if API accepts it
anthropic provider (skip Vertex)	Low	Yes
custom provider	Medium	No (token management)
Versioned model string	Low	Worth a try
Support ticket (permanent fix)	Low	Yes (once resolved)

Bottom line: Your model name is correct. This is a Databricks allowlist lag issue. Use the REST API or the direct anthropic provider as a workaround while waiting for the allowlist to be updated.

Hope this helps!

———

topic Re: Error: "Invalid model name" in Databricks AI Gateway when setting up Vertex AI endpoin in Generative AI

Error: "Invalid model name" in Databricks AI Gateway when setting up Vertex AI endpoint

Re: Error: "Invalid model name" in Databricks AI Gateway when setting up Vertex AI endpoin

Understanding the Problem

Workaround 1: Create the Endpoint via REST API (Bypasses UI Validation)

Workaround 2: Use the "anthropic" Provider Directly (Instead of Vertex AI)

Workaround 3: Use the "custom" Provider with Vertex AI's OpenAI-Compatible Endpoint

Workaround 4: Try a Versioned Model String

The Real Fix: File a Support Ticket

Summary

References