Vertex AI — Gemini

Vertex AI is Google Cloud’s enterprise inference platform. Use this vendor (rather than google) when your organization needs GCP IAM, regional data residency, pre-paid commitments, or Vertex-specific features. The Gemini model catalog largely overlaps with AI Studio, but the auth path and quotas differ.

Get credentials

You need a service account key from a GCP project that has the Vertex AI API enabled.

Sign in to the Google Cloud Console and select (or create) the project that will run inference.
Enable the Vertex AI API at console.cloud.google.com/apis/library/aiplatform.googleapis.com.
Create a service account: IAM & Admin → Service Accounts → + Create service account. Give it a name like jambonz-vertex.
Grant the role Vertex AI User (roles/aiplatform.user). This is the minimum required role; broader roles (Editor, Owner) work too but are over-privileged.
Open the new service account, go to the Keys tab → Add key → Create new key → JSON. Download the JSON file.

Treat the downloaded JSON like a password. Anyone with the file can mint tokens against your Vertex quota until the key is rotated.

Configure in jambonz

In the portal: Account → LLM Services → + Add LLM Service → Vertex AI — Gemini.

Service Account Key (JSON)

fileRequired

Upload the JSON key file you downloaded.

Project ID

stringRequired

Your GCP project id (visible in the Cloud Console — e.g. my-company-12345). Often also embedded in the service account JSON, but the form requires it explicitly.

Region

selectRequired

GCP region where you want inference to happen. us-central1 is the most common default. Pick closer regions for lower latency or regulated regions for compliance.

Click Test. The probe mints a Google access token from the service account — green means the key is valid and the service account has the right roles.

Use in an agent verb

1 session.agent({
2   llm: {
3     vendor: 'vertex-gemini',
4     model: 'gemini-2.5-flash',
5     llmOptions: {
6       systemPrompt: 'You are a helpful voice assistant.',
7     },
8   },
9   stt: { vendor: 'deepgram', language: 'en-US' },
10   tts: { vendor: 'cartesia', voice: 'sonic-english' },
11   turnDetection: 'krisp',
12   bargeIn: { enable: true },
13   actionHook: '/agent-complete',
14 }).send();

Available Models

See Google’s Vertex AI generative model list for the full Gemini lineup and per-region availability. The model ids match AI Studio (e.g. gemini-2.5-flash, gemini-2.5-pro) — Vertex deploys the same models with enterprise infrastructure underneath.

Quirks & errors

Form field naming: the jambonz form uses Region, but Google’s docs and the encrypted credential blob use location. Same field — different name.

PERMISSION_DENIED during Test — the service account is missing the roles/aiplatform.user IAM role. Add it under IAM & Admin → IAM → find the service account → edit roles.

NOT_FOUND on a model — the model isn’t available in the chosen region. Gemini availability differs by region; check the Vertex model availability matrix and either switch regions or pick a model present in yours.

Test passes but inference fails? The Test probe only verifies the credential mints a token. Specific model access is a separate check — if you’ve never called a particular model from this project, your first inference call may fail with NOT_FOUND or quota errors. Hit the Vertex AI model garden and “Open in playground” once for each model you plan to use.