Using MedGemma on GCP Vertex AI

Implementation guide · Gemma 3 · Google DeepMind

Serverless

GCP Vertex AI exposes MedGemma through model ID medgemma. Use the setup steps, sourced pricing, capabilities, and official provider links below to validate this route before deployment.

Last refreshed 2026-06-15. Next refresh: weekly.

Quick Start

  1. 1
    Create an account at GCP Vertex AI and generate an API key.
  2. 2
    Use the GCP Vertex AI SDK or REST API to call medgemma — see the documentation for request format.
  3. 3
    You'll be billed Free/1M input, Free/1M output tokens. See full pricing.
API PortalDocumentationPricing

Code Examples

Install
pip install google-cloud-aiplatform
API key
GOOGLE_CLOUD_PROJECT
Model ID
medgemma

For Google-published models use the model name directly, e.g. "gemini-2.0-flash-001". For third-party publishers (Anthropic, Meta, etc.) use the full publisher path, e.g. "publishers/anthropic/models/claude-3-5-sonnet-v2@20241022".

import os
import vertexai
from vertexai.generative_models import GenerativeModel

# Reads GOOGLE_CLOUD_PROJECT from env; authenticates via Application Default Credentials
vertexai.init(project=os.environ["GOOGLE_CLOUD_PROJECT"], location="us-central1")
model = GenerativeModel("medgemma")
response = model.generate_content("Hello")
print(response.text)

Pricing on GCP Vertex AI

TypePrice (per 1M)
Input tokensFree
Output tokensFree

Capabilities

VisionMultimodalJSON / Tool useStructured Outputs

About MedGemma

MedGemma is Google DeepMind's Gemma 3 model with multimodal text and image input. It was released 2024-07-01.

Full model details →

Model Specs

Released2024-07-01
Parameters4B
ArchitectureDecoder Only

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Provider

GCP Vertex AI

Google Cloud Platform (GCP)

Mountain View, California, United States