Vertex AI Multimodal Embeddings on GCP Vertex AI
Vertex AI Multimodal Embeddings · Google DeepMind
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Last refreshed 2026-07-01. Next refresh: weekly.
Why use Vertex AI Multimodal Embeddings on GCP Vertex AI?
GCP Vertex AI offers Vertex AI Multimodal Embeddings with free input token pricing. Vertex AI is Google Cloud's managed AI platform, offering access to Gemini models and hundreds of partner models alongside tools for fine-tuning, grounding, vector search, and end-to-end MLOps pipelines.
Input / 1M
Free
Output / 1M
Free
Cache
Not sourced
Batch
Not sourced
Setup recipe
Python + curlInstall
pip install google-cloud-aiplatformAuth
export GOOGLE_CLOUD_PROJECT=...Call
import os
import vertexai
from vertexai.generative_models import GenerativeModel
vertexai.init(project=os.environ["GOOGLE_CLOUD_PROJECT"], location="us-central1")Model ID
multimodalembeddingRequest example
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("multimodalembedding")
response = model.generate_content("Hello")
print(response.text)Gotchas
- 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".
- The examples expect GOOGLE_CLOUD_PROJECT; rename it only if your application config maps the new variable.
Pricing
| Type | Price (per 1M) |
|---|---|
| Input tokens | Free |
| Output tokens | Free |
Capabilities
VisionMultimodal
About Vertex AI Multimodal Embeddings
Vertex AI Multimodal Embeddings is Google Cloud's foundation embedding model for image, text, and video inputs. It supports cross-modal retrieval and semantic search through the Vertex AI Multimodal Embeddings API.
Model Specs
Released2024-08-01