Generative AI on Vertex AI lets you build production-ready applications that are powered by state-of-the-art generative AI models hosted on Google's advanced, global infrastructure.
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Enterprise ready Deploy your generative AI applications at scale with enterprise-grade security, data residency, access transparency, and low latency. |
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State-of-the-art features Expand the capabilities of your applications by using the 2,000,000-token context window supported by Gemini 1.5 Pro. |
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Open platform Vertex AI Model Garden provides a library of over 100 models that helps you discover, test, customize, and deploy Google proprietary and select third-party models, including Anthropic's Claude 3.5 Sonnet, Meta Llama 3, Mistral AI Mixtral 8x7B, and AI21 Labs Jamba 1.5. |
Core capabilities
- Text generation
Send chat prompts to a Gemini model and receive streaming or non-streaming responses.
- Multimodal processing
Process multiple types of input media at the same time, such as image, video, audio, and documents.
- Embeddings generation
Generate embeddings to perform tasks such as search, classification, clustering, and outlier detection.
- Model tuning
Adapt models to perform specific tasks with greater precision and accuracy.
- Function calling
Connect models to external APIs to extend the model's capabilities.
- Grounding
Connect models to external data sources to reduce hallucinations in responses.
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- Generative AI Evaluation Service
Evaluate any generative model or application and benchmark the evaluation results.
Vertex AI and Google AI differences
Gemini API in Vertex AI and Google AI both let you incorporate the capabilities of Gemini models into your applications. The platform that's right for you depends on your goals as detailed in the following table.
- Scaled deployments
- Enterprise
- Technical support
- Modality-based pricing
- Indemnity protection
- 100+ models in Model Garden
- Experimentation
- Prototyping
- Ease of use
- Free tier
- Token-based pricing
Build using Vertex AI SDKs
Client libraries make it easier to access Google Cloud APIs from a supported language. Although you can use Google Cloud APIs directly by making requests to the server, client libraries provide simplifications that significantly reduce the amount of code you need to write.
Vertex AI provides Vertex Generative AI SDKs for these languages: Python , Node.js , Java , Go , and C# .
Get started
Try one of these quickstarts to get started with generative AI on Vertex AI.
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Generate text using the Gemini API in Vertex AIUse the SDK to send requests to the Gemini API in Vertex AI.
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Send prompts to Gemini using the Vertex AI Studio Prompt GalleryTest prompts with no setup required.
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Generate an image and verify its watermark using ImagenCreate a watermarked image using Imagen on Vertex AI.
More ways to get started
Here are some notebooks, tutorials, and other examples to help you get started. Vertex AI offers Google Cloud console tutorials and Jupyter notebook tutorials that use the Vertex AI SDK for Python. You can open a notebook tutorial in Colab or download the notebook to your preferred environment.
Get started with Gemini using notebooks
The Gemini model is a groundbreaking multimodal language model developed by Google AI, capable of extracting meaningful insights from a diverse array of data formats, including images, and video. This notebook explores various use cases with multimodal prompts.
 Run in Colab |
 Run in Colab Enterprise |
 Open in Vertex AI Workbench |
 View on GitHub |
Get started with Vertex AI Studio
Use Vertex AI Studio to engineer and manage prompts, get prompt code, and tune models, all in a code-free environment.
Best practices for prompt design
Learn how to design prompts to improve the quality of your responses from the model. This tutorial covers the essentials of prompt engineering, including some best practices.
| Open in Colab |
Open in Colab Enterprise |
Open in Vertex AI Workbench |
View on GitHub |
