Vertex AI
Google Cloud's unified machine learning platform for building, deploying, and scaling AI models.
Technical explanation
Vertex AI is Google Cloud's answer to the ML platform question. It combines Google's foundation models (Gemini, PaLM), custom model training, and deployment infrastructure into one service. If you're building AI on Google Cloud, Vertex AI is the central hub for everything from experimentation to production.
The platform offers multiple entry points. You can use pre-trained foundation models through the Generative AI Studio, fine-tune models on your data, or train completely custom models using AutoML or custom training jobs. Vertex AI handles the infrastructure, scaling training across TPUs and GPUs as needed. Deployment options include managed endpoints, batch prediction, and edge deployment.
Google's models are a key draw. Gemini 1.5 Pro offers 1 million token context windows, far exceeding most competitors. The multimodal capabilities handle text, images, video, and audio in unified models. Integration with other Google Cloud services like BigQuery, Cloud Storage, and Pub/Sub makes building pipelines straightforward.
The trade-off is complexity. Vertex AI has a lot of features, and the learning curve is steeper than simpler API services. Pricing can be opaque, with costs spread across compute, storage, and model usage. For teams already on Google Cloud, it's the obvious choice. For others, the ecosystem lock-in might outweigh the benefits. Consider whether you need the full platform or just API access to Gemini.