Vertex AI

Vertex AI is Google Cloud's unified machine learning platform that brings together AutoML, custom model training, generative AI capabilities, and agent-building tools into a single integrated environment. It enables data scientists and developers to build, deploy, and scale ML models and AI applications using Google's infrastructure, including access to Gemini models and specialized hardware like TPUs.

Key Capabilities

Vertex AI provides AutoML for training high-quality models without code, custom training with support for TensorFlow, PyTorch, and JAX, and Vertex AI Pipelines for orchestrating reproducible ML workflows. The platform includes Feature Store for managing and serving features, Model Registry for versioning and deployment, and an Agent Engine for building production-scale AI agents. Generative AI capabilities include access to Gemini models, prompt management, model tuning, and grounding with Google Search.

Who Should Use Vertex AI

Vertex AI is best suited for teams building on Google Cloud who need a comprehensive ML platform, organizations looking to leverage Google's generative AI models like Gemini, and enterprises requiring scalable inference with both online and batch prediction capabilities. It also appeals to developers building AI agents with the Agent Engine.

Getting Started

New Google Cloud accounts receive $300 in credits valid for 90 days. Create a project in the Google Cloud Console, enable the Vertex AI API, and start with AutoML or Vertex AI Studio to experiment with generative AI models. The platform offers extensive documentation, Colab Enterprise notebooks, and sample code for quick onboarding.

Pricing & Accessibility: Vertex AI uses usage-based pricing that varies by service. Custom training is priced per node-hour, online predictions per request, and generative AI models per million tokens. Gemini 2.5 Pro pricing starts at $1.25 per million input tokens. New accounts get $300 in free credits.

Why Consider Vertex AI: Vertex AI provides direct access to Google's leading Gemini AI models, purpose-built ML infrastructure including TPUs, and a fully managed platform that covers the entire AI lifecycle from experimentation through production deployment and monitoring.