Vertex AI is Google Cloud’s unified platform for machine learning (ML) and artificial intelligence (AI). It’s designed to help data scientists and ML engineers build, deploy, and scale ML models faster and more effectively. Vertex AI integrates various Google Cloud ML services into a single, seamless development environment.

Key Features of Google Vertex AI:

• Unified Platform: Provides a single interface for the entire ML lifecycle, from data preparation and model training to deployment, monitoring, and management.
• Vertex AI Studio: A web-based UI for rapid prototyping and testing of generative AI models, offering access to Google’s foundation models like Gemini and PaLM 2.
• Model Garden: A catalog where you can discover, test, customize, and deploy Vertex AI and select open-source models.
• AutoML: Enables training high-quality models on tabular, image, text, and video data with minimal code and data preparation.
• Custom Training: Offers the flexibility to use your preferred ML frameworks (TensorFlow, PyTorch, scikit-learn) and customize the training process.
• Vertex AI Pipelines: Allows you to orchestrate complex ML workflows in a scalable and repeatable manner.
• Feature Store: A centralized repository for storing, serving, and managing ML features.
• Model Registry: Helps you manage and version your trained models.
• Explainable AI: Provides insights into how your models make predictions, improving transparency and trust.
• AI Platform Extensions: Connects your trained models with real-time data from various sources and enables the creation of AI-powered agents.
• Vertex AI Agent Builder: Simplifies the process of building and deploying enterprise-ready generative AI agents with features for grounding, orchestration, and customization.
• Vertex AI RAG (Retrieval-Augmented Generation) Engine: A managed orchestration service to build Gen AI applications that retrieve information from knowledge bases to improve accuracy and reduce hallucinations.
• Managed Endpoints: Simplifies model deployment for online and batch predictions.
• MLOps Tools: Provides capabilities for monitoring model performance, detecting drift, and ensuring the reliability of deployed models.
• Enterprise-Grade Security and Governance: Offers robust security features to protect your data and models.
• Integration with Google Cloud Services: Seamlessly integrates with other Google Cloud services like BigQuery and Cloud Storage.
• Support for Foundation Models: Offers access to and tools for fine-tuning and deploying Google’s state-of-the-art foundation models, including the Gemini family.

Google Vertex AI Pricing:

Vertex AI’s pricing structure is based on a pay-as-you-go model, meaning you are charged only for the resources you consume. The cost varies depending on several factors, including:

• Compute resources used for training and prediction: Different machine types and accelerators (GPUs, TPUs) have varying hourly rates.
• Usage of managed services: AutoML training and prediction, Vertex AI Pipelines, Feature Store, and other managed components have their own pricing structures.
• The volume of data processed and stored.
• The number of requests made to deployed models.
• Specific foundation models and their usage costs.

Key things to note about Vertex AI pricing:

• Free Tier: Google Cloud offers a free tier that includes some free credits and usage of Vertex AI services, allowing new users to explore the platform.
• Pricing Calculator: Google Cloud provides a pricing calculator to estimate the cost of using Vertex AI based on your specific needs and configurations.
• Committed Use Discounts: For sustained usage, Committed Use Discounts (CUDs) can offer significant cost savings.
• Monitoring Costs: It’s crucial to monitor your usage and set up budget alerts to manage costs effectively.
• Differences with Google AI Studio: While both offer access to Gemini models, Vertex AI is a more comprehensive enterprise-grade platform with additional deployment, scalability, and management features, which can result in different overall costs compared to the more usage-based pricing of Google AI Studio for experimentation.

For the most up-to-date and detailed pricing information, it’s recommended to consult the official Google Cloud Vertex AI pricing page.