Vertex AI
Google Cloud’s enterprise AI platform for accessing Gemini, Imagen, Veo, Gemma, embeddings, and broader MLOps tooling.
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What is Vertex AI?
Vertex AI is Google Cloud's managed platform for building, training, and deploying machine learning models. It provides access to Google's foundation models, including Gemini for text and reasoning tasks, Imagen for image generation, and Veo for video generation. The platform combines model access with MLOps tools, allowing teams to manage the full lifecycle of AI projects from experimentation through production deployment. You can use it via Vertex AI Studio for interactive work, Agent Builder for creating AI agents, or programmatically through APIs. It's designed for organisations that want to use enterprise-grade AI without managing underlying infrastructure.
Key Features
Access to Google's foundation models
Gemini, Imagen, Veo, Gemma, and embeddings models
Vertex AI Studio
Low-code interface for testing and fine-tuning models
Agent Builder
Tools for creating and deploying AI agents with custom instructions
MLOps capabilities
Model training, evaluation, monitoring, and deployment workflows
Integration with Google Cloud ecosystem
Works with BigQuery, Cloud Storage, and other GCP services
Batch and real-time predictions
Support for different inference patterns
Pros & Cons
Advantages
- Direct access to Gemini and other capable Google models without separate vendor relationships
- Full MLOps suite reduces need for separate tools when managing production models
- Free tier available for experimentation and small-scale projects
- Deep integration with other Google Cloud services for data pipelines
Limitations
- Requires Google Cloud account setup and familiarity with GCP environment, which has learning overhead
- Pricing for production use can increase significantly with scale, particularly for higher-tier models
- Limited to Google's model ecosystem; less flexibility if you need specific third-party models
Use Cases
Building customer service chatbots with Gemini and Agent Builder
Generating product images or marketing content with Imagen
Creating ML pipelines that combine model inference with data stored in BigQuery
Fine-tuning foundation models on proprietary company data
Batch processing large volumes of text or images for classification or analysis