Google Vertex AI is Google Cloud’s unified machine learning platform. It provides access to Google’s foundation models (Gemini), AutoML for custom model training without code, managed training infrastructure for custom models, and deployment tooling for serving predictions at scale. For enterprise AI projects, Vertex AI is the GCP counterpart to the AWS combination of Bedrock (for foundation models) and SageMaker (for custom ML).

Official documentation: https://cloud.google.com/vertex-ai/docs

Foundation Models (Gemini)

Vertex AI provides access to Google’s Gemini family of models through the Generative AI API.

Gemini 1.5 Pro - Google’s most capable model with a context window up to 2 million tokens. The extended context window is its distinguishing feature: it can process entire codebases, long documents, hours of audio, or video content in a single request. Strong at reasoning, code generation, and multi-modal understanding.

Gemini 1.5 Flash - A faster, cheaper model optimized for high-volume tasks. Competitive with GPT-4o-mini for routine tasks at lower latency.

Gemini embedding models - Text embedding models for semantic search and RAG. Available in multiple dimensions to trade off between accuracy and storage cost.

Model Garden

The Model Garden is a catalog of first-party (Google), third-party (Anthropic Claude, Meta Llama, Mistral), and open-source models available on Vertex AI. You can deploy models from the garden to managed endpoints with a few clicks. This multi-model approach is similar to Bedrock’s model marketplace, allowing teams to evaluate and switch models without changing infrastructure.

AutoML

AutoML trains custom models without writing ML code. You upload a labeled dataset, select the task type (classification, regression, object detection, text classification, entity extraction, translation), and AutoML handles feature engineering, architecture search, hyperparameter tuning, and model selection automatically.

AutoML is strongest for tabular data classification and regression tasks where the dataset is well-structured and labeled. It is also competitive for image classification and object detection when you have hundreds to thousands of labeled images. For text tasks, foundation models with few-shot prompting often outperform AutoML with less data preparation effort.

Custom Training

For ML teams that need full control, Vertex AI provides managed training with custom containers. You package your training code (PyTorch, TensorFlow, JAX, or any framework) in a Docker container, specify hardware requirements (CPU, GPU type and count, TPU), and Vertex AI handles provisioning, execution, and artifact storage.

Vertex AI Training supports distributed training across multiple GPUs and TPUs, hyperparameter tuning with Bayesian optimization, and experiment tracking with Vertex AI Experiments. TPU access is a unique advantage of GCP for teams training large models from scratch.

Vertex AI Pipelines

Pipelines orchestrate ML workflows as directed acyclic graphs (DAGs). Built on Kubeflow Pipelines, they support custom components written in Python, pre-built components for common tasks (data processing, training, evaluation, deployment), and caching of intermediate artifacts to avoid recomputation.

Vertex AI Search and Conversation

Vertex AI Search provides managed RAG infrastructure similar to Bedrock Knowledge Bases. You ingest documents, and the service handles chunking, embedding, indexing, and retrieval. Vertex AI Conversation builds on this to create conversational agents with grounded responses, comparable to Bedrock Agents.

Comparison with AWS

Vertex AI combines capabilities that AWS splits across Bedrock and SageMaker. The Gemini models compete directly with Anthropic Claude and GPT-4 models available on Bedrock. AutoML competes with SageMaker Autopilot. Custom training competes with SageMaker Training. For multi-cloud organizations, Vertex AI is the GCP pillar of the ML strategy. For AWS-primary organizations, the relevance is primarily competitive awareness and understanding client environments that use GCP.

Pricing

Vertex AI pricing varies by service. Gemini API calls charge per input/output token. AutoML charges per training hour and per prediction. Custom training charges per compute hour based on the machine type and accelerators selected. Prediction endpoints charge per node-hour. The pricing structure is comparable to AWS equivalents, with TPU access being uniquely cost-effective for large-scale training on GCP.