Vertex AI

Vertex AI is Google Cloud’s managed platform for building, training, deploying, and monitoring machine learning models and AI applications. It combines data science, data engineering, machine learning engineering, generative AI, and MLOps capabilities within Google Cloud.

The platform supports AutoML workflows and custom training with user-supplied code and frameworks. Its Model Garden provides access to Google models, selected open models, and partner models. Vertex AI also supports Gemini-based generative AI applications, multimodal processing, model tuning, evaluation, and production deployment.

Google announced Vertex AI general availability on May 18, 2021. On April 22, 2026, Google announced Gemini Enterprise Agent Platform as the product’s evolution. Google stated that future Vertex AI services and roadmap updates would be delivered through that platform.

Vertex AI Studio provides tools for prompt design, testing, and generative AI application prototyping. Developers can work with text, images, video, audio, and code inputs, depending on the selected model.

AutoML supports training for selected tabular, image, and video workloads without requiring users to write model-training code. Custom training supports preferred frameworks, custom code, hyperparameter tuning, and prebuilt or custom containers.

Model Registry stores model versions and supports evaluation and deployment workflows. Vertex AI endpoints provide online inference, while batch prediction supports asynchronous processing without a continuously deployed endpoint.

Vertex AI Pipelines orchestrates repeatable machine learning workflows. Feature Store supports feature serving and monitoring. Model Monitoring identifies training-serving skew and inference drift. Explainable AI provides feature-attribution and example-based explanations for supported models.

Model Garden allows users to discover, test, customize, and deploy Google, open-source, and partner models. The platform also includes APIs, SDKs, notebooks, managed infrastructure, and integrations with Google Cloud data and application services.

Vertex AI uses consumption-based pricing rather than a single universal subscription plan. Charges vary by service, model, region, compute resource, storage, training duration, endpoint configuration, input volume, and output volume.

Generative AI models are generally billed by tokens or other model-specific units. Training, prediction, pipelines, notebooks, storage, networking, and infrastructure can create separate charges. Google publishes service-specific rates on its official pricing pages.

New Google Cloud customers can receive $300 in welcome credit for 90 days. The credit can be used with eligible Google Cloud products, subject to the Free Trial terms and product restrictions.

Pros

• Supports the full machine learning lifecycle.
• Combines generative AI, traditional ML, and MLOps tools.
• Connects directly with BigQuery, Cloud Storage, and other Google Cloud services.
• Offers managed online endpoints and batch prediction.
• Provides access to Google, open, and partner models through Model Garden.
• Exposes console, command-line, SDK, REST, and gRPC interfaces.

Cons

• Pricing can be difficult to forecast across multiple services.
• Production use requires a Google Cloud project and billing configuration.
• Many capabilities depend on region, model, and service availability.
• Teams may face migration effort when moving workloads to another cloud.
• The platform’s branding and product structure changed during its 2026 evolution.

Comparable platforms include Amazon SageMaker for managed machine learning development and deployment, Amazon Bedrock for managed foundation-model access, and Microsoft Azure Machine Learning for enterprise model operations.

Databricks Mosaic AI is another alternative for organizations centered on lakehouse data and machine learning workflows. IBM watsonx.ai provides model development, governance, and generative AI tooling for enterprise environments.

What is Vertex AI?
Vertex AI is Google Cloud’s managed platform for developing, training, deploying, and monitoring machine learning models and AI applications.

Does Vertex AI provide an API?
Yes. Vertex AI exposes REST and gRPC services through the aiplatform.googleapis.com service. Google also provides SDKs and client libraries for supported programming languages.

What does Vertex AI integrate with?
Documented integrations include BigQuery, Cloud Storage, Dataproc, Cloud Run, Google Kubernetes Engine, Colab Enterprise, and BigQuery ML.

Is Vertex AI free?
Vertex AI is primarily usage-based. New Google Cloud customers may use a $300 credit for 90 days, subject to Google Cloud Free Trial conditions.

Does Vertex AI offer a free trial?
Google Cloud offers a 90-day Free Trial with $300 in welcome credit for eligible new customers. The offer is not a separate unlimited Vertex AI subscription.

What models are available?
Model availability changes over time. Vertex AI Model Garden includes Google models such as Gemini and selected open-source and partner models.