Vertex AI
Google Cloud's managed, unified platform for building, training, deploying, and scaling ML models and generative AI applications, including Gemini and third-party models.
Description
Vertex AI is Google Cloud's managed AI platform, announced and made generally available on May 18, 2021 at Google I/O. The platform unifies the previous Google Cloud AI Platform and AutoML services under a single UI and API, and covers the full ML lifecycle: data preparation, training, evaluation, deployment, and monitoring.
The platform provides access to Model Garden with 200+ models (Google Gemini, partner models such as Anthropic's Claude, open-source models such as Llama), Vertex AI Studio for prompt design and testing, Vertex AI Agent Builder for building AI agents, Vertex AI Pipelines for ML workflow orchestration, Vertex AI Feature Store, Vertex AI Evaluation, and Vertex AI Vector Search. It supports both AutoML (code-free) and custom training workflows.
Vertex AI is available as a managed cloud service on Google Cloud and through Vertex AI on Distributed Cloud for air-gapped on-premises environments. The platform supports CMEK, VPC Service Controls, IAM-based role-based access control, audit logs, and data residency. Pricing follows a pay-as-you-go model based on training time, tokens for generative models, and prediction hours.
MLOps LifecycleMLOps LifecycleFull model lifecycle: registry, feature store, prompt management, monitoring and human-in-the-loop.
Model Registry
Feature Store
Prompt Management
Monitoring
Human-in-the-Loop
Data & KnowledgeData & Knowledge ManagementData connectors, vector database integration, native vector search and data management (PII, provenance, synthetic data).
ApplicationsAI ApplicationsDomains and use cases this platform is best suited for — from RAG and fine-tuning to scientific research.
SecurityEnterprise SecurityCertifications, access controls and data-protection features essential for corporate deployments and cloud privacy compliance.
Developer EcosystemDeveloper EcosystemDeveloper resources: available SDKs, supported programming languages, and infrastructure features and model-deployment methods.
Pricing & Business ModelPricing & Business ModelBilling models (usage-based, provisioned throughput), resource limits and SLA parameters (uptime, support tiers).
Pricing models
Resource quotas
SLA & Support