Vertex AI is Google Cloud’s platform for building and running AI, and it is the enterprise home of Google’s Gemini models. Its standout strengths are multimodal models that work across text, images, audio, video and very long documents; Google-quality search; and tight integration with BigQuery.
What you get with Vertex AI
- Gemini models in several sizes, from fast, low-cost models to the most capable reasoning models.
- Model Garden — Google, open-source and partner models (including Anthropic Claude and Meta Llama) in one place.
- Vertex AI Search — managed enterprise search and RAG over websites, documents and structured data.
- Agent tooling — Vertex AI Agent Builder, the open-source Agent Development Kit (ADK) and support for open protocols for connecting agents and tools.
- MLOps — training, tuning, evaluation, pipelines and monitoring for both generative and traditional ML.
Where Gemini’s multimodality pays off
- Retail: generating and checking product attributes from images and supplier specs.
- Media: describing, tagging and searching video and image archives.
- Insurance: reviewing photos and documents submitted with claims.
- Contact centres: summarising recorded calls and spotting trends.
- Legal and finance: comparing long contracts and reports in a single pass.
Grounding: your data and Google Search
Gemini responses can be grounded in your own data through Vertex AI Search or a custom RAG pipeline, and — where appropriate — in Google Search results for up-to-date public information. Grounding with citations is the most effective way to reduce hallucinations and build user trust.
AI where your data lives: BigQuery
For data-driven organisations, using Gemini alongside BigQuery is a major advantage. Teams can call models from SQL to classify, summarise or extract information from large tables, generate insights in natural language and build forecasting models without moving data out of the warehouse.
Security and governance
- VPC Service Controls to create a security perimeter around AI and data services.
- IAM roles, customer-managed encryption keys (CMEK) and regional processing options.
- Google states that customer data used with Vertex AI is not used to train its models without permission.
- Evaluation services and safety filters for quality and responsible-AI checks.
Managing cost
Choose the smallest Gemini model that meets quality targets, use context caching for repeated large inputs, batch non-urgent jobs and monitor usage by project. Long-context and multimodal inputs are powerful but can be expensive — reserve them for the tasks that genuinely need them.
Our Google Vertex AI team designs and builds Gemini solutions on Google Cloud.
Frequently asked questions
What is Vertex AI?
Vertex AI is Google Cloud's managed platform for building, deploying and operating AI, including access to Gemini and other models, enterprise search, agent tooling and MLOps.
What makes Gemini different?
Gemini models are natively multimodal — they work across text, images, audio, video and very long documents — which suits use cases like media search, image-based product data and long-document analysis.
Can I use non-Google models on Vertex AI?
Yes. Vertex AI Model Garden includes open-source and partner models, such as Meta Llama and Anthropic Claude, alongside Google's models.