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Google Vertex AI

by Google Cloud · est. 2021

Google Vertex AI is Google Cloud's end-to-end machine learning and generative AI platform. It covers the complete ML lifecycle — data preparation, model training, evaluation, deployment, and monitoring — while also providing native access to Google's frontier AI models (Gemini 2.0 Flash, Gemini 2.0 Pro) and a Model Garden of 150+ open-source and third-party models. Vertex AI's Agent Builder enables enterprise teams to create AI agents and RAG-powered applications without deep ML expertise. For APAC enterprises on Google Cloud — common in financial services (particularly Singapore and Hong Kong), technology companies, and media — Vertex AI provides a unified infrastructure for both traditional ML and modern generative AI workloads, with GCP-native IAM, monitoring, and compliance features.

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AIMenta verdict
Recommended
5/5

"Google Cloud's end-to-end ML platform with the best Gemini integration for APAC. Covers training, deployment, and agent building — Gemini 2.0, Gemma, and 150+ models via Model Garden. Recommended for APAC enterprises on GCP needing unified ML and GenAI infrastructure."

Features
6
Use cases
4
Watch outs
4
What it does

Key features

  • Vertex AI Studio: no-code and low-code interface for prompting, fine-tuning, and deploying Gemini models
  • Model Garden: access to 150+ models including Gemini, Llama, Mistral, Gemma, and specialist models
  • Agent Builder: build AI agents and RAG applications with grounding in Google Search or enterprise data sources
  • Managed notebooks: managed Jupyter and Colab Enterprise environments for data science development
  • AutoML: automated model training for tabular, image, text, and video tasks without custom ML code
  • Model monitoring: production model quality and data drift detection with automated alerting
When to reach for it

Best for

  • APAC enterprises on Google Cloud Platform wanting unified ML and generative AI infrastructure with GCP-native integration
  • Teams building RAG applications or AI agents on Gemini models who want managed deployment without cloud infrastructure management
  • Data science teams wanting access to a wide range of open-source and proprietary models through a single API
  • APAC organisations that want Google Search grounding for AI systems — ensuring outputs are based on current, verifiable information
Don't get burned

Limitations to know

  • ! Strong GCP lock-in: Vertex AI integrates with BigQuery, GCS, and GCP IAM; migration adds significant friction
  • ! Pricing complexity: Vertex AI has multiple pricing dimensions — training compute, inference, storage, model API calls — cost estimation requires careful modelling
  • ! Vertex AI Agent Builder is maturing but not yet at the enterprise feature depth of purpose-built agentic workflow platforms
  • ! Smaller APAC partner ecosystem for Vertex AI compared to AWS SageMaker — fewer certified Vertex AI practitioners in the region
Context

About Google Vertex AI

Google Vertex AI is a AI productivity tool from Google Cloud, launched in 2021. Google Vertex AI is Google Cloud's end-to-end machine learning and generative AI platform. It covers the complete ML lifecycle — data preparation, model training, evaluation, deployment, and monitoring — while also providing native access to Google's frontier AI models (Gemini 2.0 Flash, Gemini 2.0 Pro) and a Model Garden of 150+ open-source and third-party models. Vertex AI's Agent Builder enables enterprise teams to create AI agents and RAG-powered applications without deep ML expertise. For APAC enterprises on Google Cloud — common in financial services (particularly Singapore and Hong Kong), technology companies, and media — Vertex AI provides a unified infrastructure for both traditional ML and modern generative AI workloads, with GCP-native IAM, monitoring, and compliance features.

Notable capabilities include Vertex AI Studio: no-code and low-code interface for prompting, fine-tuning, and deploying Gemini models, Model Garden: access to 150+ models including Gemini, Llama, Mistral, Gemma, and specialist models, and Agent Builder: build AI agents and RAG applications with grounding in Google Search or enterprise data sources. Teams typically deploy Google Vertex AI for APAC enterprises on Google Cloud Platform wanting unified ML and generative AI infrastructure with GCP-native integration and teams building RAG applications or AI agents on Gemini models who want managed deployment without cloud infrastructure management.

Common trade-offs to weigh: strong GCP lock-in: Vertex AI integrates with BigQuery, GCS, and GCP IAM; migration adds significant friction and pricing complexity: Vertex AI has multiple pricing dimensions — training compute, inference, storage, model API calls — cost estimation requires careful modelling. AIMenta editorial take for APAC mid-market: Google Cloud's end-to-end ML platform with the best Gemini integration for APAC. Covers training, deployment, and agent building — Gemini 2.0, Gemma, and 150+ models via Model Garden. Recommended for APAC enterprises on GCP needing unified ML and GenAI infrastructure.

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