Google Vertex AI
by Google (Google Cloud)
Google Cloud's unified platform to build, deploy, and scale machine learning and generative AI
Google Vertex AI is Google Cloud's unified machine learning and generative AI platform that brings the full ML lifecycle, foundation models, and agent tooling into a single environment. It spans data preparation, training, fine-tuning, deployment, and MLOps, with access to Gemini and 100+ models via Model Garden.
At a Glance
- Category
- Enterprise Platform
- Pricing
- Usage-based, Pay-as-you-go, Freemium
- Target Market
- Data Scientists, ML Engineers, Enterprise Developers, CTOs, CIOs, MLOps Teams
- Founded
- 2021
- Headquarters
- Mountain View, California, United States
Key Features
- ✓Model Garden
A library of 100+ first-party, open, and partner models (Gemini, Imagen, Veo, Lyria, Llama, Gemma, Claude) deployable from one place.
- ✓AutoML
No-code training of high-quality models on tabular, image, text, and video data with just a few clicks.
- ✓Custom training and fine-tuning
Train custom models and fine-tune foundation models like Gemini using the Vertex AI SDK and managed compute.
- ✓Agent Builder and Agent Engine
Build production AI agents and run them on a generally available managed runtime, with the open Agent Development Kit and A2A protocol.
- ✓Generative media models
Generate images (Imagen), video (Veo), speech (Chirp), and music (Lyria) within a single platform.
- ✓MLOps and model monitoring
Pipelines, feature store, evaluation, and monitoring for managing models reliably in production.
Capabilities
Use Cases
- •Multimodal generative AI applications
Use Gemini and generative media models to process and produce text, images, audio, video, and code in one platform.
- •Custom ML model development and deployment
Build, train, and serve fraud detection, recommendation, and forecasting models with full MLOps support.
- •Production AI agents
Develop, deploy, and govern enterprise agents on a managed runtime with grounding via Google Search and enterprise data.
Ideal For
Best For
- ✓End-to-end machine learning lifecycle and MLOps
- ✓Building generative AI and multimodal applications with Gemini
- ✓Developing and deploying production AI agents
Integrations
Market Analysis
Pros
- ✓Only major platform with generative media across video, image, speech, and music
- ✓Strong cost efficiency on model inference
- ✓Comprehensive end-to-end ML lifecycle and MLOps
- ✓AutoML enables no-code model building for non-experts
Cons
- ✗Multi-dimensional pricing can be complex to estimate
- ✗Steep learning curve and dense documentation for newcomers
- ✗Costs can scale quickly during experimentation
Pricing
Free trial credits
$300 free credits
- ✓$300 in credits for new Google Cloud users
- ✓Valid for 90 days
- ✓Experiment with Vertex AI and Google Cloud products
Pay-as-you-go (Generative AI)
Usage-based
- ✓Per-token / per-1,000-character text, chat, and code generation
- ✓Imagen image generation starting at $0.0001
- ✓No upfront commitment
Custom training and Agent Engine
Usage-based
- ✓Custom model training billed by machine type, region, and accelerators
- ✓Agent Engine runtime billed per vCPU-hour and GB-hour
- ✓Search and data-store queries billed per 1,000 queries
Pricing is usage-based with no upfront commitment and a multi-dimensional billing model spanning model inference (per token / per 1,000 characters), training compute, prediction nodes, storage, and Agent Engine runtime. New customers receive $300 in free credits valid for 90 days.
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