Google Vertex AI & the Gemini API
Google rents out its Gemini models through two doors: the free-to-start Gemini API for developers and enterprise-grade Vertex AI on Google Cloud. The family is natively multimodal (text, images, audio, video in one stream) with the largest context windows in the frontier class (1M-2M tokens), plus two Google-only tricks: grounding in real Google Search and deep discounts via context caching.
01.The Problem: One Model, Two Completely Different Customers
Imagine two people who both want to use Google's best AI models.
Person 1: a developer at midnight with an idea. They want a key in five minutes, a free tier to poke at, and no meetings with a cloud vendor.
Person 2: a platform engineer at a bank. They need IAM roles, private networking, regional data governance, contracts, and invoices that land inside the cloud bill they already have.
One product rarely serves both well. So Google ships the same Gemini models through two doors:
- the Gemini API — the midnight developer's door,
- Vertex AI — the bank's door, inside Google Cloud.
The idea in one line: one frontier model family, wrapped twice — once for speed of learning, once for safety of operating.
And Gemini brings two party tricks almost nobody else matches at the same quality: it natively understands text, images, audio, and video in a single token stream, and it can hold 1M-2M tokens in one request — the largest windows in the frontier class.
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