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Google AI Studio is a free, browser-based prototyping tool aimed at individual developers experimenting with Gemini through a simple API key. Vertex AI is the enterprise platform: it runs inside Google Cloud projects with IAM, VPC controls, audit logging, regional data residency, SLAs, and the full MLOps stack. Most production workloads belong on Vertex AI; AI Studio is for prototyping.
Model Garden includes Google's own Gemini family (Pro, Flash, and long-context variants), Imagen for image generation, Veo for video, Chirp for speech, and Codey for code. Third-party models include Anthropic's Claude, Meta's Llama, Mistral, AI21, and a growing list of open-source and partner models. Availability of specific models can vary by region.
Pricing is consumption-based and varies by component. Foundation models are billed per 1K input/output tokens (or per image/second of video). Custom training is billed per machine-hour on the chosen CPU/GPU/TPU configuration. Online prediction endpoints are billed per node-hour while running, batch prediction per job. Storage, Pipelines, Feature Store, and Model Monitoring have their own line items. New customers get GCP free credits, and there is a small always-free tier for experimentation.
Yes. Vertex AI supports supervised fine-tuning on Gemini and several open models, distillation for smaller student models, and RLHF for alignment. Tuned model weights stay within your Google Cloud project, are not used to train Google's base models, and can be deployed to private endpoints with the same governance controls as base models.
No. Per Google Cloud's customer data terms, prompts, responses, and tuning data submitted to Vertex AI are not used to train or improve Google's foundation models, and customer data is logically isolated within the customer's project. Enterprise controls including CMEK, VPC Service Controls, and data residency settings further restrict where data is processed and stored.
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