Choosing a model shouldn't mean choosing a cloud provider forever. Model Garden brings together first- and third-party models under a single deployment surface on Vertex AI.
Model Garden is Vertex AI's central catalog, bringing together more than 200 foundation and specialized models, organized into three categories: Google's proprietary models (Gemini, Imagen, Veo, Chirp, the embedding models), third-party partner models available as a fully managed API, and open-weight models that can be deployed on Google Cloud infrastructure under your own project.
The value proposition is avoiding letting model choice become an irreversible architecture decision: they're all invoked with similar API patterns and deployed over the same Vertex AI endpoint, IAM, and billing layer.
Besides the Gemini family, Model Garden includes Imagen for image generation and editing with style control, Veo for video generation from text or image, Chirp for high-accuracy multilingual speech recognition, and the embedding models (`text-embedding-005`, `multimodalembedding`) that power Vector Search and Vertex AI Search.
One of the least-known facts among teams evaluating Google Cloud: Anthropic's Claude models are available on Model Garden as a fully managed API inside Vertex AI, with the same billing and IAM controls as the rest of the platform. The same applies to Mistral AI models. This lets an organization standardize its deployment, observability, and access-control infrastructure on Vertex AI while choosing the specific model -- Gemini, Claude, Mistral -- based on whichever benchmark best fits each task, without duplicating credential integration and logging per provider.
For organizations with full data-isolation requirements or a need for deep fine-tuning, Model Garden offers one-click deployment of open-weight models like Llama (Meta), Gemma (Google's own open-weight family), and others, on the project's own GPUs (A100, H100) or TPUs. Unlike managed APIs, here the user pays directly for the endpoint's compute and is responsible for sizing it.
Model Garden includes per-model technical sheets with standard benchmarks (MMLU, HumanEval, recommended use cases) and, for several models, a playground integrated into Vertex AI Studio for testing prompts without writing code before deciding. The practical recommendation: run the same set of prompts representative of your domain (not generic benchmarks) against two or three candidates before locking in the production model, because relative performance varies significantly by task type.
Fine-tuning options vary by the model's origin. Gemini supports managed supervised fine-tuning directly on Vertex AI. User-deployed open-weight models allow full fine-tuning or low-cost techniques like LoRA, running as a custom training job on the project's GPUs. Partner models like Claude generally don't expose fine-tuning within Vertex AI, limited to use via prompt engineering and, in some cases, distillation techniques separate from the provider.
Having 200 options isn't free in governance terms: every model deployed in Model Garden requires its own quota policy, its own per-token or per-GPU-hour cost analysis, and, for open-weight models, your own responsibility for security patches on the serving container. The operational recommendation is limiting the internally approved catalog to two or three models per task type, rather than letting each team freely choose among 200 options.
Carlos Montiel is an enterprise AI solutions architect. He implements LLMs, Agents, RAG, and orchestrators for companies across Guatemala and Latin America. Reach out for a consultation.
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