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A catalog entry does not define execution or billing. Preserve the model, deployment method and documentation date when names change.
Vertex AI Model Garden remains a common search name for Google’s model catalog. On November 1, 2026, the official link redirected to Gemini Enterprise Agent Platform documentation. We retain the familiar name while using the current page and model cards.
The redirect does not establish that every existing service or interface changed completely. A decision needs the particular model, execution route and project conditions, checked before migration or budget comparison.
Separate catalog and execution
Model Garden is described as a library for discovering, testing, tuning and deploying models. Catalog presence does not explain how execution is billed or operated.
Save the chosen card and usage path, including deployment requirements, resources and operating ownership. Do not transfer one model’s execution mode to another because both appear in the same search interface.
Check managed and self-deployed paths
Partner models as a service have their own executor terms, regions and interfaces to verify. Deploying open weights yourself is another scenario with different operating work.
For example, compare a ready API with weights deployed on a dedicated resource. Align the model and task first, then evaluate expenses and duties, including preparation and maintenance rather than only a generation unit. The scenarios should remain distinguishable in the decision record.
Establish cloud-project restrictions
Check permissions, organizational policies and resource availability. A visible model may not yet be usable in the selected environment. Test under the role and project intended for the application.
A model–version–region–deployment–capabilities–access table reduces confusion across alternatives. It also explains why one option needs another preparation stage rather than treating catalog visibility as operational readiness.
Evaluate the complete scenario
Use identical requests with quality criteria. Preserve readiness time, latency and actual resources. For self-deployment, include time when resources are allocated but no useful workload is running.
The comparison should answer the project’s question: an early experiment, persistent service or tuning. One average cost without the task cannot choose between these goals. Keep the pilot’s date and precise configuration, plus card date and current documentation address. When names change, these records reveal which mode was actually compared rather than relying on an old search phrase.
Relationship to an owned GPU pool
Google’s catalog does not confirm automatic onboarding of third-party physical GPUs.
Continue with Bedrock, Model Garden and Foundry and inference-capacity planning.