# Bedrock, Vertex Model Garden and Foundry: cloud approaches compared

Compare cloud model platforms through an equivalent task, exact execution mode, project access, full operating period and unresolved conditions.

Author: Retrivium team
Updated: 2026-11-01
Canonical URL: https://retrivium.ai/journal/bedrock-vs-vertex-vs-foundry/
Language: en

Amazon Bedrock, Google Cloud Model Garden and Microsoft Foundry support model use but are not identical products. Establish model, execution route and environment conditions before comparing expense and team work.

Sources were checked on November 1, 2026. The former Vertex AI Model Garden link leads to Gemini Enterprise Agent Platform documentation, while Microsoft uses Foundry Models. Familiar search names do not imply identical migration of every existing system.

## Choose an equivalent unit

Begin with the task and quality requirements, then find an appropriate model in each option. If no exact common model exists, state differences instead of presenting them solely as infrastructure properties.

Preserve input format, context, tools and output limits. Use the same synthetic requests to distinguish tested evidence from conclusions not yet transferable to future traffic. Different models can be a useful comparison, but need a clearly stated boundary.

## Verify model and execution mode

The [Bedrock catalog](https://docs.aws.amazon.com/bedrock/latest/userguide/model-cards.html) starts model investigation. [Google Model Garden](https://docs.cloud.google.com/gemini-enterprise-agent-platform/models/model-garden/explore-models) combines discovery and several usage paths. [Foundry Deployment Overview](https://learn.microsoft.com/en-us/azure/foundry/concepts/deployments-overview) separates deployment modes.

Identify the actual mode in each comparison row. Managed APIs, self-deployed weights and dedicated compute can differ in billing and responsibilities. Do not use one mode's price to justify another mode's budget.

## Align environment and access

Check region, application roles, networking and required features. A catalog entry and permission to make a working call are different. Test from future application conditions rather than only an administrator console.

Suppose a model meets quality requirements but its required mode is unavailable in the chosen environment. This need not exclude the whole platform, but creates another scenario to assess. Keep the restriction visible beside cost rather than silently assuming access.

## Compare a complete operating period

One option may bill API units; another requires allocated-resource time and preparation. Align task and time horizon, adding integration, observation and model-change work where needed.

Do not declare a winner from one successful call. Preserve latency variation, failures and useful-answer quality. Test access changes, incident investigation and rollback: organizations may value these independently. Keep an unknown-conditions column in the final table so a preliminary estimate does not become a claim of ready access. It should reveal work still needed before production.

## Place an owned pool in context

Cloud alternatives inform user expectations without confirming acceptance of arbitrary external physical GPUs.

Prepare with [full router cost](/journal/token-router-total-cost/) and [inference economics](/journal/inference-unit-economics/).