Microsoft Foundry, Amazon Bedrock, and Gemini Enterprise Agent Platform (formerly Vertex AI) are the foundation layer. The control-plane choice shapes identity, model access, governance defaults, and deployment options for everything you build on top.
Microsoft
Unified enterprise AI platform spanning models, agents, tools, evaluations, and governance.
Why teams pick it: Broadest model catalog plus agents, governance, and sovereign deployment options.
AWS
Managed foundation model platform with Bedrock Agents, AgentCore, and Bedrock Guardrails.
Why teams pick it: Framework-agnostic model access with strong AWS-native agent and safety integration.
Enterprise GenAI platform combining Model Garden, agent tooling, MLOps, and safety controls.
Why teams pick it: Model Garden plus Gemini, Agent Development Kit, and enterprise MLOps in one stack.
Where the major cloud foundations expose protocol alignment for MCP, A2A, and OpenAPI.
Model Context Protocol is becoming the default interoperability layer for tool and service connections.
Flowise 3.1.3 added chatflow-as-MCP-server and custom MCP server support, expanded Agentflow client filtering and knowledge fields, added initial observe package scaffolding, and fixed clickjacking plus frame-header handling.
Microsoft Agent Framework Python 1.9.0 added tool-approval middleware, integrated approvals into Harness agents, denied MCP server-initiated sampling by default, and promoted orchestrations to stable.
Agent2Agent support matters where teams expect cross-agent handoffs instead of single-vendor agent silos.
Microsoft Agent Framework 1.7.0 added harness agents, A2A session support, and new Foundry integration hooks, extending the framework's orchestration and inter-agent workflow surface.
Microsoft Agent Framework 1.4.0 introduced experimental skills API breaking changes, migrated its A2A layer to a2a-sdk v1.0, and tightened default DevUI access controls and CORS posture.
OpenAPI still matters as the stable enterprise contract for API exposure, governance review, and gateway alignment.
Semantic Kernel Python 1.43.1 added function_choice_behavior support to Azure AI and OpenAI Assistant agents, fixed MessagePack handling, and rejected encoded dot-segment paths in OpenAPI plugins.
This bridges the platform layer with the category hubs, so visitors can quickly see the native cloud product most relevant to each tracked category.
| Category | Microsoft Foundry | Amazon Bedrock | Gemini Enterprise Agent Platform |
|---|---|---|---|
| Agent Frameworks | Foundry Agent Service | Bedrock Agents + AgentCore | Agent Builder + ADK |
| Orchestration | Logic Apps (AI-enhanced) | Step Functions | Cloud Workflows |
| Governance | AI Content Safety | Bedrock Guardrails | Model Armor |
| Assistants (Coding) | GitHub Copilot | Amazon Q Developer | Gemini Code Assist |
| Assistants (Productivity) | M365 Copilot | Amazon Q Business | Gemini for Workspace |
| Assistants (Build Your Own) | Copilot Studio | Amazon Q Apps | Gemini Enterprise |
Use the full table below after the platform summaries above. This is the detail layer for direct vendor-to-vendor comparison rather than the best entry point for first-time visitors.
| Attribute | Microsoft Foundry | Amazon Bedrock | Gemini Enterprise Agent Platform |
|---|---|---|---|
| Formerly known as | Azure AI Foundry / Azure AI Studio / Azure OpenAI Service | AWS Bedrock (common informal name) | Vertex AI; AI Studio is a separate free playground |
| What it is | Unified PaaS for enterprise AI: models, agents, tools, evaluations, governance | Managed service for foundation models with agent building and guardrails | Full ML + GenAI platform: model garden, agent engine, MLOps, evaluation |
| Model catalog | 11,000+ models | 100+ foundation models | 200+ via Model Garden |
| Unique models | GPT-5.4, GPT-5.4 Pro, Phi-4, MAI-Image-2, Sora 2 | Amazon Nova, Amazon Titan | Gemini 3.1 Pro, Nano Banana Pro, Veo 3.1, Imagen 4 |
| Agent service | Foundry Agent Service (GA) | Bedrock Agents + AgentCore (GA) | Agent Builder + ADK + Agent Engine |
| Guardrails | AI Content Safety | Bedrock Guardrails | Model Armor + Vertex AI Safety |
| Protocol support | MCP, A2A, OpenAPI | MCP, A2A | MCP, A2A, OpenAPI |
| SDK languages | Python, C#, JS/TS, Java | Python, JS, Java, Go, .NET | Python, Java, Go, TypeScript |
| Unified SDK | azure-ai-projects v2 | boto3 + AWS SDK | google-cloud-aiplatform |
| Connectors | 1,400+ via Logic Apps + Tool Catalog | 220+ AWS services | GCP services + Workspace |
| Free tier | $200 Azure credit | $200 AWS credit | $300 GCP credit + AI Studio free playground |
| Pricing model | Pay-per-token + per-tool | Pay-per-token + per-feature | Pay-per-token + per-compute |
| On-premises | Yes (Foundry Local) | No | No |
| MCP server | Yes (mcp.ai.azure.com) | Preview | Preview |
| EU data residency | Yes | Yes | Yes |
| SOC 2 / ISO 27001 | Yes / Yes | Yes / Yes | Yes / Yes |
| HIPAA | Yes | Yes | Yes |
| Key updates | SDK 2.0 GA, Agent Service GA, GPT-5.4 GA, PromptFlow deprecated | AgentCore GA, Automated Reasoning GA, 85% guardrails price cut | ADK open-sourced, Gemini 3.1 Pro, Gemini Enterprise rename, Wiz acquisition |
They are the three control-plane platforms that shape identity, model access, governance defaults, and deployment options for enterprise AI workloads. Picking a platform constrains every layer above it, so they are compared side-by-side as the foundation layer.
Microsoft Foundry (formerly Azure AI Foundry / Azure AI Studio / Azure OpenAI Service), Amazon Bedrock, and the Gemini Enterprise Agent Platform (formerly Vertex AI). The 'Gemini Enterprise' assistant product is a separate offering and not the same as the agent platform.
Microsoft Foundry Local provides an on-premises option. Amazon Bedrock and the Gemini Enterprise Agent Platform run as managed cloud services without an on-prem deployment surface today.
Yes. Microsoft Foundry, Amazon Bedrock, and the Gemini Enterprise Agent Platform all expose MCP and A2A support; Microsoft and Google additionally publish OpenAPI tool catalogs. The platform comparison row tracks the current status per protocol.
Use the category hubs to drill from the cloud foundation layer into tracked agent, orchestration, governance, assistant, and update pages.
Compare managed cloud agent stacks with open source agent frameworks used in enterprise deployments.
Review workflow engines, pipeline builders, and automation layers connected to the platform layer.
Check guardrails, safety controls, and policy tooling mapped across the major cloud vendors.
Explore coding assistants, productivity copilots, and build-your-own assistant platforms.
Follow releases, deprecations, acquisitions, and other market changes across the tracked landscape.
Review sourcing standards, contribution rules, and project scope before editing tracked platform data.