Comparison

Updated October 25, 2026

· ~2 min read

Amazon Bedrock vs Microsoft Foundry vs Google Vertex AI in 2026: Models, Capacity, Agents and Data Residency

Amazon Bedrock
Microsoft Foundry
Vertex AI
Claude
OpenAI
Gemini

A year ago the model you wanted often decided the cloud. That is mostly over. Claude is on Bedrock, Vertex AI and, since June 2026, generally available in Microsoft Foundry. OpenAI's models reached general availability on Bedrock in June 2026, alongside Azure. Gemini remains exclusive to Google Cloud, and open-weight families (Llama, Mistral, Qwen, DeepSeek, gpt-oss, Gemma) are on all three in some form.

So the choice now rests on the things around the model: where your data and identity already live, how you buy capacity, what the agent runtime costs and what your procurement team has already signed.

Model availability

Amazon Bedrock

Microsoft Foundry

Google Vertex AI

Anthropic Claude

Yes

Yes, GA since June 2026

Yes

OpenAI GPT models

Yes, GA since June 2026

Yes

Open-weight gpt-oss only

Google Gemini

No (Gemma open weights only)

No

Yes

First-party models

Amazon Nova

Phi and other Microsoft models

Gemini, Gemma, Imagen, Veo

Open-weight families

Llama, Mistral, Qwen, DeepSeek, gpt-oss, Gemma and others

Large catalogue through Foundry Models

Model Garden

Buying capacity

Amazon Bedrock

Microsoft Foundry

Google Vertex AI

Pay per token

Standard tier

Standard deployments: Global, Data Zone or Regional

Pay-as-you-go

Faster at a premium

Priority tier: up to 25% better output tokens per second on most supported models

Provisioned deployments

Provisioned throughput

Cheaper, slower

Flex tier (discounted) and batch

Batch

Batch

Reserved capacity

Reserved tier: tokens-per-minute for 1 or 3 months; legacy Provisioned Throughput

PTUs billed hourly, with monthly or annual reservations

Provisioned throughput subscriptions

Agent runtimes

Bedrock AgentCore

Foundry Agent Service

Vertex AI Agent Engine

Runtime pricing

$0.1276 per vCPU-hour (no CPU charge when idle) and $0.0169 per GB-hour

Billed through the models and tools it uses

$0.0864 per vCPU-hour and $0.0090 per GB-hour, with a monthly free tier

Frameworks

Any: Strands, LangGraph, CrewAI, OpenAI Agents SDK, Claude Agent SDK

Foundry agents, Semantic Kernel and Agent Framework, others via code

Agent Development Kit, LangGraph and others

Identity for agents

AgentCore Identity: inbound auth and outbound OAuth to third parties

Entra ID, including Entra Agent ID

Google Cloud IAM and service accounts

Tool gateway

AgentCore Gateway turns APIs and Lambda into MCP tools

Tools and MCP connections in Foundry

Tools and MCP support in Agent Builder

Residency and privacy

All three state that prompts and outputs are not used to train the providers' models, and all three offer private connectivity. The differences are in routing. Bedrock runs in-Region by default, with cross-Region inference profiles that route within a geography (US, EU, APAC) or globally for more capacity. Foundry offers Global, Data Zone (US or EU) and Regional deployments; Claude on Foundry is available as global standard with a US data zone option. Vertex AI offers regional endpoints and a global endpoint. If a contract says data must stay in the EU, check the specific model and deployment type on each platform. Availability varies by model, and the newest models often launch on global routing first.

Which one

Question 1 of 3

Which cloud holds the data the model needs to read?

Our verdict

**Bedrock:** your data, IAM and networking are on AWS. The widest choice of capacity tiers, AgentCore for running agents, and inference that stays inside your VPC boundary through PrivateLink. **Foundry:** your company runs on Microsoft. Entra ID, Microsoft 365 data, Azure consumption commitments that Claude and OpenAI usage can draw down, and the most mature OpenAI deployment options. **Vertex AI:** you want Gemini, or your data is in BigQuery. Strong data and search integration and a free tier for Agent Engine. Multi-cloud for models alone rarely pays. A gateway in front of one platform, with a second as fallback for one or two models, is usually enough.

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As of October 25, 2026. Provided as is, for general information, and accurate to the best of our knowledge on the date shown. Not legal, compliance, tax, financial or other professional advice. Prices, limits and vendor services change; check current vendor documentation before relying on anything here. Terms of use.