Azure AI Foundry vs AWS Bedrock vs Google Vertex AI: Which platform should you build on?

Your identity is Microsoft

If employees authenticate via Entra ID (formerly Azure AD), Azure AI Foundry gives you Managed Identity across every service — no API keys, no secrets management, full RBAC audit trails. Replicating this on AWS requires bespoke SAML federation work; on GCP it requires Workload Identity Federation configuration.

M365 is your productivity layer

Copilot Studio deploys agents directly into Teams, SharePoint, and Outlook with native graph connectors. If your team lives in M365, the time-to-value for AI assistants is dramatically shorter on Azure than AWS Bedrock or Google Vertex AI.

Regulated industry requirements

Azure AI Foundry inherits Azure's full compliance portfolio — HIPAA BAA, FedRAMP High, ISO 27001, PCI DSS, NHS DSPT. All three platforms meet core compliance requirements, but Azure's breadth and existing enterprise procurement agreements often give it the edge in regulated deals.

You want low-code agent development

Copilot Studio allows non-developer teams (HR, customer service, finance) to build and maintain AI agents without writing code. Neither AWS Bedrock nor Google Vertex AI has a comparable citizen-developer surface with the same enterprise governance controls.

You need the widest model marketplace

AWS Bedrock offers the broadest third-party model catalog today — Anthropic Claude 3.5 Sonnet, Meta Llama, Mistral, AI21 Jurassic, Cohere Command. For organisations that want to evaluate or run multiple model vendors without managing separate API contracts, Bedrock is the most convenient single endpoint.

AWS is your primary cloud

If your data sits in S3, your team knows IAM deeply, and your applications run on ECS or Lambda, Bedrock is the lowest-friction path. Running Azure AI Foundry workloads that depend on your AWS data estate adds unnecessary data egress cost and latency.

ML research pipelines matter

The Bedrock + SageMaker combination is the strongest ML research and training platform of the three. For organisations that fine-tune models regularly or run large-scale training jobs alongside inference, the native SageMaker integration gives AWS a meaningful edge.

Customer inference at AWS scale

If customer-facing inference workloads already run at scale in AWS (e.g., next to a Redshift data warehouse or an RDS database), Bedrock delivers the lowest latency and best operational efficiency by keeping compute and data colocated.

BigQuery is your data warehouse

Gemini in BigQuery allows you to run AI directly against your data warehouse without moving data. For analytics-heavy organisations, this eliminates the ETL step entirely — SQL analysts can call Gemini from a BigQuery query to summarise, classify, or extract from text columns at terabyte scale.

Google Workspace is your productivity layer

If your team works in Google Docs, Gmail, Drive, and Meet, Vertex AI Agent Builder connects natively to the Google Workspace corpus. Building internal knowledge agents, meeting summarisers, or doc review tools is faster on Vertex AI than Azure or AWS for Google Workspace organisations.

You want Gemini 2.0 natively

Google Vertex AI is the first platform where Gemini 2.0 Flash and Pro are production-available with SLAs, provisioned throughput, and fine-tuning support. For organisations that have evaluated models and specifically want Gemini 2.0 as their primary LLM, Vertex AI provides tighter native support than calling the API externally.

Open-source and research workloads

Vertex AI Model Garden offers a broader open-source model selection than Azure AI Foundry, including many HuggingFace models served at scale. For research teams wanting to experiment with open weights without running their own GPU clusters, Model Garden reduces operational overhead.