Can I use both Azure AI Foundry and AWS Bedrock together?

Yes. Many large enterprises run Azure AI Foundry for Microsoft-ecosystem workloads — Teams agents, SharePoint RAG, M365 Copilot extensions — while running AWS Bedrock for inference workloads that sit next to existing AWS infrastructure. The platforms call different model endpoints and can coexist within the same organisation. The main consideration is data gravity: each AI workload performs best when it runs close to the data it needs, so keep each use case on its native cloud.

Which platform has more AI models — Azure AI Foundry or AWS Bedrock?

AWS Bedrock currently has a broader third-party model marketplace, including Anthropic Claude, Meta Llama, Mistral, AI21 Labs, and Cohere. Azure AI Foundry has a strong model catalog — GPT-4o, o1, Phi-4, Mistral, Llama, and via AI Foundry Model Catalog — but its selection skews toward the OpenAI family. For organisations wanting the widest possible model choice or wanting to use Anthropic Claude specifically, AWS Bedrock has an advantage. For organisations already on Azure OpenAI, Azure AI Foundry is the natural home.

What is the cost difference between Azure AI Foundry and AWS Bedrock?

Both platforms use pay-per-token pricing for on-demand inference, with comparable per-token rates for equivalent model families. Azure AI Foundry offers Provisioned Throughput Units (PTU), which provide reserved capacity at predictable monthly cost — typically more economical for high-volume, consistent workloads. AWS Bedrock offers Provisioned Model pricing with similar economics. The key cost differences emerge at the infrastructure layer: Azure AI Foundry deployments using Private Endpoints and Azure AI Search add Azure infrastructure costs that AWS Bedrock Knowledge Bases also add on the AWS side. Total cost of ownership comparisons should include vector storage, private networking, and logging costs alongside token pricing.

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

Both are enterprise-grade managed AI platforms, but they sit in fundamentally different ecosystems. Azure AI Foundry is purpose-built for Microsoft-centric organisations; AWS Bedrock excels for AWS-native workloads and multi-cloud teams. The right answer depends almost entirely on where your data and identity already live.

Quick Verdict

Choose Azure AI Foundry if…

✓Your org runs on Microsoft 365, Teams, SharePoint or Dynamics 365
✓You need Entra ID RBAC and Managed Identity (no stored credentials)
✓HIPAA, FCA, or FedRAMP compliance is a hard requirement

Choose AWS Bedrock if…

✓Your workloads already run in AWS and your team knows IAM deeply
✓You need the widest possible third-party model marketplace today
✓You are building ML-research-heavy pipelines with SageMaker integration

Consider both if…

✓You operate a hybrid cloud strategy with workloads in both clouds
✓Your AI use cases span CRM (Salesforce on AWS) and productivity (M365)
✓You want to avoid single-vendor lock-in at the model layer

Feature comparison

How the platforms stack up across the capabilities that matter most for enterprise AI deployments.

Feature	Azure AI Foundry	AWS Bedrock
Model catalog breadth	GPT-4o, o1, Phi-4, Mistral, Llama + fine-tuning	Claude, Titan, Llama, Mistral, AI21 — wider 3rd-party variety
Enterprise security (IAM)	Entra ID RBAC + Managed Identity (credential-free)	IAM roles + STS — strong but separate from AD
Native M365 integration	Teams, SharePoint, Dynamics, Outlook native connectors	Requires custom API bridge or 3rd-party middleware
Managed Identity	Zero-credential auth across all Azure services	IAM Instance Roles achieve similar but no AD federation
Multi-agent orchestration	Semantic Kernel + AutoGen, Copilot Studio visual builder	Bedrock Agents with step functions; more code-heavy
Low-code builder	Copilot Studio — citizen developer friendly	PartyRock playground; no enterprise-grade low-code agent UI
Compliance frameworks	HIPAA BAA, FedRAMP High, ISO 27001, PCI DSS, GDPR	HIPAA BAA, FedRAMP High, ISO 27001, PCI DSS — equivalent
Pricing model	PTU (provisioned) or pay-per-token; PTU gives cost predictability	On-demand + provisioned throughput; generally competitive
Vendor lock-in risk	High if using Copilot Studio; lower if Semantic Kernel only	High if using Bedrock Agents/Knowledge Bases natively
RAG tooling	Azure AI Search (hybrid + semantic) deeply integrated	Knowledge Bases with OpenSearch; strong but less tightly coupled

Strong / native Partial / requires configuration Not available

When to choose Azure AI Foundry

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.

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.

Regulated industry requirements

Azure AI Foundry inherits Azure's full compliance portfolio — HIPAA BAA, FedRAMP High, ISO 27001, PCI DSS, NHS DSPT. For healthcare, financial services, or public sector, this removes a significant procurement and legal review burden.

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. AWS has no equivalent citizen-developer surface for enterprise agent creation.

Our recommendation

For organisations that have standardised on Microsoft — meaning Entra ID for identity, M365 for productivity, and Azure for cloud infrastructure — Azure AI Foundry is the clear choice. The integration depth is not superficial: Managed Identity means your AI agents authenticate to every Azure service without a single stored credential. Azure AI Search plugs directly into SharePoint and OneDrive data estates. Copilot Studio agents surface inside Teams without any embedding work. The productivity multiplier is real.

AWS Bedrock is the stronger choice for teams deeply invested in the AWS ecosystem — particularly those running large SageMaker pipelines, using Aurora/Redshift as their data layer, or operating in AWS GovCloud. The Bedrock model marketplace also tends to receive newer third-party model versions (Claude, Llama, Mistral) slightly ahead of Azure, which matters for organisations wanting to stay on the frontier.

Where we see organisations make mistakes is assuming this is a permanent binary choice. Many large enterprises run Azure AI Foundry for internal productivity agents (M365-integrated) and AWS Bedrock for customer-facing inference workloads (sitting next to existing AWS infrastructure). The platforms are not mutually exclusive — but each workload should live where its data and identity already reside. We recommend Azure AI Foundry as the primary enterprise AI platform for any Microsoft-centric organisation, with AWS Bedrock as a secondary option for specific AWS-native workloads.

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

Feature comparison

When to choose Azure AI Foundry

Our recommendation

Not sure which platform fits your workload?

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