Amazon Bedrock vs Azure AI Foundry
The question that decides it: Do you need first-party OpenAI models and catalog breadth, or AWS-native governance and FedRAMP High?
Our verdict
Already on AWS, or need FedRAMP High: Bedrock. Already on Microsoft, or you want first-party OpenAI models with a migration that is mostly a base-URL change: Azure AI Foundry.
Why
Catalog size is the most eye-catching difference and the most misleading. Azure AI Foundry publishes 11,000+ models against Bedrock's 100. But Bedrock's number counts models from 19 curated model providers, while Azure's counts an enormous partner and open-weight catalog of wildly varying production readiness. The useful question is not how many models exist but whether the specific ones you need are there, deployable in your region, at a price you can forecast.
The genuine Azure advantage is first-party OpenAI. Azure OpenAI clients are thin wrappers over the official OpenAI SDKs, so migrating an existing OpenAI integration is mostly a base-URL and credential change. If your code already calls OpenAI and you need enterprise governance around it, that is the shortest path in this entire catalog. Azure also ships a built-in Model Router, and gives fine-grained residency control through Global, Data Zone, single-region and Azure Government deployment types.
Bedrock's advantage is depth of compliance and integrated tooling. ISO, SOC, CSA STAR Level 2, GDPR, FedRAMP High, and HIPAA-eligible with an AWS BAA. FedRAMP High in particular has no counterpart in this comparison and settles US public sector questions on its own. Bedrock also builds in Guardrails, evaluations, Knowledge Bases and AgentCore rather than treating them as separate products, and offers Batch and Flex at 50% off standard.
Both share the same limits as any hyperscaler platform: no cross-cloud routing, so multi-cloud failover needs an external gateway; deep coupling to their own cloud's IAM, regions and quotas; and pricing that is hard to forecast — Bedrock across tiers and Provisioned Throughput, Azure around PTUs and deployment types. Azure carries one extra irritation worth naming: constant renaming, from Azure AI Studio to Azure AI Foundry to Microsoft Foundry, which leaves docs and portal labels inconsistent and makes it harder to confirm which attestations cover which brand.
Which one, concretely
Choose Amazon Bedrock if
- You need FedRAMP High or CSA STAR Level 2
- Your infrastructure and IAM are already AWS
- You want Guardrails, evaluations and Knowledge Bases built in rather than assembled
- You want custom model import alongside 19 third-party providers
Choose Azure AI Foundry if
- You want first-party OpenAI models under enterprise governance
- You have existing OpenAI SDK code and want a base-URL-level migration
- You want the largest published catalog and a built-in Model Router
- You need Data Zone, single-region or Azure Government residency control
What catches people out
- Azure's 11,000+ model count includes a large partner and open-weight catalog of varying production readiness. Verify your specific models, in your region, at a forecastable price.
- Azure's repeated rebranding leaves documentation and portal naming inconsistent, and its compliance attestations are documented at Azure service level rather than under the Foundry brand — verify scope per service.
- Bedrock model availability varies by region, so a model you tested may not be available where your data must live.
- Neither routes cross-cloud. Multi-cloud failover requires an external gateway.
- Both need capacity planning around committed throughput units, which is a different discipline from paying per token.
Side by side
2 of 16 fields differ, marked with a dot. Every figure links to the vendor page it came from. Blank values read Not published rather than No — silence from a vendor is not a negative answer.
| Field | Amazon Bedrock | Azure AI Foundry |
|---|---|---|
| Ease of leaving Derived score, higher is easier | 60/80 Some work to leave | 80/80 Some work to leave |
| What kind of product Category | Cloud platform | Cloud platform |
| Who runs it Deployment model | Managed only | Managed or self-host |
| Licence Licence | Proprietary | Proprietary |
| Models available Models available | ~100 | ~10,000 |
| Model providers reachable Upstream providers | Not published | Not published |
| Monthly cost per person Seat fee | Not published | None |
| SOC 2 audited SOC 2 audited | Yes | Yes |
| Will sign a HIPAA agreement HIPAA BAA | Yes | Yes |
| Does not retain your data Zero data retention | Yes | Yes |
| Can keep data in the EU EU data residency | Not published | Yes |
| Content guardrails Content guardrails | Yes | Yes |
| Quality testing Evals | Yes | Yes |
| Batch processing Batch processing | Yes | Yes |
| Rule-based routing Conditional routing | Yes | Yes |
| Usage dashboards and logs Observability | Not published | Yes |
| Settings can live in version control Declarative config-as-code | Yes | Yes |
Verified 3 days ago for Amazon Bedrock and Verified 3 days ago for Azure AI Foundry. Want more fields, or a third option in the mix? Open these two in the full comparison tool.
Common questions
Does Azure AI Foundry really have 11,000 models?
That is the published catalog figure, but it includes a very large partner and open-weight catalog of varying production readiness, against Bedrock's 100 models from 19 curated providers. The number is not a like-for-like comparison — check whether the specific models you need are available in your region at a price you can forecast.
Which is easier to migrate an existing OpenAI integration to?
Azure AI Foundry. Azure OpenAI clients are thin wrappers over the official OpenAI SDKs, so migration is mostly a base-URL and credential change. Bedrock offers OpenAI-compatible Responses and Chat Completions endpoints, which helps, but Azure's first-party path is shorter.
Which has stronger compliance?
Bedrock, for regulated US work specifically: it carries FedRAMP High, CSA STAR Level 2, ISO, SOC and GDPR, and is HIPAA-eligible with an AWS BAA. Azure has broad attestations too, but they are documented at Azure service level rather than under the Foundry brand, so scope needs verifying per service.