Enterprise Agent Hosting Compared: AWS Bedrock vs Google Vertex AI vs Azure AI Foundry

Enterprise AI selections usually default to whichever cloud the master contract already covers. That instinct optimizes procurement and ignores fit, which is how agent rollouts stall on per-account concurrency ceilings nobody surfaced in the RFP, identity work doubles instead of reusing the existing SSO graph, and compliance freezes launches from data-residency gaps that should've been caught at vendor selection.

The three hyperscaler platforms look interchangeable on paper, and they aren't. Picking the wrong one bleeds integration rework and risk reviews, not per-token cost. This piece compares AWS Bedrock, Google Vertex AI, and Azure AI Foundry on three concerns enterprises feel in production: agent concurrency, SSO/IAM/audit/data integration, and compliance across SOC 2, HIPAA, FedRAMP, and GDPR.

The Three Platforms in One Paragraph Each

Each platform sits on a different cloud and doesn't solve the same problem the same way.

AWS Bedrock. AWS's managed model marketplace. One API across Anthropic Claude, Meta Llama, Amazon Titan, Cohere, Mistral, and AI21, with Bedrock AgentCore handling the agent runtime, guardrails, and knowledge bases.

Google Vertex AI. Google Cloud's end-to-end ML and generative AI platform. It bundles Gemini, Gemma, and a Model Garden of fifty-plus third-party options with managed training, fine-tuning, vector search, and Agent Builder.

Azure AI Foundry. Microsoft's unified AI workshop. Built around the Azure-OpenAI partnership for the GPT family, plus Llama, Mistral, DeepSeek, Cohere, Phi, and Anthropic, with an agent service tightly bound to Entra ID and Microsoft 365.

With the baselines set, start with the concern most teams underweight at the RFP stage: concurrency.

Concern #1: Agent Concurrency at Enterprise Scale

Agent concurrency is where naive load assumptions break. All three enforce per-account, per-region, and per-model quotas, and the shape of those quotas decides whether your rollout survives Monday morning.

Bedrock. Quotas are model-specific and region-specific, expressed as tokens-per-minute and requests-per-minute. Provisioned Throughput contracts buy guaranteed capacity in model units, which is the lever for committed enterprise load.

Vertex AI. Quotas use a dynamic shared-quota model on Gemini for online prediction, with explicit Provisioned Throughput for reserved capacity. You'll request increases through the Cloud Console for sustained agent traffic above default ceilings.

Foundry. Throughput uses Provisioned Throughput Units (PTUs) for predictable load and Standard (pay-as-you-go) for bursty traffic. PTUs guarantee latency but cost more if utilization stays under 70%.

The takeaway: each platform supports enterprise agent traffic, but reserved-capacity pricing only pays back on sustained load. Bursty workloads punish PTU-heavy designs. Concurrency tells you what'll scale. Integration tells you what'll ship on time.

Concern #2: Integration with Existing Enterprise Systems

Most enterprise integration cost lives in four buckets: SSO, IAM, audit logging, and data movement between the AI platform and your existing systems of record. Here's how the three platforms handle each.

Integration	AWS Bedrock	Google Vertex AI	Azure AI Foundry
SSO	AWS IAM Identity Center, SAML 2.0, OIDC	Cloud Identity, Workforce Identity Federation, SAML	Entra ID (formerly Azure AD), native to Microsoft 365
IAM granularity	IAM policies + Bedrock-specific resource ARNs	IAM roles + VPC Service Controls	Entra ID + Azure RBAC + Conditional Access
Audit logging	CloudTrail + Bedrock model invocation logs	Cloud Audit Logs + Vertex AI request logging	Microsoft Purview + Azure Monitor
On-prem data	AWS PrivateLink, Direct Connect, S3 sync	Private Service Connect, Interconnect, BigQuery federation	Azure Private Link, ExpressRoute, on-prem data gateway
Microsoft 365 / Office data	Custom connectors needed	Custom connectors needed	Native (Foundry treats M365 as first-class)

The shortest path to production depends on your existing identity provider. Microsoft 365 shops save weeks on Foundry because Entra ID, Purview, and Conditional Access already cover the controls. AWS-native shops get the same compounding on Bedrock. Multi-cloud shops benefit from Vertex's Workforce Identity Federation.

Integration cost is real, but compliance is what blocks launch.

Concern #3: Compliance and Data Residency Coverage

Compliance is rarely the differentiator on slides, and almost always the differentiator on launch dates. The three platforms aren't equal here.

Compliance	AWS Bedrock	Google Vertex AI	Azure AI Foundry
SOC 2 Type II	Yes	Yes	Yes
HIPAA BAA	Yes (HIPAA-eligible across Claude, Llama, Titan)	Yes (covered under Google Cloud BAA)	Yes (covered under Azure BAA)
FedRAMP High	Yes (broad service coverage)	Yes (first GenAI platform to reach FedRAMP High, per Google Cloud 2025)	Yes (Azure Government, select Foundry services)
GDPR / EU data residency	EU regions available; data processing addendum standard	EU regions + Sovereign Controls for EU	EU Data Boundary commitment
Industry-specific	PCI DSS, ISO 27017/27018, IRAP	ISO 27001/17/18, PCI DSS, HITRUST	ISO 27001, PCI DSS, sector-specific (UK NHS, etc.)

All three cover the core frameworks. The real splits are at the edges. AWS ships the broadest cert set across the underlying cloud, which matters when AI sits next to existing in-scope AWS services. Vertex's FedRAMP High milestone for generative AI shortens federal pilot paths. Foundry's EU Data Boundary is the cleanest GDPR story when residency is non-negotiable.

Three concerns mapped. The next question: which workload picks which platform.

Per-Platform Decision Frame: When Each One Wins

This isn't about which platform is "best." It's which one wins for which org profile.

Pick Bedrock when	Pick Vertex AI when	Pick Foundry when
You're AWS-native and need broadest compliance overlap with existing services	You need FedRAMP High generative AI today	You're Microsoft 365 / Dynamics native
Claude family is central to your agent design	Your data already lives in BigQuery or you need Gemini-class multimodal	OpenAI GPT family is central to your design
Bedrock AgentCore guardrails matter for content filtering	Workforce Identity Federation across multiple IdPs is a constraint	Entra ID + Purview already cover your control plane
You need the largest model catalog under one API	Custom ML / MLOps depth matters as much as inference	EU Data Boundary is a hard compliance line

The frame leaves one gap: model diversity beyond what one hyperscaler hosts, and inference capacity that doesn't compete with the same cloud's training jobs at peak. These three cover most enterprise stacks, but they aren't the only API surface worth keeping warm.

Where GMI Cloud Fits Into an Enterprise Stack

The better-designed enterprise stacks treat model access as a portfolio, not a single vendor commitment. GMI Cloud fits that portfolio role as a complement to Bedrock, Vertex, or Foundry, not a replacement.

Multi-model API. The GMI Cloud Inference Engine exposes 100+ open and proprietary models through one OpenAI-compatible endpoint. That's useful when a hyperscaler doesn't carry a model your team wants, or when you want to A/B between vendors without re-architecting.

NVIDIA-optimized inference. The platform runs on NVIDIA Reference Cloud infrastructure with H100, H200, and B200 capacity. Inference is tuned with TensorRT-LLM and SGLang, which matters when the hyperscaler region you'd default to is hitting capacity limits at peak.

Frontier-class model picks. Claude Opus class models cover deep document analysis, code review, and multi-step agent reasoning. Frontier GPT models cover general chat, structured extraction, and tool-use orchestration. Both are reachable through Inference Engine without locking into one hyperscaler's quotas.

When it earns a slot. Multi-cloud strategy, overflow during a hyperscaler region outage, or model evaluation across providers before locking a Provisioned Throughput contract.

What it doesn't replace. GMI Cloud doesn't carry FedRAMP High, SOC 2 Type II, or HIPAA BAA attestations directly, and it isn't a Conditional Access or enterprise IAM provider. Workloads bound by those compliance requirements still need to land on Bedrock, Vertex AI, or Foundry. GMI Cloud's role is the model API sitting beside those primaries, not in place of them.

The Bottom Line

Enterprise platform selection isn't decided by feature tables. It's decided by whichever platform your existing identity, audit, and compliance graph already covers, plus the model your highest-value workload depends on.

Bedrock wins on AWS-native compliance overlap and Claude depth. Vertex AI wins on FedRAMP High generative AI and BigQuery-adjacent workloads. Foundry wins on Microsoft 365 integration and EU Data Boundary. GMI Cloud sits alongside any of them when you need a model or capacity the hyperscaler can't deliver.

FAQ

Which platform has the highest agent concurrency limit out of the box?

None publish a single "max concurrent agents" number, because concurrency depends on model, region, and account quota. All three support provisioned-capacity contracts (Bedrock Provisioned Throughput, Vertex Provisioned Throughput, Azure PTUs) for sustained load. Default quotas almost always need an increase request before production launch.

Can I use the same model across Bedrock, Vertex, and Foundry?

Some models overlap (Claude on Bedrock and Vertex, Llama on all three), but day-zero access and fine-tuning options differ. If portability matters, design against an OpenAI-compatible interface. The GMI Cloud Inference Engine offers that abstraction across 100+ models.

Which is best for HIPAA-regulated workloads?

All three carry BAA coverage on core services. AWS Bedrock tends to be fastest to PHI approval given AWS's depth in healthcare. Vertex and Foundry both work, with Foundry preferred when the EHR or admin stack already lives in Microsoft. Confirm the specific model is BAA-covered, since coverage varies.

Where does on-prem inference fit if none of these match our data-residency rules?

On-prem options like NVIDIA NIM containers, IBM watsonx, and Oracle AI services exist for air-gapped deployments. They trade hyperscaler operational depth for residency control. A hybrid pattern (on-prem for regulated workloads, GMI Cloud Inference Engine for everything else) is common in financial services and federal pilots.