By mid-2026, the three hyperscaler AI platforms have copied each other’s homework. Managed model catalogs, agent frameworks, guardrails, RAG tooling, provisioned throughput — pull a feature slide from AWS Bedrock, Google Vertex AI, or Microsoft’s Azure AI Foundry and you could swap the logo without anyone noticing. The checklist era is over.
What still separates them is much harder to copy: which frontier models each cloud is allowed to serve you, and which compliance authorizations each has actually cleared. Those two factors — plus a pair of billing traps that surprise teams after the contract is signed — should drive the decision in 2026. This brief covers all of it.
The short version
If your enterprise runs regulated workloads that require FedRAMP High or DoD impact levels, the choice is Bedrock or Azure OpenAI — Vertex is disqualified until its authorization goes GA, regardless of how good Gemini is. If you need OpenAI’s frontier models under an enterprise agreement, Azure AI Foundry is the only cloud that has them. If model breadth is your priority and compliance isn’t a gate, Vertex AI’s Model Garden is the deepest catalog of the three. And if you want a hedge, note that Anthropic’s Claude family is the one frontier lineup available on all three clouds.
| AWS Bedrock | Google Vertex AI | Azure AI Foundry | |
|---|---|---|---|
| Anchor models | Claude, Amazon Nova | Gemini | OpenAI frontier models |
| Also serves | Llama, Mistral, Cohere, open-weight GPT OSS | Claude, Gemma, Llama, Mistral — 50+ in Model Garden | Claude, Llama, Mistral, Grok, DeepSeek |
| OpenAI frontier models | No (open-weight variants only) | No | Yes — exclusive among the clouds |
| FedRAMP High | Yes, incl. GovCloud IL-4/5 | Not yet GA as of mid-2026 | Yes (Azure OpenAI) |
| Billing watch-out | Provisioned throughput commitments | Thinking tokens billed as output; long-context rate step-up | Capacity quotas on frontier models |
| Best fit | Regulated workloads, AWS-committed shops | Model breadth, Google-stack shops | OpenAI dependency, Microsoft-committed shops |
What changed
Three shifts moved the board over the past year. First, Microsoft folded its Azure OpenAI story into the broader Azure AI Foundry platform and widened the catalog well beyond OpenAI — Anthropic’s Claude models landed there, joining Llama, Mistral, Grok, and DeepSeek. Second, OpenAI’s open-weight GPT OSS models appeared on Amazon Bedrock, and in June 2026 those models — along with NVIDIA Nemotron — received FedRAMP High and DoD IL-4/5 approval in AWS GovCloud. Third, Google kept extending Vertex’s Model Garden while its FedRAMP High authorization stayed in progress rather than GA.
The net effect: feature parity is now assumed, and the platforms compete on distribution rights and compliance paper. That is a very different evaluation than the one most enterprises ran in 2024.
Model exclusivity is the real differentiator
Start with the exclusives, because they are the constraint you cannot negotiate around. OpenAI’s frontier models remain available only through Azure and OpenAI’s own API — no amount of AWS or Google spend commitment gets you GPT-class frontier models on Bedrock or Vertex. The open-weight GPT OSS variants on Bedrock are useful, but they are not the flagship line. Likewise, Gemini is served through Vertex and Google’s own developer API, and Amazon’s Nova models live on Bedrock. If a specific frontier model is a hard requirement, that requirement picks your cloud for you.
Anthropic is the exception that proves the rule. Claude runs on Bedrock, on Vertex, and on Foundry — with the caveat that feature velocity differs. Anthropic’s newest API capabilities ship on its first-party API and its Anthropic-operated AWS offering first; the partner-operated clouds carry a subset that catches up on their own schedules. The same lag pattern applies to Gemini features between Google’s own API and third-party surfaces. Budget engineering time for those gaps.
On raw breadth, Vertex leads. Model Garden curates 50+ models spanning Gemini, Claude, Google’s open-weight Gemma line, Llama, and Mistral — the widest first-party frontier selection any single console offers. Foundry’s raw catalog is large and growing, but its center of gravity is still OpenAI. Bedrock’s lineup is the most Claude-centric of the three. Note that this same exclusivity fight plays out one layer up in the productivity suite — see our Copilot vs. Gemini Enterprise comparison for that side of the ledger.
Why it matters: compliance decides for regulated shops
For federal agencies, defense contractors, and a growing share of financial services and healthcare buyers, the model comparison is academic until the compliance question clears. Here the gap is stark. Amazon Bedrock holds FedRAMP High, and the June 2026 GovCloud expansion extended High and IL-4/5 coverage to additional models on the platform. Azure OpenAI likewise holds FedRAMP High. Vertex AI’s FedRAMP High authorization, as of mid-2026, is still not generally available.
Read that plainly: a CIO subject to FedRAMP High has a two-horse race today, no matter how well Gemini benchmarks. Google will presumably close this gap — but “presumably” is not a control you can show an auditor. The takeaway for the meeting: compliance authorizations are point-in-time facts, and you buy what is authorized now, not what is promised.
Billing traps to model before you commit
Two Gemini-specific traps deserve a line item in any Vertex evaluation. First, Gemini’s reasoning models bill their internal thinking tokens as output tokens. A hard question that returns a 500-token answer can generate several thousand tokens of hidden reasoning, and you pay output rates for all of it. Teams that estimate cost from visible response length will underestimate — sometimes by multiples.
Second, Gemini Pro uses tiered long-context pricing: once a single prompt crosses roughly 200K tokens, input and output bill at the higher long-context rates — approximately double the standard tier as of mid-2026 (list pricing varies; check current rate cards). Big-context RAG patterns that casually stuff documents into the prompt will cross that line without anyone noticing until the invoice arrives.
Bedrock and Foundry have their own cost sharp edges — provisioned throughput commitments on Bedrock and capacity quotas on Foundry’s frontier models chief among them — but they are capacity-shaped, not token-shaped, and easier to see coming. Whatever platform you pick, benchmark the fully loaded per-request cost in your proof of concept. If that number climbs past your comfort line at production volume, run the math against running your own inference — our self-hosted LLM vs. API cost analysis covers the break-even points.
Where each platform falls short
- Bedrock: no OpenAI frontier models and no Gemini, so shops standardized on either are out. Newer model-vendor API features often arrive on first-party APIs before Bedrock exposes them, and the developer experience still trails the other two consoles.
- Vertex AI: the FedRAMP High gap disqualifies it for regulated US workloads today, Gemini’s billing model punishes sloppy prompt engineering, and Google remains the minority enterprise cloud — you may be adding a third cloud relationship just for AI.
- Azure AI Foundry: capacity quotas on the flagship OpenAI models remain a recurring operational complaint, the broader catalog is newer and less proven than Vertex’s, and the anchor vendor sells the same frontier models direct — Azure exclusivity is a distribution deal, not a technology moat.
What to do about it
Four rules of thumb. One: if you carry FedRAMP High or DoD IL requirements, shortlist Bedrock and Azure OpenAI, and revisit Vertex only when its authorization goes GA. Two: absent a compliance gate or a hard model exclusive, default to the cloud where your data and identity already live — cross-cloud AI adds latency, egress, and a second security review for marginal benefit. Three: build your baseline workloads on a model you can move — Claude’s presence on all three clouds makes it the natural portability hedge — and keep prompts and evals in your own repo, not the vendor’s studio. Four: if data sovereignty is the real driver rather than FedRAMP, weigh the platforms against a private deployment; our private AI on VMware vs. OpenShift AI brief covers that path.
The one-liner for the steering committee: the platforms converged, so buy on model rights and compliance paper — and model Gemini’s token billing before you sign anything.
Frequently asked questions
Is Vertex AI FedRAMP High authorized?
Not as of mid-2026. Vertex AI’s FedRAMP High authorization is in progress but not generally available, which rules it out for US federal workloads and many regulated-industry buyers today. Bedrock and Azure OpenAI both hold FedRAMP High.
Can I run OpenAI models on AWS Bedrock?
Only the open-weight GPT OSS variants, which are available on Bedrock and received FedRAMP High and IL-4/5 approval in GovCloud in June 2026. OpenAI’s proprietary frontier models remain exclusive to Azure and OpenAI’s own API.
Which platform offers the most models?
Vertex AI, on curated frontier breadth — Model Garden spans 50+ models including Gemini, Claude, Gemma, Llama, and Mistral. Azure AI Foundry’s raw catalog is larger by count, but its frontier depth centers on OpenAI plus recent additions like Claude and Grok.
Why do Gemini responses cost more than the visible token count suggests?
Gemini’s reasoning models generate internal thinking tokens that are billed as output even though you never see them. A short answer to a hard question can bill as thousands of output tokens, so estimate cost from measured usage, not response length.
Is Claude available on all three clouds?
Yes. Anthropic’s Claude models are served on AWS Bedrock, Google Vertex AI, and Azure AI Foundry, making Claude the most portable frontier model family — though feature availability differs by platform and typically lands on Anthropic’s first-party API first.
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