Azure AI Foundry vs Google Vertex AI
The question that decides it: Which cloud are you already in — and if that is genuinely open, does catalog breadth or the tighter logging default matter more?
Our verdict
Already committed to one cloud: use that one, because the billing, identity and networking integration outweighs everything below. Genuinely choosing: Azure AI Foundry for catalog breadth and a hybrid deployment option, Vertex AI if you want a platform that logs nothing by default.
Why
The honest answer is that this pair is usually decided before anyone compares features. Both are model platforms inside a hyperscaler, and the value of either is that it sits behind the same identity, billing and networking as the rest of your infrastructure. If your data and your team are already in one of these clouds, the other one has to be dramatically better to be worth the split — and it is not.
Their compliance records are close to identical, which is what you would expect at this end of the market. Both publish a SOC 2 report, both will sign a HIPAA business associate agreement, both offer EU data residency, both state a zero-data-retention position, and both state they do not train on customer data. There is no meaningful compliance argument between them, and any vendor claiming otherwise is selling you something.
Two real differences remain. The first is catalog size: Azure AI Foundry states around 10,000 models against Vertex AI's 200. That gap is mostly a difference in what each counts — Azure's figure includes a very long tail of open-weight variants, while Vertex's reflects a curated Model Garden alongside first-party Gemini models — so treat it as a difference in philosophy rather than a fifty-fold advantage. If you want an obscure open-weight checkpoint available first-party, Azure is more likely to have it.
The second is data handling at the margin, and here Vertex is tighter. Vertex records logging nothing at all with a retention figure of zero days. Azure logs metadata only, also with a documented off switch, but publishes no retention figure. Both are strong answers; Vertex's is the stricter default. Azure offsets that with deployment flexibility: it records both managed and self-hosted deployment where Vertex is managed-only, so a hybrid or local footprint is possible on Azure and not on Vertex. Neither charges a separable gateway fee — both bundle inference into the model price — so cost comparison means comparing per-model rates, not platform fees.
Which one, concretely
Choose Azure AI Foundry if
- You are already on Azure for identity, billing and networking
- You want the widest first-party catalog — around 10,000 models
- You need a hybrid or local deployment option as well as managed
- You want a platform that is free to use, paying only for models and compute
Choose Google Vertex AI if
- You are already on Google Cloud
- You want a platform that logs nothing at all, with zero stated retention
- You want first-party Gemini models alongside a curated Model Garden
- You want $300 in credits to evaluate with
What catches people out
- Azure's ~10,000 model figure and Vertex's 200 count different things; it is a difference in curation philosophy, not a fifty-fold gap.
- Azure publishes no retention figure, where Vertex states zero days — Azure logs metadata only, but for an unstated period.
- Vertex AI is managed-only, so there is no hybrid or local deployment option.
- Both bundle inference into model pricing, so neither has a separable gateway fee to compare.
- Neither is a multi-provider gateway: each routes to its own catalog, so cross-cloud failover needs a gateway in front.
- Both have been rebranded recently — Foundry and Gemini Enterprise Agent Platform — so older documentation may use different names.
Side by side
Interpret these fields: How much does an LLM gateway lock you in? · LLM gateway compliance: SOC 2, HIPAA and evidence · How LLM gateway failover actually works
4 of 14 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 | Azure AI Foundry | Google Vertex AI |
|---|---|---|
| Ease of leaving Derived score, higher is easier | 80/80 Some work to leave | 60/80 Some work to leave |
| What kind of product Category | Cloud platform | Cloud platform |
| Who runs it Deployment model | Managed or self-host | Managed only |
| Licence Licence | Proprietary | Proprietary |
| Models available Models available | ~10,000 | ~200 |
| Monthly cost per person Seat fee | None | Not published |
| SOC 2 audited SOC 2 audited | Yes | Yes |
| Will sign a HIPAA agreement HIPAA BAA | Yes | Yes |
| Can keep data in the EU EU data residency | Yes | Yes |
| Does not retain your data Zero data retention | Yes | Yes |
| What gets logged Logged content | Metadata only | Nothing stored |
| You can turn logging off Body-logging opt-out | Yes | Yes |
| How long they keep it Default content retention (days) | Not published | Nothing kept by default |
| Could they train on your prompts Training on customer data | No | No |
| Free tier Free tier | Azure free account gives $200 credit valid 30 days; the Foundry platform itself is free to use and you pay only for the models and compute you deploy. | $300 in free credits for new Google Cloud customers; Grounding with Google Search includes 5,000 free queries per month. |
for Azure AI Foundry and for Google Vertex AI. Want more fields, or a third option in the mix? Open these two in the full comparison tool.
Common questions
Is Azure AI Foundry or Vertex AI better?
Whichever cloud you already run. Their compliance records are near-identical — both publish SOC 2, sign a HIPAA business associate agreement, offer EU data residency and state zero data retention — so integration with your existing identity, billing and networking outweighs every other difference between them.
Does Azure really offer 10,000 models against Vertex's 200?
Those are the stated figures, but they count different things. Azure's includes a long tail of open-weight variants; Vertex's reflects a curated Model Garden plus first-party Gemini models. Read it as a difference in curation philosophy rather than a fifty-fold advantage, and check whether the specific models you need are present.
Can either one route to the other cloud's models?
Not as sold. Each is a platform for its own catalog, so neither gives you cross-cloud failover or a single key across both. If you need to route between Azure and Google models, you want a multi-provider gateway in front of them — which is what the rest of this catalog covers.