Short answer to the SageMaker AI vs Azure ML vs Vertex AI: if your data and infrastructure already live in AWS, Azure, or Google Cloud, that’s usually the deciding factor before any feature comparison matters. But one thing needs saying first — “Google Vertex AI” as a standalone product name no longer exists as of May 2026. It’s been folded into the Gemini Enterprise Agent Platform, and most comparisons written even a few weeks ago haven’t caught up to that yet. Amazon SageMaker AI (renamed from SageMaker in December 2024) remains AWS’s end-to-end platform for building, training, and deploying models. Azure Machine Learning is Microsoft’s dedicated MLOps platform, now positioned alongside — not inside — the broader Microsoft Foundry umbrella. Here’s what actually changed, what each platform does well, and which one fits your team.
At a Glance
| Platform | Current name (2026) | Best for | Pricing model | MLOps maturity |
|---|---|---|---|---|
| Amazon SageMaker AI | Amazon SageMaker AI (renamed Dec 2024) | AWS-native teams wanting deep infrastructure control | Compute + managed-service markup, roughly 15–40% over raw EC2 | Mature — Pipelines, Feature Store, Model Registry |
| Azure Machine Learning | Azure Machine Learning (integrated alongside Microsoft Foundry) | Microsoft-standardized enterprises, regulated industries | Compute-based, platform layer largely unbundled | Mature — strong AutoML, visual designer |
| Vertex AI | Gemini Enterprise Agent Platform (renamed May 2026) | Google Cloud-native teams, especially agent-first workloads | Per-component metering — training, prediction, pipelines billed separately | Reorganized under an agent-first hierarchy — model tools now a “Models” sub-menu |
What Actually Changed in 2026
If you’ve read a “SageMaker vs Azure ML vs Vertex AI” comparison anytime before this summer, part of it is already wrong. The most consequential change: at Google Cloud Next 2026 in April, Google announced it was folding Vertex AI into a new Gemini Enterprise Agent Platform, and by May 21, 2026, the migration was complete — Vertex AI no longer appears anywhere in the Google Cloud Console, and searching for it redirects you to Agent Platform. Model Garden, Custom Training, AutoML, the Model Registry, and Pipelines all still work exactly as before; they’re just reorganized under a “Models” sub-menu inside an agent-first hierarchy. Existing API calls keep working unchanged. Separately, Azure’s older Machine Learning SDK v1 reaches end of support on June 30, 2026 — if your team is still running v1 pipelines, that’s a harder deadline than any branding change. Amazon’s side has been comparatively quiet since its December 2024 SageMaker AI rename.
Amazon SageMaker AI
SageMaker AI remains the deepest, most infrastructure-controllable option of the three, which is exactly why AWS-native teams with real engineering capacity tend to prefer it. Its strength is breadth: built-in algorithms, Feature Store, Pipelines, and Inference Pipelines that chain up to 15 containers for pre- and post-processing around a model call. Two trade-offs people underestimate: the operational tax — IAM policies, VPC design, and endpoint management add real setup time for small teams — and the pricing markup, since SageMaker’s managed instances typically run 15–40% above equivalent raw EC2 compute cost, which compounds fast on always-on endpoints. If your team already runs deep in AWS and has the engineering depth to manage that complexity, it remains a strong default.
Azure Machine Learning
Azure Machine Learning is Microsoft’s dedicated platform for training and deploying custom models — full MLOps: pipelines, AutoML, a visual designer for less code-heavy teams, and mature model versioning. As of 2026 it sits alongside Microsoft Foundry rather than inside it: Foundry handles the generative-AI and agent side — prompt orchestration, model catalog, agent deployment — while Azure Machine Learning stays the tool for training models from scratch on structured data: churn prediction, demand forecasting, custom computer vision. For Microsoft-standardized enterprises already using Entra ID, Purview, and Power BI, that’s a real integration advantage the other two platforms can’t match natively. The trade-off: teams with no existing Azure footprint rarely choose it as a green-field starting point.
Vertex AI (Now the Gemini Enterprise Agent Platform)
This is the platform most in flux, so it’s worth being precise. What you know as Vertex AI — Model Garden, Custom Training, AutoML, Model Registry, Endpoints, Pipelines — is now organized under a “Models” section inside the Gemini Enterprise Agent Platform, with a separate “Agents” section handling the newer agent-building tools: Agent Garden, ADK, Agent Engine, Memory Bank. The API endpoint is unchanged, so existing code doesn’t break. What’s actually new is the hierarchy — Google restructured the platform around agents as the primary unit of work, with model training now a sub-feature rather than the main event. For teams building traditional ML models, the practical experience is largely the same tools under a new roof. For teams building multi-agent systems, there’s genuinely more here now than Vertex AI offered a year ago.
Which Should You Choose?
Choose Amazon SageMaker AI if you’re AWS-native, need deep infrastructure control, and have the engineering depth to manage IAM, VPC, and endpoint configuration without it becoming a full-time job. Choose Azure Machine Learning if you’re standardized on Microsoft’s ecosystem — Entra ID, Purview, Power BI — and need mature MLOps for structured-data models specifically, separate from your generative-AI work in Foundry. Choose the Gemini Enterprise Agent Platform (what you’d have called Vertex AI a few months ago) if your data already lives in BigQuery and Google Cloud, or if agent orchestration — not just model training — is where your roadmap is heading. None of this should be decided on feature checklists alone: SageMaker’s managed markup, Azure’s compute-plus-services model, and Vertex’s per-component metering can each swing your actual bill by double digits depending on usage pattern, regardless of which platform “wins” on paper.
Who Wrote This
Triotech Systems is a Toronto-based managed DevOps cloud security company running production infrastructure across AWS, Azure, and GCP for fintech, healthcare, and crypto clients since 2020, including the MLOps pipelines and model deployment work that sits underneath comparisons like this one. As a managed DevOps cloud security company in Canada, we’re not reselling any of these three platforms — this reflects what we’ve actually seen hold up, and break, in production, not a vendor briefing.
Talk to an Engineer
Talk to a managed DevOps cloud security company that actually operates on all three of these platforms — reach Triotech Systems at +1 431-430-8746 or through our contact page, and we’ll help map the right one to your existing infrastructure before you commit.
Frequently Asked Questions
Is Vertex AI still called Vertex AI?
No. As of May 21, 2026, Vertex AI no longer appears in the Google Cloud Console. It was folded into the Gemini Enterprise Agent Platform, announced at Google Cloud Next 2026. Existing APIs and code keep working unchanged — only the name and console structure changed.
When did SageMaker become SageMaker AI?
Amazon reorganized SageMaker under the name Amazon SageMaker AI in December 2024, part of a broader push to unify AWS’s data, analytics, and AI tooling.
Is Azure Machine Learning part of Microsoft Foundry now?
It’s listed among Foundry’s integrated services, but it remains a distinct product for classic MLOps — training custom models on structured data — separate from Foundry’s generative-AI and agent tools. Its SDK v1 reaches end of support June 30, 2026, so teams still on v1 should migrate regardless of the Foundry question.
Which platform is cheapest?
None of them reliably, once you look past headline compute rates. SageMaker marks up managed instances over raw EC2 pricing, Azure ML bills compute plus accumulating service fees, and Vertex AI meters training, prediction, and pipelines separately. Idle endpoints and cross-cloud data egress typically matter more than list price.
Can we use more than one of these?
Yes, and many enterprises do — typically a primary platform matching their core cloud commitment, with a secondary platform for a specific capability gap the primary doesn’t cover well.
