Vertex AI vs SageMaker - how Google’s and AWS’s managed ML platforms compare
If your decision is specifically about where to run model inference rather than which platform to standardise on, our guide to LLM inference on AWS - Bedrock vs SageMaker vs self-hosted EKS covers that narrower question. Here we compare the two platforms as a whole.
First, the name change you need to know about
Before comparing anything, one fact clears up a lot of confusion. At Google Cloud Next in April 2026, Google retired “Vertex AI” as a standalone brand and renamed it the Gemini Enterprise Agent Platform. The former Vertex AI capabilities - Model Garden, custom training, AutoML, the model registry, endpoints, and pipelines - all continue, now delivered under the Agent Platform rather than as a product called Vertex AI, and that is where the roadmap moves from here.
For this comparison we still say “Vertex AI”, because that is what people search for and what most existing docs and tutorials call it. Just know that when you open the Google Cloud console today, the platform wears the new name. The underlying ML platform is the same one; the brand around it changed and expanded towards agents.
Sources we reference: the Gemini Enterprise Agent Platform (formerly Vertex AI) product page and Amazon SageMaker.
The platforms in one line each
Both cover the full ML lifecycle and integrate deeply with their own cloud. The difference is heritage and emphasis.
- SageMaker (AWS, since 2017) is the older and broadest of the two, with the deepest AWS integration and the largest surface area of features. Its newer front end, SageMaker Unified Studio, pulls data, analytics, ML, and generative AI into one environment.
- Vertex AI (Google, since 2021, now the Gemini Enterprise Agent Platform) is younger, known for strong managed AI services, tight integration with Google’s data stack (BigQuery) and open-source ML tooling, and a Model Garden that spans Google’s own and third-party models. The 2026 rebrand repositioned it around agents.
Head to head
| Vertex AI (Gemini Enterprise Agent Platform) | Amazon SageMaker | |
|---|---|---|
| Launched | 2021, rebranded 2026 | 2017 |
| Best ecosystem fit | Teams on Google Cloud, especially with data in BigQuery | Teams on AWS, especially with data in S3/Redshift |
| Model access | Model Garden: Gemini, open-weight, and third-party models (including Claude) | SageMaker JumpStart plus Bedrock for managed foundation models |
| Feature breadth | Strong managed/AutoML and agent tooling | Broadest overall feature set, longest track record |
| Direction of travel | Repositioned around agents (Agent Platform) | Unifying data + AI via Unified Studio |
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Cost shape: where the money actually goes
Headline compute rates are close enough that they rarely decide the platform. The real cost difference comes from the shape of the bill, and both platforms share the same traps.
- Idle endpoints bill anyway. On Vertex AI you pay for each model deployed to an endpoint even when it serves no predictions - you have to undeploy to stop the charge. SageMaker endpoints behave the same way. Forgotten endpoints are the most common surprise on both.
- Model Garden is not one price list. On Vertex AI, third-party models like Claude bill at the provider’s rate, open-weight models bill per token at a Google-set rate, and self-deployed models bill by the GPU-hour. “Which model” changes the cost model, not just the price.
- Committed-use discounts matter more than headline rates. Google Cloud committed-use discounts can cut a predictable Vertex AI bill substantially; AWS Savings Plans do the same for SageMaker. On both, the discount strategy moves the bill more than the per-unit rate.
- Egress and MLOps service fees are the quiet third cost on both platforms, and the main reason effective costs diverge from the headline compute rate.
For the Google-side discount mechanics, see our write-up on GCP cost optimisation. For how to keep a shared ML/data bill attributable per team, the approach in BigQuery cost optimisation transfers directly.
When to pick which
| Lean Vertex AI when... | Lean SageMaker when... |
|---|---|
| Your data already lives in Google Cloud, especially BigQuery | Your data and infrastructure already live on AWS |
| You want Gemini models and a Model Garden spanning several providers | You want the broadest feature set and the deepest AWS integration |
| You are building around agents and want Google's agent-first direction | You want one environment across data and AI via Unified Studio |
| Your team already runs on GKE and Google's open-source ML tooling | Your team already runs on AWS compute and tooling |
The honest summary: for most teams the deciding factor is which cloud your data and people already live on, not a feature scorecard. Both platforms are capable end to end; the integration tax of running ML on the cloud you are not otherwise using is usually larger than any feature gap between them. Where it is genuinely a greenfield choice, Vertex AI’s edge is Google’s data and agent story, and SageMaker’s edge is breadth and AWS depth.
When it is worth bringing in an external partner
- Cross-cloud ML strategy. If data sits in one cloud and the team knows the other, the integration cost is real and worth costing out before committing.
- Cost modelling. Endpoint, token, and egress costs on both platforms are easy to underestimate; a realistic model before rollout avoids the idle-endpoint surprise.
- Migrations between platforms. Moving an ML workload from SageMaker to Vertex AI (or back) is a project, not a config switch.
- The rebrand transition. If you have existing Vertex AI tooling, scripts, or docs, mapping them onto the Gemini Enterprise Agent Platform is worth planning rather than discovering piecemeal.
Summary
Vertex AI and SageMaker are both full-lifecycle managed ML platforms; the right one is mostly decided by which cloud you already run on.
Three things to remember:
- Vertex AI is now the Gemini Enterprise Agent Platform. The ML platform is the same; Google retired the brand in 2026 and repositioned it around agents. Search still uses “Vertex AI”, so that is what you will find in docs and tutorials.
- Ecosystem fit beats the feature scorecard. Pick the platform matching where your data and team already live; the integration tax of the other cloud usually outweighs any feature gap.
- The cost shape is the same trap on both. Idle endpoints bill anyway, model choice changes the cost model, and committed-use discounts move the bill more than headline rates.
Choosing between Vertex AI and SageMaker?
Book a free 30-minute call. We help teams pick the right managed ML platform for the cloud they already run on, model the real endpoint and token costs, and plan migrations between them.
