SageMaker vs Google Vertex AI: ML Platform Comparison
Published on February 26, 2026
We deployed 23 production inference endpoints across both AWS SageMaker and Google Vertex AI in the last 18 months. We burned through roughly $15,000 in compute learning exactly where each platform wins.
One client was paying $876/month on an ml.g5.xlarge SageMaker endpoint running 24/7 — serving maybe 100 requests a day. That is roughly $876/month to answer emails from three people. Vertex AI has its own version of this: frictionless BigQuery integration means teams run expensive queries without noticing, and those charges stack on top of the Vertex bill.
Impact: Neither platform is "cheap." The platform you pick matters less than the habits your team builds around it.
If you are picking your ML platform based on a vendor comparison sheet, you are already behind. We have run the same workloads on both platforms and documented the results. Here is the ugly truth from our MLOps engineers at Braincuber.
The Real Problem with Both Platforms
Here is something neither AWS nor Google puts in their keynote slides: both platforms have multi-dimensional pricing that almost nobody fully understands on day one.
What Both Platforms Actually Cost
Small Team (3 jobs/week + 2 endpoints)
$700–$1,500/month depending on platform and FinOps discipline.
Enterprise (10+ production models)
SageMaker: $8,000–$25,000/month. Vertex AI: $7,000–$22,000/month.
The Real Cost Driver
Inference endpoints left idle — not training. This is where budgets die on both platforms.
Who Built What — and Why It Shows
AWS SageMaker (Launched 2017)
First to market. Carries the scars of that seniority. AWS bolted on features year after year — Studio, Canvas, JumpStart, Studio Classic — and the result is a product surface that confuses even experienced ML engineers.
2025 Market Mindshare: 4.8% (down from 7.2% prior year).
Personality: Older, more complex, more battle-tested.
Google Vertex AI (Launched 2021)
Absorbed the older AI Platform and AutoML into one unified interface. Benefits from learning what SageMaker got wrong — and from Google’s genuine AI research pedigree (Transformer architecture, TensorFlow, Gemini).
2025 Market Mindshare: 10.6% (dropped from 20.5% — the whole category is fragmenting).
Personality: Newer, cleaner, more opinionated.
Where They Actually Differ (We Ran the Same Workloads on Both)
AutoML: Transparency vs. Speed
We fed a 100,000-row customer churn dataset (25 features, binary classification) into both:
SageMaker Autopilot
Result: 250 model candidates across 8 algorithms in 4 hours. Best AUC: 0.89. Returned actual notebooks showing every preprocessing step.
Total cost: ~$35
Vertex AI AutoML
Result: Best model in 2.5 hours with AUC 0.87 — but zero visibility into how it got there.
Total cost: ~$28
If you need to explain your model to a compliance officer or a CFO, Autopilot wins by a mile. If you just need a working model fast and your data is already in BigQuery, Vertex is the smarter call.
Custom CNN Training: Distributed Compute Reality
We trained a ResNet-50 on 50,000 labeled images (10 classes):
SageMaker (ml.p3.2xlarge)
Result: Completed in 6 hours. Distributed training across 4 GPUs was straightforward using the built-in framework.
Total cost: $180
Vertex AI (n1-standard-8 + NVIDIA T4)
Result: Completed in 7 hours. Deployment from the Model Registry was one click. Container setup took longer upfront.
Total cost: $165
Costs landed within $15 of each other. The real difference: SageMaker gives you more knobs; Vertex AI deploys faster once the model is trained.
Foundation Model Fine-Tuning: The Generative AI Gap
We fine-tuned Llama 2 7B on 10,000 domain-specific examples:
SageMaker JumpStart
Result: 8 hours on ml.g5.12xlarge. Required manual prompt template configuration and extra setup for inference optimization.
Total cost: ~$250
Vertex AI Model Garden
Result: 6 hours using PEFT with vLLM. One-click deployment with automatic optimization.
Total cost: ~$220
Frankly, if you are building LLM-based applications — RAG pipelines, AI agents, anything touching Gemini — Vertex AI is not even a close race. The native Gemini integration, Agent Engine (now GA), and the Gen AI Evaluation Service make SageMaker + Bedrock feel like two products duct-taped together.
The Feature-by-Feature Breakdown
| Category | AWS SageMaker | Google Vertex AI |
|---|---|---|
| Launch Year | 2017 | 2021 |
| Market Mindshare (2025) | 4.8% | 10.6% |
| AutoML | Autopilot — tabular only, high transparency | AutoML — tabular, image, text, video |
| Model Hub | JumpStart (100s of models) | Model Garden (200+ enterprise-ready models) |
| Foundation Models | Via Amazon Bedrock (separate service) | Native Gemini, Imagen, Veo |
| TPU Access | No | Yes (fewer regions) |
| GPU Access | Comprehensive (A100, H100) | Comprehensive (Nvidia) |
| Multi-Model Endpoints | Native MME + inference components | No native support — custom routing needed |
| Async Inference | Native (SQS → S3) | Batch only, no managed async |
| Data Warehouse | Redshift, Athena, S3 | BigQuery (native, no data copy needed) |
| Billing Granularity | Per second (1-min minimum on some services) | 30-second increments |
| MLflow Integration | Managed | Limited |
| Agent Development | Via Bedrock Agents | Vertex AI Agent Engine (GA) |
| Free Tier | 2-month limited credits | $300 Google Cloud credits (90 days) |
| Monthly Cost (small team) | ~$800–$1,500 | ~$700–$1,400 |
| G2 Ease of Setup | 8.4 | 8.2 |
| G2 High Availability | 9.2 | Not specified |
| Best For | AWS-committed teams, complex MLOps, distributed training | BigQuery users, GenAI apps, teams new to ML infra |
The Insider Detail Nobody Tells You
On SageMaker: The Region Trap
Real story: SageMaker notebooks are region-specific. We have seen teams accidentally launch a GPU instance in us-west-2 when their data pipeline runs in us-east-1. That data transfer cost them an extra $340/month for four months before anyone noticed.
Set CloudWatch billing alerts on Day 1, not Day 90.
On Vertex AI: The Auto-Scaling Whiplash
Real story: The auto-scaling is fast — too fast. Vertex AI can oscillate between replica counts if your thresholds overlap with normal traffic variance. We had a client whose endpoint scaled up and down 17 times in one hour, causing latency spikes during their product demo.
Fix: set longer stabilization windows and a minimum replica count of at least 1.
On Multi-Model Deployments
SageMaker’s inference components have shown up to 8x cost reduction vs. separate endpoints for teams hosting multiple LLMs. Salesforce documented this.
If you are running more than three models in production on Vertex AI today and paying for separate endpoints for each, you are overpaying.
The Controversial Opinion You Will Not Read in a Google Blog Post
Everyone defaults to Vertex AI for GenAI because Gemini lives there. That is fair. But here is the reality: Vertex AI’s MLOps tooling is still catching up to SageMaker for complex production deployments.
SageMaker’s Model Monitor has 8+ years of production refinement. Vertex AI Model Monitoring works — but when a regulated financial services client needs drift detection SLAs documented in an audit, the SageMaker paper trail is longer and deeper.
Do Not Switch Clouds Just Because Gemini Is Impressive
Our direct advice: If your compliance team already signed off on AWS, do not switch to GCP just because Gemini is impressive. The migration will take you 6–12 months for a 50+ model enterprise deployment, and you will underestimate the effort by 50–100%.
(Yes, really. We have watched this happen.)
Which Platform Should You Be On?
Stop treating this like a features debate. The answer is almost always determined by three questions:
Where does your data live? If it is in BigQuery, choose Vertex AI — the native integration alone saves weeks of pipeline work. If it is in S3, choose SageMaker. Fighting data gravity is expensive.
What is your team’s existing cloud expertise? Your team has already climbed a learning curve. Moving from AWS to GCP or vice versa means retraining people and rebuilding pipelines — that is real cost, not just compute cost.
Are you building primarily generative AI or traditional ML? Vertex AI for LLM applications, agents, and Gemini-native products. SageMaker for classification, regression, forecasting, and workloads needing mature MLOps.
The Power Move: Go Cloud-Agnostic
The play: Build cloud-agnostic ML pipelines using MLflow or Kubeflow from the start. Prototype on Vertex AI (easier, cheaper free tier), productionize on SageMaker (more mature monitoring), and serve GenAI via Vertex Model Garden — all without being hostage to either vendor.
That hybrid approach cuts development time by roughly 30%
And reduces compute costs by roughly 20% compared to being locked to a single platform.
The Braincuber Take: AWS Is Our Home, But We Use Both
At Braincuber, we deploy production AI on AWS, GCP, and Azure. Our MLOps clients on AWS get SageMaker because the ecosystem fit is unbeatable — SageMaker Pipelines plugs directly into existing CI/CD workflows, CloudWatch monitoring integrates with the rest of the AWS stack, and SageMaker Savings Plans can cut compute costs by up to 64% on committed workloads.
But if a client is building a Gemini-powered document processing system or running transformer training that benefits from TPUs, we put that on Vertex AI — period.
We do not have brand loyalty to either platform. We have loyalty to your cost-per-inference ratio.
Braincuber Technologies deploys production-grade AI and MLOps pipelines on AWS, GCP, and Azure. We have completed 500+ projects across D2C, fintech, and healthcare — and we work with SageMaker and Vertex AI daily. If your ML platform is costing more than it should, talk to us.
Stop Guessing. Get the Real Numbers.
Book our free 15-Minute MLOps Audit — we will identify your biggest platform spend leak in the first call. No vendor bias. Just the math your cloud bill is hiding from you.
Frequently Asked Questions
Is SageMaker or Vertex AI cheaper?
For small teams, Vertex AI edges out SageMaker by roughly $100–$200/month due to 30-second billing increments and a more generous free tier ($300 credit). For committed enterprise workloads, SageMaker Savings Plans (up to 64% off) can reverse that advantage entirely. The real cost driver on both platforms is inference endpoints left idle — not training.
Can I use SageMaker and Vertex AI together?
Yes, and many mature ML teams do. A common pattern: prototype and run AutoML on Vertex AI (simpler, cheaper free tier), then productionize on SageMaker for mature MLOps and Model Monitoring. The main overhead is managing two authentication systems and paying data transfer fees between AWS and GCP — budget roughly $0.08–$0.09/GB for cross-cloud egress.
Which platform is better for LLM fine-tuning?
Vertex AI wins for anything touching Google’s first-party models (Gemini, Imagen). For open-source LLMs like Llama or Mistral, SageMaker’s inference components give you granular GPU allocation per model and have documented up to 8x cost reduction vs. separate endpoints. Expect to spend roughly $220–$250 per Llama 2 7B training run on either platform.
How long does it take to migrate between platforms?
A single model: 1–2 weeks. A small team with 5 models and basic pipelines: 2–3 months. An enterprise with 50+ models: 6–12 months. Most organizations underestimate migration complexity by 50–100% — especially compliance re-certification, IAM re-architecture, and rebuilding monitoring pipelines from scratch. Do not migrate unless you have a documented 2-year cost savings that justifies it.
Does Vertex AI support asynchronous inference like SageMaker?
No — not natively. SageMaker offers a managed async inference mode via SQS, queuing requests and storing results in S3. Vertex AI only supports synchronous online prediction and batch prediction. If you need async inference on Vertex AI, you have to build your own queue using Pub/Sub and Cloud Functions — that is additional engineering time your team needs to budget for.