GCP vs AWS for ML Workloads in 2026: Vertex AI vs SageMaker (Honest Comparison)

Key Takeaways

• TPU v5p on GCP runs ~$4.20/hr on-demand; equivalent H100 on SageMaker runs $8.60–$10.80/hr — 2×+ gap, narrowed after AWS's 44% H100 price cuts in late 2025 but not gone.
• Existing data infrastructure determines 70% of decisions: BigQuery data = Vertex AI's native integration eliminates ETL; S3/Redshift data = SageMaker's ecosystem cohesion is harder to replicate.
• Teams overestimate TPU savings: hardware discount real but engineering tax significant — JAX rewrites non-trivial, PyTorch won't run as-is, teams burn 3–4 weeks on migration before data pipeline becomes bottleneck.
• Gemini 2.0 Flash at $0.075/million tokens vs Claude Sonnet at ~$3.00/million = 30–40× cost difference; 10B tokens/month = $750 with Gemini vs $30,000+ with Claude.
• Vertex AI Pipelines = easier to start/debug; SageMaker Pipelines = harder setup but more operational control at enterprise scale, 8+ years Model Monitor refinement, regulated audit trails Vertex can't match.
• Computer vision team migrating SageMaker→Vertex AI training got 38% cost reduction from TPU v5p pricing + utilization, but kept inference on SageMaker — Lambda serving stack would cost more to move than keep.
• You don't need one cloud for everything: training on GCP + inference on AWS is messier, but the cost difference justifies cross-cloud architecture, as the CV team example shows.

Introduction

You are about to spin up a serious ML training run. The GPU bill alone will be thousands of dollars. Before you pick a cloud, here is the number that tends to settle the argument quickly: a single TPU v5p chip on Google Cloud runs approximately $4.20/hr on-demand, while an equivalent H100 on AWS SageMaker (via p5 instances) runs closer to $8.60–$10.80/hr per accelerator depending on region and configuration. That is not a small gap. But price alone does not tell the full story.

Over the past 18 months, workloads have been moved between these platforms for clients running computer vision pipelines, LLM fine-tuning runs, and real-time inference APIs. This guide documents what actually happens in production — not what the marketing pages say.

AI & Machine Learning Development Services at Agile Soft Labs has run these migrations and built production ML systems on both platforms. This comparison is grounded in that deployment experience.

Training Cost: TPU v5p vs. H100/H200 on SageMaker

All prices as of mid-2026, on-demand rates in us-central1 (GCP) and us-east-1 (AWS):

Accelerator	Platform	On-Demand $/hr	1-yr Committed $/hr	Notes
TPU v5p (per chip)	Vertex AI	~$4.20	~$2.94	Min pod: v5p-8 ($33.60/hr)
TPU v5p-8 pod	Vertex AI	~$33.60	~$23.52	8 chips, ~460 TFLOPS
H100 SXM (p5.48xlarge / 8x)	SageMaker	~$98.32/hr total	~$54–$64/hr	$12.30/chip; AWS cut prices 44% in 2025
H200 SXM (p5e, 8x)	SageMaker	~$82/hr total	~$56/hr	25% price cut post-2025
A100 80GB (p4de, 8x)	SageMaker	~$40/hr total	~$22/hr	Still widely used for fine-tuning

Two things stand out from the current pricing. First, the H100 price cuts AWS announced in late 2025 (up to 44%) dramatically changed this picture — a year ago the gap was even starker. Second, TPUs and GPUs are not directly comparable. A v5p-8 pod does not perform identically to 8 H100s for every workload. JAX/XLA-native training on TPUs can be significantly faster for transformer models that map cleanly onto the architecture. For workloads with irregular compute patterns or heavy use of custom CUDA kernels, GPUs almost always win.

Vertex AI SDK Training Job

from google.cloud import aiplatform

aiplatform.init(project="my-project", location="us-central1")

job = aiplatform.CustomTrainingJob(
    display_name="llm-finetune-v5p",
    script_path="train.py",
    container_uri="us-docker.pkg.dev/vertex-ai/training/pytorch-tpu.2-3:latest",
    requirements=["transformers==4.41.0", "datasets"],
)

model = job.run(
    machine_type="ct5p-hightpu-8t",   # TPU v5p-8
    accelerator_count=0,               # machine_type encodes the TPU config
    replica_count=1,
    args=["--epochs=3", "--batch_size=64"],
)

SageMaker Equivalent

import sagemaker
from sagemaker.pytorch import PyTorch

sess = sagemaker.Session()

estimator = PyTorch(
    entry_point="train.py",
    role="arn:aws:iam::123456789:role/SageMakerRole",
    instance_type="ml.p5.48xlarge",   # 8x H100
    instance_count=1,
    framework_version="2.3",
    py_version="py311",
    hyperparameters={"epochs": 3, "batch_size": 64},
)

estimator.fit({"train": "s3://my-bucket/train-data/"})

The Contrarian Take on TPUs

Most teams overestimate how much they will save on TPUs. The hardware discount is real — but the engineering tax is significant. JAX rewrites are non-trivial. Existing PyTorch code will not run as-is. XLA compilation adds iteration time during development. Teams commonly burn 3–4 weeks of engineer time on the migration and then underutilize the TPU pods because the data pipeline became the bottleneck.

For fine-tuning runs under 100B parameters where your team already lives in PyTorch, SageMaker with H200 instances is often the pragmatic choice — especially with the new post-2025 pricing.

Cloud Development Services manages the infrastructure configuration for both training environments — TPU pod allocation and XLA optimization on GCP, and p5/p5e instance configuration and DeepSpeed/FSDP setup on SageMaker — including the cross-cloud networking when training and inference run on different clouds.

Inference Cost: Vertex AI Endpoints vs. SageMaker Endpoints vs. Bedrock

Inference economics are messier than training because utilization patterns vary so much between production deployments.

Vertex AI Prediction Endpoints charge per vCPU-hour and per accelerator-hour, with a minimum of roughly $7/day (~$210/month) even at zero traffic. The dedicated endpoint keeps a node warm. Serverless prediction helps for low-traffic models where available, but not all model types qualify for serverless deployment.

SageMaker Endpoints have a similar always-on cost structure for real-time endpoints. The key differentiator: SageMaker's Multi-Model Endpoints let you host hundreds of models behind one endpoint, slashing per-model idle costs by up to 8× for teams with diverse model portfolios. SageMaker Serverless Inference scales to zero — a genuine advantage for development environments or infrequently called production models.

For foundation model inference specifically, the decision depends on which model family you need:

• Steady, high-volume, custom model: Vertex AI Dedicated Endpoints with committed-use discounts are price-competitive
• Bursty or multi-model portfolio: SageMaker Multi-Model Endpoints or Serverless inference edge out
• Foundation model API calls: Bedrock (Claude) or Vertex (Gemini Flash) depending on which model family matters more for your use case

MLOps: Vertex AI Pipelines vs. SageMaker Pipelines

This is where the platforms diverge most visibly in day-to-day engineering use.

Vertex AI Pipelines is built on the Kubeflow Pipelines SDK. Pipelines are defined as Python functions decorated with @component, compiled to an intermediate YAML spec, and run on managed infrastructure. The DAG visualization in Cloud Console is genuinely better than anything AWS offers out of the box. Debugging a failed pipeline step typically means clicking into the node and reading logs, not hunting through CloudWatch filter expressions. The portability argument is also real — Kubeflow pipelines can theoretically run on any Kubernetes cluster, not just Google's managed infrastructure.

SageMaker Pipelines is more tightly integrated with the AWS ecosystem. EventBridge triggers, Lambda hooks, and CodePipeline integrations make it the right choice when the ML workflow is one piece of a larger AWS data platform. Model Monitor has eight-plus years of production refinement and supports regulated-industry audit trails. Healthcare and financial services teams frequently need exactly that depth. Vertex AI Model Monitoring works, but audit trail depth is not yet at parity.

Honest verdict: Vertex AI Pipelines is easier to start with and easier to debug. SageMaker Pipelines is harder to configure initially but offers more operational control at enterprise scale. For a team of 3–5 data scientists who want to ship fast, Vertex wins. For a 50-person ML platform team embedded inside a larger AWS organization, SageMaker's integrations are difficult to replicate elsewhere.

CareSlot AI healthcare ML deployments and AI-Powered Loan Management Software fintech systems stay on SageMaker specifically for Model Monitor's regulated-industry audit trail depth — HIPAA and financial compliance requirements demand the kind of inference monitoring documentation that SageMaker's mature tooling provides out of the box.

A concrete example: a fintech client had already built their alerting, compliance logging, and CI/CD entirely on AWS. Moving to Vertex Pipelines would have meant rebuilding those integrations from scratch. The decision was to stay on SageMaker and focus engineering time on optimizing instance selection instead of rebuilding operational infrastructure.

Data Layer: BigQuery + Vertex vs. S3 + SageMaker Feature Store

This section often determines the platform decision more than accelerator pricing.

If your data already lives in BigQuery, the Vertex AI integration is the strongest data-to-AI link available from any cloud provider. You can register a BigQuery table as a Vertex dataset, start a training job that reads directly from BigQuery without ETL, and have the trained model registered in the Model Registry — all within a single SDK session:

from google.cloud import aiplatform

dataset = aiplatform.TabularDataset.create(
    display_name="customer-churn-dataset",
    bq_source="bq://my-project.my_dataset.churn_table",
)

# Dataset is ready for training — no export, no ETL, no intermediate storage

No S3-equivalent dumps. No Glue jobs. No Athena prep work. The BigQuery-to-Vertex path eliminates an entire layer of data engineering infrastructure that S3-based training pipelines require.

SageMaker Feature Store is a mature product with both online (low-latency, sub-10ms) and offline (S3-backed) feature stores. For teams building real-time prediction pipelines where feature freshness matters at inference time, the online feature store's latency characteristics are a genuine differentiator. Vertex AI's Feature Store has been redesigned multiple times and is now competitive, but SageMaker's has more battle-tested production deployments at scale.

The practical rule: if you are a GCP shop with data in BigQuery, Vertex AI is the obvious answer and the integration dividend alone often justifies it. If you are AWS-native with data in S3 or Redshift, SageMaker's ecosystem cohesion is hard to replicate.

IoT Development Services sensor data pipelines illustrate where this matters most — IoT telemetry flowing into BigQuery via Pub/Sub connects directly to Vertex AI training runs with no intermediate ETL, while the equivalent AWS path requires Kinesis → S3 → Glue → SageMaker data channel configuration that adds latency and operational complexity.

Foundation Model Access: Gemini 2.x vs. Claude/Llama on Bedrock

This landscape shifted significantly in 2025–2026. Here is the current state:

Capability	Vertex AI	Bedrock	SageMaker JumpStart
Gemini 2.x	Yes (native)	No	No
Claude (Anthropic)	No	Yes	Partial (via Bedrock)
Llama 3	Yes	Yes	Yes
Fine-tune proprietary models	Limited	No	Yes
Context caching discount	Yes (90% off)	No	N/A
Batch API discount	Yes (50% off)	Yes	N/A

Vertex AI Model Garden is Gemini-first. Gemini 2.0 Flash is the production workhorse: fast, multimodal, 1M token context window, $0.075/million input tokens. Gemini 2.0 Pro handles complex reasoning tasks. Llama 3 and Gemma 3 are also available via Model Garden. Notable absence: Claude is not available on Vertex AI. If you need Anthropic's model family, you use the Anthropic API directly, which means losing GCP-native security tooling and VPC network controls.

Amazon Bedrock has the widest catalog: Claude Sonnet 4.5, Claude Opus 4, Haiku 3.5, Llama 3.x, Mistral, Amazon Nova, and as of early 2026 several open-weight OpenAI models. Bedrock's unified API surface is clean, and the model-switching story is genuinely good — swapping from Claude Sonnet to Nova for cost optimization is a one-line change.

The cost difference is significant for high-volume workloads: Gemini Flash at $0.075/million input tokens versus Claude Sonnet at approximately $3.00/million represents a roughly 30–40× difference. A team processing 10 billion tokens per month pays approximately $750 with Gemini Flash versus $30,000+ with Claude Sonnet — not a rounding error in any engineering budget.

AI Workflow Automation enterprise deployments use exactly this tiered foundation model selection — Gemini Flash for high-volume routine extraction and classification tasks, Claude Sonnet for complex reasoning and generation tasks that require the higher capability ceiling, keeping blended per-token costs well below what single-model approaches produce.

Lock-in and Portability

Both platforms pull you in if you let them.

Vertex AI's lock-in is real but often underappreciated. Custom training containers work on any Kubernetes cluster. Vertex Pipelines (Kubeflow SDK) are theoretically portable. But the moment you start using BigQuery ML, Vertex Feature Store, or Gemini's multimodal APIs, you are making bets on Google infrastructure that are not easy to move off.

SageMaker is arguably more locked in at the MLOps layer. SageMaker Experiments, SageMaker Pipelines, and the Model Registry are all proprietary services. Moving a SageMaker pipeline to another platform means rewriting it from scratch in a different framework. The flip side: if you are already deeply embedded in AWS (EKS, RDS, Kinesis), the operational overhead of adding a second cloud provider often exceeds the theoretical portability benefit.

Practical mitigation for either platform: keep training code framework-agnostic (PyTorch or JAX, not platform-specific wrappers), use MLflow for experiment tracking (both platforms support it natively), and store models in standard formats (ONNX, SavedModel) in your own storage layer rather than in platform-specific model registries.

Decision Matrix: When to Pick Each

Choose Vertex AI if:

• Your data platform is BigQuery-centric and ETL elimination has direct cost value
• You are building generative AI features with Gemini natively and cost-per-token matters
• Your team wants faster pipeline iteration with less infrastructure management overhead
• Cost is the primary concern for high-volume transformer training where TPU fits your workload
• You are a startup or a team of fewer than 10 ML practitioners

Choose SageMaker if:

• Your existing data infrastructure lives on AWS (S3, Redshift, Kinesis)
• You need tight integration with AWS services (EventBridge, Lambda, CodePipeline)
• You are in a regulated industry (healthcare, fintech) requiring deep audit trails and compliance documentation
• You host a large, diverse model portfolio that benefits from Multi-Model Endpoints
• Your ML engineers are already fluent in the AWS ecosystem

The CV team case study: A computer vision team switched training from SageMaker to Vertex AI, moving image datasets from S3 to GCS and restructuring around Cloud Storage and Vertex Training. The result was a 38% reduction in training costs — driven by TPU v5p pricing and better utilization from Vertex's managed autoscaling. The migration took six weeks, with a payback period under three months. Their inference API stayed on SageMaker because it was tightly coupled to an existing Lambda-based serving stack that would have cost more to move than to keep.

The lesson: you do not have to pick one cloud for everything. Running training on GCP and inference on AWS is operationally messier, but sometimes the numbers justify the architecture.

Explore AgileSoftLabs case studies for ML platform migration outcomes across computer vision, NLP, and real-time inference systems — including the cross-cloud architectures where training and inference run on different platforms for cost optimization. EngageAI e-commerce personalization platform uses Vertex AI for recommendation model training (BigQuery-native product catalog and user event data) while keeping real-time inference endpoints on AWS to serve low-latency predictions through the existing CloudFront + Lambda@Edge serving stack.

Ready to Choose Your ML Platform?

Neither Vertex AI nor SageMaker is the universal winner. Google Cloud's TPU pricing and BigQuery integration make it the more cost-effective choice for teams running large-scale transformer training who are already in the GCP ecosystem. SageMaker wins on MLOps maturity, ecosystem depth for AWS-native organizations, and foundation model diversity through Bedrock. Where your data lives is where you should probably train — and only deviate when the cost difference is large enough to justify the migration work.

AgileSoftLabs has run ML platform migrations and built production ML systems across both GCP and AWS. Explore the full AI and cloud services portfolio or contact our team for a workload-specific cost model before you commit to infrastructure.

Frequently Asked Questions

1. Which is cheaper for ML in 2026: Vertex AI or SageMaker?

GCP Vertex AI is 28% cheaper for training ($612 vs $847 for 72-hour run) and 35% cheaper for inference. Best for cost-sensitive startups and new AI/ML projects. However, if your data is already in AWS, egress costs can eliminate savings.

2. Is GCP or AWS better for machine learning in 2026?

GCP wins for developer experience, AutoML, TensorFlow, TPUs, and data analytics. AWS wins for enterprise control, marketplace breadth, pipelines, Feature Store, and talent availability. Choose based on your workflow and needs. No universal winner.

3. What are the pros and cons of Vertex AI vs SageMaker?

Vertex AI: easier to use, AutoML, TensorFlow, TPUs, Kubernetes-native (limited customization). SageMaker: maximum control, marketplace, pipelines, Feature Store, enterprise-ready (needs DevOps expertise). Choose speed vs control.

4. When should I choose GCP vs AWS for ML workloads?

Choose GCP if starting fresh, cost matters, or data analytics is core. Choose AWS if data is already in AWS, need granular control, or require enterprise governance. Match platform to ecosystem and needs.

5. What is better for ML: GCP Vertex AI or AWS SageMaker in 2026?

Vertex AI = faster experiment→production, AutoML, serverless, better for prototyping. SageMaker = maximum control, enterprise-ready, production-grade, better for complex workflows. Choose based on speed vs control priority.

6. How much cheaper is GCP Vertex AI compared to AWS SageMaker?

Real 2026 test: 28% cheaper training ($612 vs $847) and 35% cheaper inference on GCP. Saves $235 per 72-hour run. But egress costs from AWS can negate savings. Calculate total costs including data transfer.

7. What are the key differences between Vertex AI and SageMaker ecosystems?

SageMaker: massive marketplace, pipelines, Feature Store, enterprise-ready. Vertex AI: AutoML, TensorFlow, TPUs, Kubernetes-native, developer-friendly. AWS = mature scale, GCP = innovation speed.

8. Which platform has better developer experience: Vertex AI or SageMaker?

GCP Vertex AI = easier to use, serverless, faster experiment→production, data teams feel at home. AWS SageMaker = more complex, needs DevOps, but maximum control. Choose speed (GCP) or control (AWS).

9. What hardware differences exist between Vertex AI and SageMaker?

GCP = TPUs (optimized for TensorFlow, deep learning). AWS = GPUs (general-purpose, broader framework support). Both high performance. TPUs faster for TensorFlow; GPUs more flexible for multi-framework.

10. What is the final verdict for GCP vs AWS ML in 2026?

No universal winner. GCP = cost-effective, developer-friendly, AutoML, data analytics. AWS = enterprise control, marketplace, DevOps, talent pool. Choose GCP for new projects/cost, AWS for embedded ecosystem/control. Match to your needs.