Vertex AI vs AWS SageMaker: Which One Should You Choose?

I've spent a significant portion of my career building ML pipelines on both Vertex AI and SageMaker, sometimes simultaneously for the same organization running a multi-cloud strategy. My conclusion after years of production use is that neither is universally better. The right choice depends almost entirely on your existing cloud commitment, your team's operational model, and the specific ML workloads you're running.

This comparison intentionally avoids marketing speak and focuses on the specific technical and operational dimensions where each platform genuinely excels and struggles.

Core Philosophy Difference

SageMaker: Lego Bricks

SageMaker is an extraordinarily feature-rich collection of semi-independent services. SageMaker Studio, Training Jobs, Processing Jobs, Real-time Inference, Async Inference, Serverless Inference, Batch Transform, Feature Store, Model Monitor, Pipelines, Clarify, Ground Truth, Canvas, JumpStart, SageMaker Autopilot, SageMaker Studio Classic... this list represents hundreds of individual product decisions. The philosophy is maximalist: provide a specialized service for every conceivable ML use case and let teams compose what they need.

Vertex AI: Integrated Platform

Vertex AI has fewer overall services but they are more tightly integrated. The data lineage flows automatically from Dataset → Training Job → Model Registry → Endpoint. Metadata is tracked uniformly across steps using Vertex ML Metadata. The UX is more coherent because Google built it as a unit rather than assembling it from acquisitions.

Head-to-Head Comparison

Where Vertex AI Wins Decisively

1. Generative AI and Foundation Models

If your primary use case involves building on top of Google's foundation models (Gemini 2.0 Pro, Gemini 2.0 Flash, Imagen 3, Text Embeddings), Vertex AI is unambiguously the better platform. You get first-party API access with enterprise data governance (no training on your data, VPC peering, CMEK encryption), model fine-tuning, and grounding with Google Search — all in a unified environment. SageMaker's equivalent (Bedrock) is a separate service with a different API, different billing, and much shallower integration with SageMaker Pipelines.

2. BigQuery Integration

If your training data lives in BigQuery (extremely common in Google Cloud-native organizations), Vertex AI's native BigQuery integration is transformative. You can define a Vertex AI Dataset directly from a BigQuery table reference, and training jobs can read directly from BigQuery without any data export step. This eliminates enormous amounts of data pipeline plumbing.

# Direct BigQuery → Vertex AI Training (no data export needed)
dataset = aiplatform.TabularDataset.create(
    display_name="customer-features",
    bq_source="bq://my-project.my_dataset.customer_features",  # Direct BQ reference!
)

# Training job reads directly from BigQuery
job = aiplatform.AutoMLTabularTrainingJob(
    display_name="churn-automl",
    optimization_prediction_type="classification",
)

model = job.run(
    dataset=dataset,
    target_column="churned",
    training_fraction_split=0.8,
    validation_fraction_split=0.1,
    test_fraction_split=0.1,
)

3. TPU Access

Google's Tensor Processing Units (TPUs) provide the best price-performance ratio for training large transformer models, with TPU v5e pods offering ~2x better training throughput per dollar than equivalent A100 clusters for JAX and TensorFlow workloads. This is an exclusive advantage Vertex AI has over every other ML platform.

Where SageMaker Wins Decisively

1. Inference Option Breadth

SageMaker offers deployment patterns Vertex AI doesn't match: Async Inference (for requests that take minutes, decoupled via SQS), Serverless Inference (scale-to-zero, pay per invocation with no idle costs), and Batch Transform (run predictions on entire S3 datasets without a persistent endpoint). Vertex AI's equivalent batch prediction exists but is less battle-tested and feature-complete.

2. Ecosystem and Community Maturity

SageMaker launched in 2017 — four years before Vertex AI. The community depth shows: vastly more StackOverflow answers, GitHub examples, blog posts, and pre-built solution templates. For teams hiring ML engineers, significantly more candidates have SageMaker experience than Vertex AI experience today.

My Recommendation

• Choose Vertex AI if your org is GCP-native, uses BigQuery heavily, or needs first-party Gemini API access with enterprise controls.
• Choose SageMaker if you're AWS-native, need the diversity of inference patterns (async, serverless), or have a team with existing SageMaker expertise.
• If building primarily generative AI applications (LLM-powered products), the Vertex AI + Gemini combination is unmatched in 2026 and the trajectory is strongly in Google's favor.

Conclusion

The honest answer to "Vertex AI or SageMaker?" is: use whichever cloud you're already most invested in. The switching costs between ML platforms are real — team retraining, pipeline rewrites, data migration — and rarely justify moving for marginal feature differences. If you're starting fresh with no existing cloud commitment, evaluate your primary workloads against the comparison table above and choose accordingly.