Comparison
Vertex AI vs SageMaker
An honest 2026 comparison of Google Vertex AI and Amazon SageMaker (MLOps, model choice, data integration, pricing and lock-in) from a Google Cloud Partner.
Comparison
An honest 2026 comparison of Google Vertex AI and Amazon SageMaker (MLOps, model choice, data integration, pricing and lock-in) from a Google Cloud Partner.
If your organisation runs on, or is moving to, Google Cloud, Vertex AI is the better choice. It is a unified managed MLOps platform with Model Garden (Gemini plus leading open models), tight BigQuery and data integration, an agent builder, and less component glue than assembling an MLOps stack yourself. Amazon SageMaker remains a strong platform for organisations deeply invested in AWS, it is mature, has a broad breadth of MLOps tooling, deep AWS integration and a large install base, so AWS-native teams get end-to-end ML where their infrastructure already lives. The decision follows your cloud: Google Cloud to Vertex AI; AWS to SageMaker.
At a glance
| Feature | Amazon SageMaker | Google Vertex AI |
|---|---|---|
| Deep AWS integration | ✓ Native to AWS | No (Google Cloud native) |
| Platform maturity & tooling breadth | ✓ Mature, very broad MLOps suite | Unified, managed; broad and growing |
| Install base | ✓ Large AWS install base | Large and growing on Google Cloud |
| Unified managed MLOps (one platform) | Many separate components | ✓ Unified pipelines, registry, endpoints |
| First-party foundation models | Bedrock / JumpStart catalogue | ✓ Model Garden, Gemini + open models |
| Data warehouse integration | AWS data stack (Redshift, S3) | ✓ Tight BigQuery integration |
| Agent builder for GenAI agents | Via additional AWS services | ✓ Built-in agent builder + Google ADK |
| Lock-in posture | AWS-centric | ✓ Open models, portable; less lock-in |
| Pricing model | Component + compute usage | Managed + compute usage |
Head to head
Deep AWS integration. If your data, infrastructure and teams already live in AWS, SageMaker connects natively to S3, IAM, Redshift and the rest of the AWS stack with no cross-cloud friction.
Maturity and breadth. SageMaker has been in market longer and offers a very broad set of MLOps tooling, from data labelling to feature store, training, tuning and deployment.
Install base. A large AWS install base means many enterprises already have the skills and infrastructure to run SageMaker end to end without a platform change.
Unified managed MLOps. Vertex AI brings pipelines, model registry, endpoints, feature store and monitoring into one managed platform, less component assembly and glue code than wiring an MLOps stack together.
Model Garden. Google's first-party Gemini models sit alongside a curated set of leading open and third-party models in one managed catalogue.
No lock-in. Open and portable model support means you are not tied to a single proprietary model family, a strategic hedge against vendor lock-in.
BigQuery / data integration. Vertex AI integrates tightly with BigQuery for training data, feature engineering and batch prediction, so teams already in BigQuery build and serve models without moving data out.
Agent builder. A built-in agent builder plus the Google ADK lets you ship GenAI agents grounded on enterprise data in the same platform.
Both platforms bill primarily on compute and managed-service usage, so headline rates are only part of the story. The TCO difference is operational: Vertex AI's unified, managed MLOps reduces the engineering effort of stitching together separate components, and tight BigQuery integration avoids data movement and duplicate storage for teams already on Google Cloud. SageMaker's component breadth is powerful but can mean more pieces to operate and tune. Model choice in Vertex AI Model Garden also lets you right-size to cheaper open models where a frontier model is not required.
Unlike an assistant switch, this is a real platform migration: training pipelines, model registry, endpoints, feature store and MLOps automation are re-platformed onto Vertex AI, and data integration is re-pointed to BigQuery and Google Cloud. Done well, it reduces operational glue and consolidates your ML estate. See the full path on our SageMaker to Vertex AI migration page.
FAQs
For teams on or moving to Google Cloud, yes, unified managed MLOps, Model Garden with Gemini and open models, tight BigQuery integration and an agent builder. SageMaker stays stronger for AWS-native teams.
Yes, tightly, for training data, feature engineering and batch prediction, so teams in BigQuery build models without moving data out.
Both are wide. Vertex AI Model Garden's advantage is first-party Gemini alongside leading open models plus an agent builder in one platform.
It is a real platform migration, pipelines, registry, endpoints, feature store and MLOps re-platformed onto Vertex AI, with data re-pointed to BigQuery. A Google Cloud Partner plans it to minimise rework and downtime.
Decided on Vertex AI? See how the SageMaker to Vertex AI migration works →
Codimite, a Google Cloud Partner, re-platforms your SageMaker pipelines, registry, endpoints and feature store onto Vertex AI workload by workload, with parity verification. Start with a free quote.
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