MLOps & responsible AI
Amazon SageMaker Feature Store vs Azure ML managed feature store
Amazon SageMaker Feature Store maps to the Azure Machine Learning managed feature store.
Side-by-side comparison
| Aspect | Amazon Web Services | Microsoft Azure |
|---|---|---|
| Service | Amazon SageMaker Feature Store | Azure ML managed feature store |
| Lifecycle | Generally available | Generally available |
| What it is | Online (low-latency latest values) and offline (historical) feature stores, organized as feature groups. | Feature-set specifications with managed materialization to offline and online stores, plus point-in-time joins. |
| Pricing model | Online store billed by read/write units and storage. Offline store billed at S3 rates. | Billed for materialization compute plus underlying storage, and the online store's backing service (for example Azure Cache for Redis). |
| Docs | Amazon SageMaker Feature Store docs | Azure ML managed feature store docs |
| Pricing page | Amazon SageMaker Feature Store pricing | Azure ML managed feature store pricing |
| Exams | MLA-C01 Retiring | AI-300 |
Key differences
- SageMaker organizes features into feature groups with an online store (latest values, ms latency) and/or offline store (history in S3). Feature Processor runs transformation code.
- Azure uses feature-set specifications that the system materializes to offline and online stores on a schedule, with managed transformation pipelines and point-in-time joins.
Pricing summary
Pricing models only. No prices are quoted because they change by region and over time.
- AWS
- Online store billed by read/write units and storage. Offline store billed at S3 rates. Amazon SageMaker Feature Store pricing →
- Azure
- Billed for materialization compute plus underlying storage, and the online store's backing service (for example Azure Cache for Redis). Azure ML managed feature store pricing →
Certification exam mapping
| Exam | Provider | Level | Status |
|---|---|---|---|
| AWS Certified Machine Learning Engineer – Associate MLA-C01 | AWS | Associate | retiring Being updated to MLA-C02 (beta registration opened Sep 1, 2026). Last day for MLA-C01 in English was Sep 28, 2026; Korean, Japanese, and Simplified Chinese remain until MLA-C02 GA. |
| Microsoft Certified – Machine Learning Operations Engineer Associate AI-300 | Azure | Associate | current MLOps and GenAIOps infrastructure on Azure (IaC with Bicep/CLI, GitHub Actions). Closest successor in scope to the retired DP-100. |
Relevance is based on each exam's published scope, not a line-by-line objective match. Check the official study guide.
Verification notes
- Azure online store backing service (Redis) should be confirmed in the managed feature store docs for your region and version.
FAQ
What is the Azure equivalent of Amazon SageMaker Feature Store?
Amazon SageMaker Feature Store maps to the Azure Machine Learning managed feature store.
How does pricing compare between Amazon SageMaker Feature Store and Azure ML managed feature store?
AWS: Online store billed by read/write units and storage. Offline store billed at S3 rates. Azure: Billed for materialization compute plus underlying storage, and the online store's backing service (for example Azure Cache for Redis). Check the official pricing pages for current rates.
Which certification exams cover this topic?
AWS Certified Machine Learning Engineer – Associate (MLA-C01) [retiring]; Microsoft Certified – Machine Learning Operations Engineer Associate (AI-300).