ML platform (SageMaker AI ↔ Azure ML)
SageMaker AI inference (endpoints) vs Azure ML online & batch endpoints
SageMaker AI endpoints map to Azure Machine Learning managed online endpoints (real-time), batch endpoints, and serverless deployments.
Side-by-side comparison
| Aspect | Amazon Web Services | Microsoft Azure |
|---|---|---|
| Service | SageMaker AI inference (endpoints) | Azure ML online & batch endpoints |
| Lifecycle | Generally available | Generally available |
| What it is | Real-time, serverless, asynchronous, and batch-transform inference options. | Managed online endpoints (real-time, multiple deployments with traffic split), Kubernetes online endpoints, batch endpoints (async jobs on clusters), and serverless (standard) deployments for catalog models. |
| Pricing model | Per-second instance billing for real-time/async endpoints, per-request + duration for serverless, and per-instance time for batch. | VM hours for online deployments, cluster node time for batch jobs, and per-token for serverless model deployments. |
| Docs | SageMaker AI inference (endpoints) docs | Azure ML online & batch endpoints docs |
| Pricing page | SageMaker AI inference (endpoints) pricing | Azure ML online & batch endpoints pricing |
| Exams | MLA-C01 Retiring | AI-300 |
Key differences
- Real-time inference maps to managed online endpoints. Both support multiple deployments behind one endpoint with traffic splitting (blue/green).
- Batch transform maps to batch endpoints (async jobs on compute clusters).
- SageMaker Serverless Inference maps only partially. Azure "serverless/standard deployments" apply to catalog models billed per token, not to arbitrary custom containers.
- SageMaker Asynchronous Inference (queued requests, scale to zero) has no direct Azure ML endpoint type. Batch endpoints or a custom queue are the closest options.
- Inference components and multi-model endpoints on SageMaker don't have a 1:1 Azure feature.
Pricing summary
Pricing models only. No prices are quoted because they change by region and over time.
- AWS
- Per-second instance billing for real-time/async endpoints, per-request + duration for serverless, and per-instance time for batch. SageMaker AI inference (endpoints) pricing →
- Azure
- VM hours for online deployments, cluster node time for batch jobs, and per-token for serverless model deployments. Azure ML online & batch endpoints 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
- The claim about missing async inference and multi-model equivalents is based on the Azure endpoint types documented (online, Kubernetes online, batch, serverless) on 2026-10-19.
FAQ
What is the Azure equivalent of SageMaker AI inference (endpoints)?
SageMaker AI endpoints map to Azure Machine Learning managed online endpoints (real-time), batch endpoints, and serverless deployments.
How does pricing compare between SageMaker AI inference (endpoints) and Azure ML online & batch endpoints?
AWS: Per-second instance billing for real-time/async endpoints, per-request + duration for serverless, and per-instance time for batch. Azure: VM hours for online deployments, cluster node time for batch jobs, and per-token for serverless model deployments. 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).