What’s the difference between SageMaker Serverless and SageMaker Real-Time Endpoints?

Understanding the Difference Between SageMaker Serverless and SageMaker Real-Time Endpoints

Introduction

Amazon SageMaker provides multiple deployment options for AI inference as a service, allowing businesses to deploy machine learning (ML) models efficiently. Two key deployment choices are SageMaker Serverless Inference and SageMaker Real-Time Endpoints. While both serve AI inference workloads, they cater to different use cases based on cost, scalability, and performance requirements.

This knowledge base explores the differences between these two deployment options, their benefits, limitations, and ideal use cases—helping you choose the best approach for your AI inference as a service needs.

1. Overview of SageMaker Real-Time Endpoints

1.1 What Are SageMaker Real-Time Endpoints?

SageMaker Real-Time Endpoints are designed for low-latency, high-throughput AI inference workloads. They provide a persistent, always-available endpoint where ML models serve predictions in real time.

1.2 Key Features

Persistent Infrastructure: Dedicated compute instances (CPU/GPU) remain active to handle requests.

Low Latency: Optimized for applications requiring immediate responses (e.g., fraud detection, chatbots).

Auto-Scaling: Automatically adjusts capacity based on traffic.

Customization: Supports instance type selection (ml.m5.xlarge, ml.g4dn.xlarge, etc.).

High Availability: Deploys across multiple Availability Zones (AZs) for fault tolerance.

1.3 Use Cases

Real-time applications (e.g., recommendation engines, voice assistants).

High-traffic AI inference as a service where latency is critical.

Applications needing consistent performance (e.g., financial trading models).

1.4 Limitations

Cost: Continuous instance usage incurs charges even during idle periods.

Over-Provisioning Risk: Requires careful capacity planning to avoid unnecessary costs.

2. Overview of SageMaker Serverless Inference

2.1 What Is SageMaker Serverless Inference?

SageMaker Serverless Inference is a pay-per-use deployment option where AWS manages the underlying infrastructure, automatically scaling resources based on demand.

2.2 Key Features

No Infrastructure Management: AWS handles provisioning, scaling, and maintenance.

Cost-Efficiency: Charges apply only for inference execution time (no idle costs).

Automatic Scaling: Scales to zero when inactive, ideal for sporadic workloads.

Simplified Deployment: No need to select instance types—just configure memory size.

2.3 Use Cases

Sporadic or unpredictable workloads (e.g., batch processing, internal analytics).

Proof-of-concept (PoC) deployments where cost optimization is key.

Low-traffic AI inference as a service with variable request patterns.

2.4 Limitations

Cold Starts: Initial requests may experience latency due to cloud  infrastructure spin-up.

Lower Throughput: Not optimized for high-volume, real-time inference.

Memory Constraints: Limited to 6 GB (as of latest updates), restricting large models.

3. Key Differences Between SageMaker Serverless and Real-Time Endpoints

4. Choosing Between Serverless and Real-Time Endpoints

4.1 When to Use SageMaker Real-Time Endpoints

Low-latency requirements (e.g., customer-facing applications).

Consistent high traffic needing guaranteed performance.

AI inference as a service with strict SLAs.

4.2 When to Use SageMaker Serverless Inference

Cost-sensitive workloads with irregular traffic.

Development/testing environments where idle costs should be minimized.

Batch processing or internal analytics with no strict latency needs.

4.3 Hybrid Approach

Some businesses use both solutions:

Real-Time Endpoints for customer-facing AI inference.

Serverless Inference for backend batch processing.

5. Performance and Cost Comparison

5.1 Performance Considerations

Real-Time Endpoints excel in latency-sensitive scenarios.

Serverless Inference introduces variability due to cold starts.

5.2 Cost Analysis

Real-Time Endpoints: Higher baseline cost (always-on instances).

Serverless Inference: Lower cost for infrequent workloads (pay-per-use).

Example Scenario:

High traffic (1000+ requests/min): Real-Time Endpoints are more cost-effective.

Low traffic (few requests/hour): Serverless Inference saves costs.

6. Conclusion: Selecting the Right AI Inference as a Service Option

Both SageMaker Serverless Inference and SageMaker Real-Time Endpoints provide robust AI inference as a service capabilities, but they serve different needs:

Choose Real-Time Endpoints if you need low latency, high throughput, and consistent performance for mission-critical applications hosting.

Opt for Serverless Inference if you prioritize cost efficiency, automatic scaling, and sporadic workloads.

By understanding these differences, businesses can optimize their AI inference deployments for performance, scalability, and cost-effectiveness.