Amazon Elastic Inference: A Retrospective and Guide to Modern Alternatives

Service Discontinuation Notice

Amazon Elastic Inference (EI) is a legacy service and is being discontinued. As of April 15, 2023, AWS is no longer onboarding new customers to Elastic Inference. Existing customers will be supported until October 15, 2024. This service is not recommended for any new machine learning workloads. This article provides a historical overview of the service and details the modern AWS services that have replaced it for cost-effective ML inference.

What Was Amazon Elastic Inference?

Amazon Elastic Inference (EI) was a service that allowed you to attach low-cost, right-sized GPU-powered acceleration to any Amazon EC2 or Amazon SageMaker instance. It was designed to solve a common problem in machine learning deployment: the cost-performance gap for inference workloads.

ML models often benefit from GPU acceleration for inference (generating predictions), but using a full-sized GPU instance (like a p4d.24xlarge or g5.xlarge) can be prohibitively expensive and wasteful if the model doesn't require the full power of the GPU 100% of the time. On the other hand, running inference on a CPU-only instance could be too slow.

Elastic Inference bridged this gap by allowing you to provision a general-purpose CPU instance for your application and attach just the right amount of GPU acceleration needed, potentially reducing inference costs by up to 75%.

The End of an Era: Discontinuation of Elastic Inference

As the field of machine learning has evolved, so have the hardware and software solutions for optimizing inference. Newer, more efficient, and more powerful alternatives have emerged. In recognition of this, AWS has chosen to discontinue the Elastic Inference service to focus on these modern solutions.

The key dates for the phase-out are:

• April 15, 2023: No new customers were onboarded.

• October 15, 2024: End-of-life for existing customers.

Modern Alternatives for Cost-Effective ML Inference on AWS

AWS now offers several superior alternatives for running ML inference workloads efficiently and cost-effectively.

1. AWS Inferentia2 Instances (Inf2)

AWS Inferentia is a family of custom-designed silicon chips built by AWS specifically for high-performance, low-cost machine learning inference. The latest Inf2 instances are the premier choice and direct successor for workloads that would have used Elastic Inference.

• Best For: High-throughput, low-latency inference for large language models (LLMs) and computer vision models.

• Why It's Better: Offers significantly better price-performance compared to previous-generation GPU instances. They are purpose-built for inference, avoiding the overhead of graphics-focused GPUs.

2. General-Purpose GPU Instances (G4dn, G5)

For workloads that require more flexibility or use NVIDIA's CUDA libraries, the G4dn and G5 instances, equipped with NVIDIA GPUs, are excellent choices. Newer NVIDIA GPUs (like the A100 in P4d instances) also support Multi-Instance GPU (MIG) capabilities, which allow a single large GPU to be partitioned into several smaller, isolated GPU instances. This functionally achieves the same goal as Elastic Inference—right-sized GPU acceleration—but in a more modern and efficient package.

• Best For: Flexible inference workloads, models requiring CUDA, and scenarios where you can consolidate multiple models onto a single GPU using MIG.

• Why It's Better: Provides access to the latest GPU technology and a mature software ecosystem. MIG offers a direct way to avoid underutilization.

3. AWS Graviton Processors

For workloads that are not highly sensitive to latency and do not require GPU acceleration, AWS Graviton instances (powered by Arm-based processors) offer the best price-performance for CPU-based inference.

• Best For: CPU-bound ML models, such as many traditional ML models (e.g., scikit-learn, XGBoost) and smaller deep learning models.

• Why It's Better: Can provide significant cost savings (up to 40% better price-performance) over comparable x86-based instances.

4. Amazon SageMaker Serverless Inference

For models with intermittent or unpredictable traffic, SageMaker Serverless Inference is an ideal choice. It automatically provisions, scales, and turns off compute capacity based on the volume of inference requests. You pay only for the compute time you use to process requests and the amount of data processed, completely eliminating idle capacity costs.

• Best For: Applications with sporadic traffic patterns, such as chatbots that are active only during business hours or APIs that are called infrequently.

• Why It's Better: The ultimate solution for avoiding paying for idle resources, perfectly aligning with the original cost-saving goal of Elastic Inference.