S3 vs EFS for AI Workloads

AI workloads have diverse storage needs: training datasets, model artifacts, checkpoint files, feature stores, and inference caches. S3 and EFS both store data on AWS but serve fundamentally different access patterns. Choosing the wrong one causes performance bottlenecks or unnecessary cost.

Fundamental Differences

Amazon S3 is object storage. You store and retrieve entire objects (files) via HTTP API. No filesystem semantics - no directories, no file locking, no random access within files. Virtually unlimited capacity. Extremely durable (99.999999999%).

Amazon EFS is a managed NFS filesystem. Mounted like a local filesystem on EC2, ECS, EKS, Lambda, and SageMaker. Standard filesystem operations: read, write, seek, list directories, file locking. Capacity grows and shrinks automatically. Elastic Throughput is now the default throughput mode and scales bandwidth automatically with your workload, so you no longer have to provision throughput or rely on burst credits for most use cases.

AI Training Data

Large Datasets (100GB+)

S3 is the standard choice. Training frameworks (PyTorch, TensorFlow) have native S3 data loading, and the Amazon S3 Connector for PyTorch (built on the AWS Common Runtime) gives the highest throughput option for streaming S3 data into PyTorch training. SageMaker AI Training offers several input modes: File mode downloads the dataset first, Pipe mode streams data directly from S3, and FastFile mode mounts S3 as a filesystem and streams objects on demand. S3 handles any dataset size.

EFS can store training data with POSIX filesystem access, which simplifies code that expects local file paths. With Elastic Throughput an EFS file system now scales to multiple GB/s automatically, but for large sequential reads S3 is still more cost-effective and typically performs better.

Winner: S3 for large datasets

Small Datasets with Random Access

S3 requires downloading entire objects. If your training code reads small portions of large files randomly (e.g., random access within HDF5 files), S3 is inefficient.

EFS supports random access within files, making it suitable for workloads that read small portions of large files. NFS caching improves repeated access patterns.

Winner: EFS for random access patterns

Model Artifacts

Model Storage and Versioning

S3 is the standard for model artifact storage. S3 versioning tracks model versions. SageMaker Model Registry stores model artifacts in S3. Lifecycle policies move old versions to cheaper storage tiers.

EFS can store models but lacks built-in versioning. More expensive per GB than S3.

Winner: S3

Model Loading for Inference

S3 model loading requires downloading the model to the inference instance at startup. For large models (multi-GB), this can take minutes and contributes to cold start time.

EFS model loading is near-instant because the filesystem is already mounted. The model appears as a local file. For SageMaker endpoints and Lambda, EFS mounting eliminates the model download step.

Winner: EFS for fast model loading; S3 for long-running endpoints (download once)

Checkpoint Storage

Training checkpoints must be written frequently and reliably:

S3 checkpoint writing requires uploading the complete checkpoint file. For large checkpoints (multi-GB), the upload takes seconds to minutes. S3’s durability ensures checkpoints are not lost.

EFS checkpoint writing is a standard file write operation. Faster for frequent, small checkpoints. The filesystem handles the write; no need for upload logic.

Winner: EFS for frequent checkpoints; S3 for durability

Cost Comparison

Prices below are us-east-1 list rates and vary by region. Always confirm against the live AWS pricing pages.

EFS Standard is roughly 13x more expensive per GB than S3 Standard. For large datasets (100GB+), this difference is significant. With Elastic Throughput you also pay per GB read and written, so active filesystem traffic adds cost on top of storage.

Beyond Standard and Infrequent Access, EFS also offers an Archive storage class for long-lived data accessed a few times a year or less, and a One Zone option that trades cross-AZ redundancy for lower cost. EFS Intelligent-Tiering moves files between Standard, IA, and Archive automatically based on access patterns.

Cost optimization: Store large, infrequently accessed datasets in S3. Use EFS for data that needs filesystem access and is actively used. Enable EFS lifecycle management (Intelligent-Tiering) to move inactive data to IA or Archive automatically.

Performance Comparison

S3 excels at parallel bulk reads. EFS excels at low-latency random access.

The Lines Are Blurring

The classic split (S3 for objects, EFS for filesystem access) is softer than it used to be, because S3 now has several ways to behave like a filesystem and to serve low-latency reads.

• Mountpoint for Amazon S3 - an open source client that mounts an S3 bucket as a local filesystem, so applications that expect file paths can read S3 directly with high aggregate throughput. There is also a Mountpoint for Amazon S3 CSI driver (generally available) for mounting buckets into Kubernetes pods on Amazon EKS.
• S3 Express One Zone - a single-Availability-Zone storage class purpose-built for consistent single-digit millisecond access. It stores data in directory buckets, can scale to millions of requests per second, and is aimed squarely at latency-sensitive AI/ML training and analytics where you want S3 economics with much lower latency. The tradeoff is single-AZ durability.
• Amazon FSx for Lustre - a fully managed high-performance parallel filesystem that links to an S3 bucket and is the common choice for large distributed training that needs sub-millisecond filesystem latency at very high throughput, beyond what EFS targets.

For many AI teams the practical decision is now S3 as the durable system of record, plus one of these access layers (Mountpoint, S3 Express One Zone, or FSx for Lustre) when the workload needs filesystem semantics or very low latency, with EFS reserved for shared POSIX workspaces.

Common Patterns for AI

Pattern 1: S3 Primary (Most Common)

Store everything in S3. Training data, models, checkpoints, and datasets all in S3. Use framework-native S3 integration for data loading. This is the simplest and cheapest approach.

Pattern 2: S3 + EFS Hybrid

S3 for bulk storage and archival. EFS mounted on compute instances for:

• Model loading (fast cold starts)
• Shared workspace for teams using notebooks
• Checkpoint storage during training
• Temporary processing storage

Pattern 3: EFS for Shared Development

Data science teams share datasets and models via EFS. Each notebook instance mounts the same EFS filesystem. Changes are immediately visible to all team members.

Recommendation

Use S3 as the default for AI workloads. It handles most storage needs at the lowest cost. Use S3 for training datasets, model artifact storage, pipeline outputs, and long-term storage.

Add EFS when you need filesystem semantics: shared development environments, fast model loading for inference, frequent checkpoint writes during training, or workloads that require random file access.

Avoid storing large training datasets on EFS when S3 would work. The cost difference at scale is substantial and the performance difference for sequential reads is minimal.

Sources

• Amazon S3 pricing (official)
• Amazon EFS pricing (official)
• Amazon EFS storage classes and Infrequent Access (official)
• Amazon S3 Express One Zone storage class (official)
• Mountpoint for Amazon S3 (official)
• Choosing an input mode and a storage unit for SageMaker AI training (official)