S3 vs EFS: Which AWS Shared Storage Should You Choose?

Definition

Amazon S3 and Amazon EFS both let multiple AWS resources access the same data — but through completely different access models. Amazon S3 (Simple Storage Service) is object storage accessed over HTTPS through a REST API: you PUT and GET files identified by a key, and the service gives you virtually unlimited capacity, 11 nines of durability, and global accessibility. Amazon EFS (Elastic File System) is a managed NFS file system mounted by EC2, ECS, EKS, Fargate, and Lambda: it presents files and directories with POSIX semantics, supports concurrent access from thousands of clients, and grows and shrinks elastically. The key question isn't which is "better" but which fits your application's access pattern.

If your code reads files using s3:GetObject API calls or presigned URLs, you want S3. If your code calls open(), read(), write(), or fseek() on a path like /mnt/data/file.csv, you want EFS.

How They Work

S3 stores data as objects within globally-named buckets. Each object has a key, a body (up to 5 TB), and metadata. Clients use the AWS SDK, CLI, or any HTTPS-aware tool to PUT, GET, LIST, or DELETE. The service automatically replicates data across at least three Availability Zones within the chosen Region, and offers strong read-after-write consistency on all operations.

EFS creates a regional file system that exposes NFSv4.1 mount targets in each Availability Zone of your VPC. Any compute service that speaks NFS — EC2, ECS, EKS, Fargate, and Lambda (via configuration) — can mount the file system at a path and treat it like a local disk. The file system grows automatically as you write data and shrinks as you delete; you never pre-provision capacity. EFS replicates data across multiple Availability Zones (Regional file systems) or stores it in a single AZ (One Zone file systems for lower cost).

Critical distinction: S3 is accessed via an HTTPS API from anywhere, while EFS is mounted on Linux clients inside a specific VPC.

Side-by-Side Comparison

| Aspect | Amazon S3 | Amazon EFS | | --- | --- | --- | | Storage model | Object (API-based) | File (POSIX, NFSv4.1) | | Access protocol | HTTPS REST API (SDK/CLI/SigV4) | NFSv4.1 mount from a VPC | | Object/file size | Up to 5 TB per object | Up to 48 TB per file | | Total capacity | Virtually unlimited | Petabyte-scale, elastic | | Clients | Any HTTPS client, any account, any Region | Linux EC2/ECS/EKS/Fargate/Lambda in the same VPC (or peered VPC) | | Windows support | Yes (HTTPS) | No (EFS is NFS-only; use FSx for Windows instead) | | Concurrent access | Unlimited readers, multiple writers (with versioning) | Thousands of concurrent NFS clients | | Durability | 99.999999999% (11 nines) Multi-AZ | 11 nines Multi-AZ (Regional); 11 nines One Zone | | Availability SLA | 99.99% (Standard) | 99.99% (Regional); 99.9% (One Zone) | | Consistency | Strong read-after-write | Close-to-open (NFSv4 standard) | | Latency | 100s of ms per request | Sub-millisecond (General Purpose mode) | | Throughput | Scales horizontally per prefix | Up to 20 GB/s (Elastic throughput) | | Encryption | SSE-S3, SSE-KMS, SSE-C, DSSE-KMS | KMS at rest, TLS in transit | | Pricing model | Per GB stored + per request + egress | Per GB stored + per GB throughput (if Provisioned) | | Lifecycle to colder tiers | Standard → Standard-IA → Glacier classes | Standard → IA (30 days) → Archive (90+ days) |

Pricing Comparison: Concrete Numbers (us-east-1, 2026)

| Storage class | S3 (per GB-month) | EFS (per GB-month) | | --- | --- | --- | | Standard / hot | $0.023 | $0.30 | | Infrequent Access | $0.0125 (S3 Standard-IA) | $0.016 (EFS IA) | | Archive | $0.004 (Glacier Flexible) – $0.00099 (Glacier Deep Archive) | $0.008 (EFS Archive) | | Free tier | 5 GB Standard (12-month) | 5 GB Standard (12-month) |

Per-GB stored, S3 Standard is roughly 13× cheaper than EFS Standard. EFS pays for the convenience of POSIX semantics with a hefty hot-storage premium. That gap closes substantially with EFS IA and Archive — EFS Intelligent-Tiering (the default) automatically moves data between Standard, IA, and Archive based on access frequency.

Both services charge for requests (S3) or throughput (EFS Provisioned) and for data transfer out to the internet, but data transfer within the same Region to AWS services is free for both.

Common Use Cases

Choose S3 when:

• Storing unstructured data: images, video, audio, logs, backups, documents, ML training datasets.
• Hosting a static website or serving assets via CloudFront.
• Building a data lake queryable by Athena, Redshift Spectrum, EMR, Glue, or Databricks.
• Storing backup targets — EBS snapshots, RDS backups, and AWS Backup all live on S3.
• Sharing data across AWS accounts or with external parties via presigned URLs.
• Storing artifacts for CI/CD (Docker images go to ECR, but build artifacts often go to S3).
• Working from Windows EC2 instances — EFS is Linux-only.

Choose EFS when:

• Multiple EC2 instances (or Fargate tasks) need to read/write the same files concurrently with POSIX semantics.
• Lifting an on-premises NFS workload to AWS without refactoring application code.
• Hosting home directories for a development environment with many users.
• Building a shared content management system (WordPress, Drupal, Magento) with multiple web servers behind a load balancer.
• Storing Lambda function shared state that exceeds the 10 GB /tmp limit.
• Building a machine learning training shared dataset across Fargate tasks or SageMaker training jobs.
• Running CI/CD shared workspaces where build artifacts must be visible across runners.

When EFS Is the Wrong Answer

Developers often reach for EFS when S3 would have been simpler. Common anti-patterns:

• Storing user uploads from a web application — almost always belongs on S3 with presigned URLs, not EFS.
• Backups — S3 (with versioning + lifecycle to Glacier) is far cheaper.
• Static asset serving — S3 + CloudFront is the canonical answer; EFS over HTTP requires a webserver fleet.
• Big-data analytics — Athena, Spark, and Trino all read S3 natively; EFS is a poor fit.
• Cross-account sharing — S3 bucket policies are simpler than VPC peering + NFS access.

The deciding test: if your code or framework only writes files via open()/fwrite() because that's how it was written, EFS keeps you from refactoring. If you control the code, S3 is usually cheaper and more scalable.

Performance and Throughput Modes (EFS)

EFS performance is controlled by two settings:

Performance mode (set at creation, cannot change):

• General Purpose (recommended) — lowest latency per operation; up to 35,000 operations per second.
• Max I/O (legacy) — higher latency per operation but unlimited aggregate throughput; mostly superseded by Elastic throughput mode.

Throughput mode (changeable):

• Elastic (recommended default) — scales throughput automatically with workload; pay only for what you use; up to 20 GB/s.
• Provisioned — fixed throughput regardless of file system size; for steady, predictable workloads.
• Bursting (legacy) — throughput scales with file system size using burst credits.

Most workloads should use General Purpose + Elastic, which is the default in the AWS Console.

Performance and Scaling (S3)

S3 scales by prefix rather than file system: each prefix supports at least 3,500 PUT/COPY/POST/DELETE and 5,500 GET/HEAD requests per second. To exceed this, split objects across multiple prefixes — there's no global throughput cap. S3 Transfer Acceleration uses CloudFront edge locations for faster long-distance uploads. S3 Express One Zone offers single-digit millisecond first-byte latency for hot workloads at higher request prices.

Comparison with Alternatives

| Service | Protocol | Use case | | --- | --- | --- | | S3 | HTTPS object API | Unstructured data, backups, data lakes, static sites | | EFS | NFSv4.1 | Multi-client Linux shared storage | | EBS | Block (NVMe via EC2) | Single-instance disk: boot, DB, app data | | FSx for Windows File Server | SMB | Windows shared file storage | | FSx for NetApp ONTAP | NFS + SMB + iSCSI | Multi-protocol enterprise storage | | FSx for OpenZFS | NFS | High-performance Linux file storage | | FSx for Lustre | Lustre | HPC + ML training at very high throughput | | S3 Glacier | HTTPS (archival tier of S3) | Long-term archive | | Storage Gateway | NFS/SMB/iSCSI bridge to S3 | Hybrid on-prem to S3 |

If your need is "shared file storage from Windows," use FSx for Windows, not EFS. If your need is "shared file storage with NetApp features (snapshots, replication, dedupe)," use FSx for ONTAP. EFS is the cheap, simple default for Linux NFS only.

Pros and Cons

S3 Pros — Unlimited scale, 11 nines durability, accessible from anywhere with HTTPS, rich ecosystem (Athena, Glue, CloudFront, Replication, Lifecycle), Intelligent-Tiering automates cost optimization, cross-account sharing is trivial.

S3 Cons — Not a filesystem (no fseek, no partial in-place updates), per-request charges accumulate for chatty workloads, internet egress fees surprise bills, misconfigured bucket policies can expose data, eventual consistency between buckets in different Regions.

EFS Pros — POSIX file system semantics, mountable by multiple clients including Lambda, automatic capacity growth, no provisioning, integrates with EC2/ECS/EKS/Fargate seamlessly, supports backup via AWS Backup.

EFS Cons — Linux-only (no Windows), more expensive per GB than S3 by ~13× at Standard tier, requires VPC networking and NFS port 2049 connectivity, NFS over the network adds latency vs. local disk, throughput costs add up for high-bandwidth workloads.

Exam Relevance

This comparison appears regularly across AWS certifications:

• Cloud Practitioner (CLF-C02) — definitions, object vs file storage distinction, the existence of EFS as a managed NFS service.
• Solutions Architect Associate (SAA-C03) — most-tested scenario: "Multiple EC2 instances in an Auto Scaling Group need to read/write the same data" → EFS. "Store user-uploaded images served via CloudFront" → S3. "Windows servers need shared file storage" → FSx for Windows, not EFS.
• SysOps Administrator (SOA-C02) — EFS performance modes, throughput modes, lifecycle to IA/Archive.
• Solutions Architect Professional (SAP-C02) — hybrid scenarios using Storage Gateway, multi-Region replication strategies, choosing between EFS and FSx for ONTAP.

Classic exam trap: any scenario mentioning Windows servers or SMB protocol → not EFS. Any scenario mentioning "shared by multiple instances" + Linux + POSIX → EFS. Any scenario mentioning "unstructured data" or "data lake" or "static website" → S3.

Frequently Asked Questions

Q: Can I mount an S3 bucket as a file system?

A: Not natively, but tools like Mountpoint for Amazon S3 (AWS's official open-source FUSE driver, GA in 2023) let you mount an S3 bucket as a read-mostly file system on Linux. It supports sequential reads, file uploads, and basic POSIX operations, but it is not a substitute for EFS — random in-place writes, file locking, and full POSIX semantics are limited. For true shared file system workloads, use EFS; for occasional file-style access to objects, Mountpoint works well.

Q: Is EFS Multi-AZ by default?

A: Yes — Regional EFS file systems (the default) replicate data across multiple Availability Zones within a Region, surviving AZ failures. One Zone EFS file systems store data in a single AZ at lower cost (~47% cheaper) but lose data if that AZ is destroyed. Choose Regional unless you have a specific cost-sensitive, recoverable workload like development environments or replicas that can be rebuilt.

Q: Can Lambda functions access EFS?

A: Yes. Configure the Lambda function to run inside a VPC and attach an EFS Access Point. The function then sees a mounted path like /mnt/data and can read/write files there. This is useful when (1) you need to share state between Lambda invocations larger than the 10 GB /tmp limit, (2) you want to share data between Lambda and EC2/Fargate, or (3) you're running ML inference and want to load large models from EFS once. Note: EFS-attached Lambdas incur a small additional cold-start latency for the VPC ENI attachment.

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This article reflects AWS features and pricing as of 2026. AWS services evolve rapidly — always verify against the official S3 and EFS documentation before making production decisions.