If you’ve ever stared at an AWS pricing page wondering whether to pick EBS, EFS, or S3 for your application, you’re not alone. These services overlap just enough to be confusing, and different enough to impact your performance and bill if you get it wrong.
The most common question:
“What’s the right AWS storage for my workload and how do I avoid overspending?”
So here’s a real-world breakdown of how these services differ, when to use each, and how to avoid the most common mistakes teams make.
🧱 EBS — Think Fast SSD for One Server
Amazon EBS (Elastic Block Store) is basically a cloud hard drive. You attach it to an EC2 instance, format it like a regular disk, and go.
It’s block storage, so it’s fast, low-latency, and great for single-server applications — like a database that needs consistent IOPS or an app server with a local working set.
When it shines:
- Running PostgreSQL, MySQL, MongoDB, or similar
- Needing high IOPS with low latency
- Hosting boot volumes for EC2 instances
Gotchas:
- It’s bound to one AZ — if that zone goes down, your volume goes with it (unless you snapshot to S3).
- You can’t share it across multiple EC2s (except for niche multi-attach cases).
- You pay for provisioned capacity, used or not, which is a silent killer for cloud bills.
💡 Quick tip: Use gp3 over gp2. You get baseline performance included and can separately control IOPS if you need more.
📂 EFS — Shared File Storage
Amazon EFS (Elastic File System) is a fully managed NFS file system. Mount it across multiple EC2 instances, and they’ll all see the same files in real time.
It’s file storage, so it behaves like a traditional network drive perfect for apps that expect POSIX compliance, file locking, or just a normal folder structure.
When it shines:
- Shared content across web fleets
- Lift-and-shift apps that rely on local file systems
- Lambda, ECS, or batch jobs that need shared working directories
Gotchas:
- EFS is expensive, around 10x the cost of S3 per GB.
- Slightly higher latency than EBS — it’s over the network.
- Linux only (uses NFS v4.1 — not natively supported by Windows).
💡 Cost tip: Always enable lifecycle rules. Move cold files to EFS Infrequent Access or you’ll be paying premium rates for idle bytes.
🪣 S3 — Your Scalable, Cheap, Durable Data Lake
Amazon S3 (Simple Storage Service) is AWS’s object storage built for scale, cost-efficiency, and durability. You don’t mount it. You access it via HTTP APIs or SDKs.
It’s designed to store everything: images, logs, backups, videos, documents and serve it to anything: web apps, mobile apps, data pipelines, you name it.
When it shines:
- User uploads, backups, static websites, logs
- Big data or ML workloads
- Archival and lifecycle-based tiering (Glacier, Deep Archive)
Gotchas:
- It’s not a file system. You can’t just cd into it from EC2.
- You pay per request and for data transfer, which can spike bills at high volume.
- No partial updates — if you want to change a byte, you upload the whole object.
💡 Smart move: Set up S3 lifecycle policies on day one. Auto-transition to IA, Glacier, or Deep Archive based on file age or access patterns.
⚖️ Comparison Table
🧠 Recommendation on its use
When teams optimize storage spend, here’s how to frame the decision:
- If it’s tied to a single server and needs high performance → use EBS.
- If it’s shared across EC2s and needs a file system → use EFS (with lifecycle).
- If you can access it via API and don’t need POSIX → use S3. Always.
And honestly, most modern architectures can design around S3. You just need to structure your code to upload/download via SDKs or signed URLs and the cost savings speak for themselves.
📊 Monthly Cost Comparison (1 TB Storage)
⚖️ How to Choose (Real-World Flow)
Here’s how we usually help teams decide:
Do multiple EC2s need to access the same files?
Yes → Use EFS, but enable lifecycle rules immediately.
Do you need low-latency, disk-like storage for a single server?
Yes → Use EBS, right-size aggressively.
Is it just files you upload, serve, or archive?
Use S3. It’s cheap, durable, and infinitely scalable.
Mixing workloads?
Use all three, but offload aggressively:
- Boot disks → EBS
- Shared configs → EFS (with IA tier)
- Logs, uploads, backups → S3 (with lifecycle)
🧠 What Teams Get Wrong (And How It Adds Up)
- Using EFS for stuff that could sit on S3 — a few cents vs a few dollars per GB.
- Leaving EBS volumes provisioned with way more IOPS or space than needed.
- No lifecycle policies — especially S3 buckets holding years of cold logs or media.
TL;DR still applies: S3 crushes on cost for most scalable needs, EFS for shared file convenience, EBS for raw single-instance performance. If your workload has evolved (e.g., more serverless or AI-driven), S3 often wins even more now with integrations like S3 Express One Zone for sub-millisecond latency.
🧾 Final Thoughts (and Mistakes to Avoid)
Every AWS storage service has its place but they’re not interchangeable.
- Don’t use EFS just because you “need a shared folder” — unless your app really needs file semantics, you’re better off with S3.
- Don’t keep cold data on EBS or EFS — snapshot to S3 or archive aggressively.
- Don’t overlook request/transfer costs on S3 — especially with chatty workloads.
The right choice depends on your access pattern first, and cost second. But if you’re mindful of both, you can design storage that scales, performs, and stays lean.
Extra Optimization Tips
Hybrid Wins: Combine them — e.g., EBS for hot DB data, S3 for cold archives via snapshots. Tools like AWS DataSync make migrations seamless.
Cost Hacks: Enable S3 Requester Pays for public buckets to offload transfer fees. For EFS, monitor with CloudWatch and scale to One Zone-IA for non-critical data (even cheaper at ~$0.016/GB-month).
Common Pitfalls: Teams still forget S3’s minimum duration fees (e.g., 30 days for IA) — early deletes cost full price. Also, cross-region replication adds ~$0.02/GB; keep it intra-region unless needed for DR.
If You’re Scaling: For AI/ML, S3’s integration with SageMaker and Bedrock makes it unbeatable for datasets. EFS shines in HPC clusters now with better Elastic Throughput.
