The Slim Venn Diagram of EBS and S3

Even though EBS and S3 are both storage options in AWS, it's pretty uncommon to find situations where they’re equally good fits. S3 (Simple Storage Service) is great for storing unstructured data at massive scale. EBS, on the other hand, is like a virtual hard drive (or SSD) for your EC2 instance, giving you low-latency access to data. The good news? Since they’re built for such different purposes, picking the right one is usually pretty easy as long you know what your app actually needs.

Determine Your Application’s Needs

The answer to whether your application should use S3 or EBS lies within your applications requirements. A good place to start is to ask yourself a few questions:

1. Is my app running infrastructure compatible with EBS?
2. EBS volumes are primarily intended for EC2 instances, so if your app is running on AWS Lambda, EBS isn’t an option. Some other services like EMR or ECS can use EBS—but only if you’re running them with compatible launch types (EC2, Fargate for ECS, etc.)
3. How much data do I need to store?
4. EBS volumes are block storage that you allocate up front, and each one has a size limit (currently up to 64TB). That works fine if you know how much space you need or want low-latency access. But if your app deals with large media files—like images or videos—that can pile up fast, S3 is probably a better fit. It scales almost infinitely without you having to worry about managing capacity.
5. How much throughput do I need?
6. EBS can hit up to around 4,000 MB/s of throughput on its top-tier volume types—if you max out the size and provision the highest possible performance. S3 doesn’t publish exact throughput numbers, but it’s rarely the bottleneck in an application. With the right setup—like using multiple key prefixes and parallel requests—S3 can scale to very high throughput levels, basically as much as you need.
7. That said, both EBS and S3 offer plenty of throughput for most use cases, so before making a decision based on throughput performance, it's worth checking if throughput is actually your limiting factor.
8. Do you need single-digit millisecond latency?
9. S3 is fast, latencies are usually in the 10–30 ms range for most operations. But if your app needs something even faster, EBS is the way to go. It can consistently hit single-digit millisecond latency, especially with the right volume type and instance setup. If your workload is latency-sensitive—like a database or a transactional system—anything slower probably won’t cut it.
10. Need High IOPS?
11. S3 can scale to handle a huge number of read/write operations, but you’re charged per request so heavy I/O can get expensive fast. EBS, on the other hand, has an upper IOPS limit that depends on the volume type (currently up to 256,000 IOPS with io2 Block Express). But since EBS pricing isn’t based on the number of operations, it’s usually a better fit for workloads with high IOPS requirements, like databases or analytics engines.
12. What is your data access pattern?
13. S3 offers tiered storage, so you can move objects to cheaper classes depending on how often you need to access them. For example, if you rarely touch the data, S3 Glacier or Glacier Deep Archive is great for long-term, low-cost storage. EBS doesn’t have that kind of tiering, so if your data isn’t accessed often, it’s usually way more expensive to keep it on EBS compared to putting it in S3’s colder storage tiers.

Cost Considerations

Below is a cost breakdown of the 2 services, for more current and in depth cost reference official AWS documentation.

Cost Deep Dive

As the chart above shows, S3 is a lot cheaper than EBS when it comes to storage costs and that gap gets even wider when you factor in a few more things. With EBS, you have to pre-allocate storage per volume, so even if you're only using 30% of it, you're still paying for the whole thing. S3 is pay-as-you-go, so you only pay for the actual amount of data you're storing. No wasted space, no wasted money.

Use Cases

Where S3 crushes EBS

S3 is perfect for storing large multimedia files—like photos, videos, and audio—that don’t need super-fast access. These types of files are usually read and written to infrequently, making them a great fit for S3. For example, a platform like Instagram could use S3 to store all the photos and videos users upload or a video streaming services could rely on S3 to store their massive content libraries. In fact, Netflix uses S3 to power its media storage infrastructure, so it’s a highly reliable choice.

Where EBS crushes S3

EBS is the go-to solution when you need high-speed access for data that’s being read or written constantly. It’s perfect for databases, whether SQL or NoSQL, because of its low-latency and high-performance capabilities. AWS even uses EBS behind the scenes to run databases in RDS (Relational Database Service). Plus, EBS’s volume snapshots make backups super easy, helping to protect your data and making your application more resilient in case of failures.

A Dynamic Duo

What if you need both: super low latency in some parts of your app and cheap, scalable storage elsewhere? Good news—you can totally use both EBS and S3 together.

Take Instagram: you could store user data like likes, comments, and profile info in a database running on EBS, while keeping all the photos and videos in S3. Want to tie it all together? Just store the object metadata (like file paths or tags) in the EBS-backed database. That way, you get the speed of EBS where it matters, and the scalability and cost savings of S3 where it counts.

AI applications, particularly those involving large-scale model training, demand a unique blend of storage characteristics. Models and training datasets are often stored in Amazon S3 because of its virtually unlimited scale and durability. However, S3's object storage nature isn't optimized for high-speed, low-latency access that training jobs often require.

That’s where Amazon EBS comes in. EBS can be used in two key ways to complement S3:

• Ephemeral storage: Acting as a local, high-speed cache to S3. Frequently accessed data can be staged on EBS volumes to accelerate I/O performance.
• Durable, iterative storage: During model training, snapshots of EBS volumes can be taken and moved across compute instances. This allows teams to checkpoint progress, iterate efficiently, and ensure training continuity.

For instance, in certain machine learning pipelines, training data is first loaded from S3, cached on an EBS volume, and then used for high-speed read/write operations during training.

While powerful, this hybrid approach typically demands custom implementation to maintain synchronization and ensure data integrity. Developers must manage the complexity of syncing data between S3 and EBS, avoid stale caches, and build in recovery logic—all of which add a lot of engineering overhead.

Automatically Managed S3 Cache Solutions

To simplify this complexity, companies like Archil have developed smart, automated caching layers on top of S3. These solutions reduce latency and operational cost without the need to manually provision or manage EBS volumes.

By transparently caching frequently accessed data, they offer near-EBS performance while maintaining the cost-efficiency and scale of S3. However, it's important to note that these systems usually operate with eventual consistency—there may be a slight lag between updates to the source data in S3 and what's served from the cache. This however is a feature not a flaw, since it reduces the amount of reads/writes you make to S3, reducing your AWS bill.

In short, while DIY solutions using EBS give you full control and raw power, managed S3 cache layers provide a compelling alternative for teams looking to reduce complexity without sacrificing performance.