Office Hours #10 – S3 Files Deep Dive: Architecture, Benefits, and How It Compares to JuiceFS

​In April 2026, AWS launched Amazon S3 Files, a service that allows you to mount an S3 bucket as a POSIX-compliant file system with zero or minimal migration effort. For organizations deeply invested in AWS, this offering seems like a natural evolution, turning the world's most popular object store into a shared file system with sub-millisecond latency.

​But what exactly is S3 Files under the hood? What workloads does it truly excel at? And how does it compare to a purpose-built distributed file system like JuiceFS?

​During this office hour, we'll talk about the architecture of S3 Files, its high-performance EFS storage tier, and its automatic write-back sync window. We'll then compare it side-by-side with JuiceFS in terms of things like metadata performance, write amplification, cross-cloud support, and cost structure. There are no perfect solutions, only trade-offs. By the end of this session, you'll know exactly how to evaluate your AI/ML and other workloads before settling on a storage solution.

​Agenda

• ​17:00 – 17:10 PST | S3 Files Breakdown: What It Is and How It Works

• ​17:10 – 17:20 PST | S3 Files Pros & Cons

• ​17:20 – 17:35 PST | Comparison: JuiceFS vs. S3 Files

• ​17:35 – 17:45 PST | Live Q&A – Ask the JuiceFS Team Anything

​​Speaker: Joe Zhou, Juicedata Developer Advocate

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​JuiceFS  Office Hours

​​​​​​​​​The JuiceFS Office Hours is a recurring online event held on the fourth Thursday of each month. It serves as a dedicated platform for JuiceFS users and community members to:

• ​​​​​​​​​​Share our practices and community experiences in the AI field

• ​​​​​​​​​​Receive immediate responses to their questions from the JuiceFS team

• ​​​​​​​​​​Learn about the latest product updates and feature releases

​​​​​​​​​About JuiceFS
JuiceFS is an open-source, high-performance distributed file system for the cloud. With full POSIX compatibility, it turns object storage into massive local disks across platforms and regions, and is widely used in AI applications like autonomous driving, generative AI, quant research, and biotech.