Comparison
AWS EFS vs Training Pipes
Both give you a POSIX filesystem in the cloud. Only one is priced for ML training workloads where the hot working set is much smaller than the total dataset.
Short answer
AWS EFS is a good managed NFS filesystem for general-purpose shared storage, but it's priced by provisioned capacity — not by hot working set. That makes it expensive for ML training, where most data is read infrequently between epochs. Training Pipes uses object storage as the durable tier and a regional NVMe cache for the hot set, so you pay cold-storage prices for the 90% of your dataset that isn't hot and cache-accelerator prices for the 10% that is.
Feature-by-feature comparison
| Feature | EFS | Training Pipes |
|---|---|---|
| Protocol | NFSv4.1 | NFSv4.0, NFSv4.1, SMB 3.x |
| POSIX semantics | Yes | Yes |
| Pricing model The pricing model is the core difference for ML workloads. | Per provisioned GB | Per-GB object storage + plan tier |
| Hot/cold tiering | Standard vs IA (charges on read) | NVMe cache + cold object storage |
| Multi-cloud support | No | Yes |
| Bring your own S3 bucket | No | Yes |
| Cross-region access | Requires replication | Gateway in any supported region |
| S3-compatible API to same data | No | Yes |
| Transport security | TLS (optional) | WireGuard + optional TLS |
| Request/read overhead | Charged per GB read on Elastic | Included in plan |
| Free tier | 5 GB for 12 months | Free tier for ongoing use |
When to use EFS
- You need managed NFS and are 100% committed to AWS.
- Your workload is general-purpose (web apps, CI, home directories), not training.
- Your hot working set is close to 100% of your dataset.
- You already have VPC + IAM plumbing you want to reuse.
When to use Training Pipes
- You train models repeatedly on the same datasets.
- Your hot working set is a fraction of your total dataset.
- Your data already lives in S3, GCS, R2, or another S3-compatible store.
- You run training across multiple regions or clouds.
- You want SMB and S3 API alongside NFS against the same data.
The verdict
Pick EFS if you need a drop-in managed NFS on AWS with zero architectural change and budget isn't a concern. Pick Training Pipes if you're training models, read the same data many times, want to work across clouds, or care about the bill. For a 50 TB dataset with an 8 TB hot set, we typically see 15-20× lower monthly costs with Training Pipes while matching or beating EFS on training throughput.
Frequently asked questions
- Is Training Pipes a drop-in replacement for EFS?
- Close to it. You mount it the same way (standard NFSv4), your training code is unchanged, and the POSIX semantics are there. The difference is that the backing store is object storage (ours or yours) instead of EFS-specific infrastructure.
- How does Training Pipes stay cheaper than EFS?
- EFS charges per GB of provisioned filesystem capacity. Training Pipes charges per GB of object storage (cold) plus a plan-included regional cache for the hot working set. For datasets where only 10-30% is hot at any time — true for most ML training — this saves 80-95%.
- Can I use Training Pipes with my existing AWS S3 bucket?
- Yes. Create a BYO connection to your S3 bucket and mount it via NFS/SMB with no data migration. You keep your existing IAM, lifecycle rules, and bucket policy.
- Does Training Pipes support EFS-style IA (infrequent access)?
- The concept is already built in. Your object storage backend handles cold storage economics directly (you can apply lifecycle rules to move cold data to Glacier or Deep Archive), while the regional cache handles hot-read acceleration. You don't pay twice.
- What about EFS Elastic Throughput?
- EFS Elastic Throughput charges per GB read ($0.03/GB on reads). For a training workload that re-reads the dataset 100 times, that's significant. Training Pipes doesn't charge per-read on cached data, so repeat reads are effectively free.
Related reading
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