Vultr Object Storage – S3-Compatible Storage for AI Workloads
Scalable, S3-compatible object storage integrated with Vultr GPU instances. Store training datasets, model checkpoints, and production artifacts affordably.
Object Storage Features
S3-Compatible API
Drop-in replacement for AWS S3. Use boto3, rclone, s3cmd, MinIO client, or the AWS CLI with a single endpoint change — no code rewrite needed.
Predictable Per-GB Pricing
Pay only for what you store. No per-request charges for standard GET/PUT operations. Ideal for large-scale ML dataset storage with high I/O frequency.
Global CDN Edge
Vultr's anycast CDN delivers static assets from 32+ global PoPs. Serve model inference responses, static ML artifacts, and API results at the edge.
Access Control & Encryption
Fine-grained S3 ACLs, bucket policies, and server-side AES-256 encryption. CORS configuration for web-facing API integrations.
GPU Instance Integration
Mount buckets via s3fs-fuse or stream data via boto3 directly from Vultr GPU instances. Co-located storage and compute minimize egress latency.
Unlimited Scalability
No object count limits. Store petabytes of training data, model weights, and checkpoints without pre-provisioning storage capacity.
Object Storage Pricing vs AWS S3 & GCS
| Feature | Vultr | AWS S3 | Google Cloud |
|---|---|---|---|
| Storage (per GB/mo) | ~$0.020 | ~$0.023 (S3 Standard) | ~$0.020 (Standard) |
| GET requests (10K) | Free | $0.004 | $0.004 |
| PUT requests (1K) | Free | $0.005 | $0.005 |
| Egress (per GB) | ~$0.01 (CDN) | $0.09 (Internet) | $0.08 (Internet) |
| S3 API Compatible | Yes | Native | Via XML API |
| GPU Co-location | Yes (same DC) | Partial | Partial |
AI & ML Storage Use Cases
ML Training Datasets
Store ImageNet, Common Crawl, or custom datasets. Stream multi-TB datasets directly to GPU training nodes using tf.data or PyTorch DataLoader with S3 connectors.
Model Weight Storage
Centralize GGUF, safetensors, ONNX, and checkpoint files. Version model weights with bucket versioning and restore previous checkpoints instantly.
Generative AI Assets
Store raw video/audio for fine-tuning multimodal models. Serve generated images and videos via Vultr CDN to end users without bandwidth spikes.
Database Backups
Automated PostgreSQL, MongoDB, and Redis backups to object storage. Lifecycle rules automatically archive old backups to cold storage tiers.
Quick Integration Examples
import boto3
s3 = boto3.client(
"s3",
endpoint_url="https://ewr1.vultrobjects.com",
aws_access_key_id="YOUR_ACCESS_KEY",
aws_secret_access_key="YOUR_SECRET_KEY",
)
# Upload dataset
s3.upload_file("dataset.tar.gz", "my-ml-bucket", "datasets/dataset.tar.gz")
# Stream to GPU instance
obj = s3.get_object(Bucket="my-ml-bucket", Key="models/llama3-70b.gguf")
data = obj["Body"].read()# Configure rclone rclone config create vultr s3 \ provider=Other \ endpoint=ewr1.vultrobjects.com \ access_key_id=YOUR_KEY \ secret_access_key=YOUR_SECRET # Sync datasets to GPU instance rclone sync vultr:my-ml-bucket/datasets ./datasets/ \ --transfers=32 --checkers=16 --progress
Related Technical Guides
Related Infrastructure Pages
Object Storage FAQ
Is Vultr Object Storage compatible with AWS S3?
Yes. Vultr Object Storage uses the S3-compatible API, meaning any tool that works with AWS S3 — boto3, rclone, s3fs, MinIO client, and AWS CLI — works natively with Vultr Object Storage with a simple endpoint change.
What are the pricing differences vs AWS S3?
Vultr Object Storage is significantly cheaper than AWS S3 for most use cases. Vultr charges per GB of storage with no per-request fees for standard operations, making it ideal for large ML dataset storage with frequent GPU instance access.
Can I mount Vultr Object Storage directly to a GPU instance?
Yes. You can mount Vultr Object Storage using s3fs-fuse or goofys, or access it via the S3-compatible API from Python (boto3) directly within training scripts. This enables streaming large datasets without local disk bottlenecks.
What data types are best suited for Vultr Object Storage?
ML training datasets, model weight files (GGUF, safetensors), inference artifacts, video assets for generative AI, database backups, and static website assets are all ideal. Object storage excels for files accessed by GPU compute instances.
Ready to Store AI Datasets on Vultr?
New accounts signed up via referral link may be eligible for promotional credits. Credits subject to Vultr's official program terms.