Vultr Object Storage – S3-kompatibler Cloud-Speicher für KI
Skalierbarer S3-kompatibler Objektspeicher, integriert mit Vultr GPU-Instanzen. Speichern Sie Trainingsdatensätze, Modell-Checkpoints und Produktionsartefakte kostengünstig.
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
Ist Vultr Object Storage mit AWS S3 kompatibel?
Ja. Vultr Object Storage verwendet die S3-kompatible API, das heißt, jedes Tool, das mit AWS S3 funktioniert – boto3, rclone, s3fs, MinIO-Client und AWS CLI – funktioniert nativ mit Vultr Object Storage durch einfache Änderung des Endpunkts.
Was sind die Preisunterschiede im Vergleich zu AWS S3?
Vultr Object Storage ist erheblich günstiger als AWS S3. Vultr berechnet pro GB Speicher ohne Gebühren pro Anfrage für Standardoperationen, was es ideal für die Speicherung großer ML-Datensätze mit häufigem GPU-Instanz-Zugriff macht.
Kann ich Vultr Object Storage direkt an eine GPU-Instanz mounten?
Ja. Sie können Vultr Object Storage mit s3fs-fuse oder goofys mounten oder über die S3-kompatible API von Python (boto3) innerhalb von Training-Skripten darauf zugreifen. Dies ermöglicht das Streamen großer Datensätze ohne lokale Festplatten-Engpässe.
Welche Datentypen eignen sich am besten für Vultr Object Storage?
ML-Trainingsdatensätze, Modellgewicht-Dateien (GGUF, safetensors), Inferenz-Artefakte, Video-Assets für generative KI, Datenbank-Backups und statische Website-Assets sind alle ideal für Objektspeicher.
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.