vaultlayer 0.1.14

AI compute arbitrage CLI — move GPU training jobs between clouds automatically

VaultLayer

Run AI training jobs across 11 GPU cloud providers — 60–70% cheaper than AWS on-demand, with 99.9% job completion SLA, using your existing commands unchanged. 93% GPU cloud market coverage (~$39B addressable).

pip install vaultlayer
vaultlayer run python train.py --model llama-3-7b --epochs 10

✓ Job completed in 4h 32m
✓ 1 interruption recovered automatically (AWS Spot → Lambda H100)
✓ Saved $142.40 vs AWS On-Demand
→ View full report: https://vaultlayer.pages.dev/jobs/j-0042

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What It Does

VaultLayer sits between your training script and the cloud. It:

• Checkpoints automatically — syncs model weights + optimizer state to a zero-egress R2 Vault on every save
• Detects interruptions — intercepts AWS/GCP/Azure termination signals before your job dies
• Migrates instantly — provisions a replacement node on the cheapest available provider and resumes from last checkpoint
• Tracks savings — shows real-time cost vs what you would have paid on AWS On-Demand

No changes to your PyTorch or JAX code. No YAML configs. No PhD-level infra knowledge required.

Commands

# Training
vaultlayer run python train.py                                         # run with full protection
vaultlayer run --data s3://bucket/prefix python train.py              # mirror S3→R2 then run
vaultlayer run --data r2://my-dataset python train.py                 # use dataset already in R2
vaultlayer run --regions eu-central-1,eu-west-1 python train.py      # GDPR-only regions
vaultlayer run --excluded-regions cn-north-1 python train.py          # never use China region
vaultlayer stop <job-id>                                               # graceful stop + checkpoint
vaultlayer logs <job-id> [--tail N] [--follow]                        # stream logs from R2

# Dataset storage (no S3 required)
vaultlayer sync ./data --dataset-id my-dataset                        # upload local data → R2
vaultlayer sync s3://bucket/prefix --dataset-id my-dataset            # mirror S3 → R2 (one-time egress)
vaultlayer datasets                                                    # list datasets + storage costs
vaultlayer datasets --delete my-dataset                                # delete + stop billing

# Region discovery
vaultlayer regions list-all                                            # list all valid regions + compliance notes
vaultlayer regions current                                             # show current provisioning region

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Supported Providers

Provider	Type	Status
AWS EC2 Spot	Hyperscaler	✅ Live
Lambda Labs	Neocloud	✅ Live
CoreWeave	Neocloud	✅ Live
RunPod	Neocloud	✅ Live
Vast.ai	Neocloud	✅ Live
Voltage Park	Neocloud	✅ Live
Crusoe	Neocloud	✅ Live
Nebius	Neocloud	✅ Live
Hyperstack	Neocloud	✅ Live
GCP	Hyperscaler	✅ Live
Azure	Hyperscaler	✅ Live
AWS On-Demand	Hyperscaler	✅ Last-resort fallback

11 providers live — 93% GPU cloud market coverage (~$39B addressable, ~$21B migratable training).

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Model Size Support

Model Size	Method	Checkpoint Size	Status
7B	Full fine-tune	~69 GB	✅ MVP
13B	Full fine-tune	~125 GB	✅ MVP
30B	Full fine-tune	~288 GB	✅ MVP
70B	QLoRA (4-bit)	~46 GB	✅ MVP
70B	Full fine-tune	~782 GB	🔜 Phase 2

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Tech Stack

Layer	Technology	Cost
Code + Docs	GitHub (this repo)	Free
CI/CD	GitHub Actions	Free (2k min/mo)
Vault / Storage	Cloudflare R2	Free up to 10GB
Agent Runtime	Railway	Free $5/mo credit
Webhooks	Cloudflare Workers	Free 100k req/day
Agent Message Queue	Upstash Redis	Free 10k cmd/day

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Repository Structure

vaultlayer/
├── README.md
├── docs/
│   ├── PRD.md              # Full product requirements
│   ├── ARCHITECTURE.md     # System design + agent topology
│   └── AGENTS.md           # Agent specs + build order
├── dashboard/
│   └── index.html          # Savings dashboard prototype
└── src/
    ├── cli/
    │   ├── main.py
    │   ├── run.py
    │   ├── checkpoint_template.py
    │   └── init.py
    ├── vaultlayer/
    │   └── _resume_hook.py
    ├── agents/
    │   ├── orchestration/
    │   ├── pricing/
    │   ├── watchdog/
    │   │   └── signals.py
    │   ├── vault/
    │   ├── broker/
    │   ├── finops/
    │   └── namespace/
    └── shared/

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SLA

99.9% job completion rate — not node uptime. Jobs survive infrastructure failures. Recovery SLA: interrupted job resumes within 10 minutes from last checkpoint.

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Dataset Storage (No S3 Required)

VaultLayer's Neutral Zone (Cloudflare R2) is a first-class storage provider. Users with no AWS or cloud storage account can upload training data directly and train from it on any provider.

# Upload from your laptop / on-prem server
vaultlayer sync ./training-data --dataset-id my-dataset

# Train — data is mounted at /mnt/vaultlayer on every provisioned node
vaultlayer run --data r2://my-dataset python train.py

# See what you're storing and the monthly cost
vaultlayer datasets

Pricing:

Action	Cost
Upload (local → R2)	Free
Storage	$0.020 / GB / month ($0.0195 — 30% markup over Cloudflare R2 base rate)
Read (R2 → training node)	$0.00 (zero egress within Cloudflare network)
S3 mirror (one-time)	AWS egress charge (~$0.09/GB, first 100 GB/month free)

Storage quotas by plan:

Plan	Storage limit
Free	10 GB
Pro	500 GB
Enterprise	Unlimited

Datasets are soft-deleted with vaultlayer datasets --delete <id> — billing stops immediately, R2 objects are purged within 24 hours.

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Region Control

VaultLayer can provision nodes in any AWS region that has GPU capacity. By default it uses the region from your vaultlayer init configuration.

Restrict to specific regions (e.g. GDPR compliance — EU data stays in EU):

vaultlayer run --regions eu-central-1,eu-west-1 python train.py

Exclude regions (e.g. avoid China, GovCloud, sanctioned territories):

vaultlayer run --excluded-regions cn-north-1,cn-northwest-1 python train.py

If both flags are given, --excluded-regions takes priority. If neither is given, any region is allowed.

Discover regions:

vaultlayer regions list-all    # all GPU-capable regions with compliance notes
vaultlayer regions current     # show which region your credentials point to

Compliance note: H100/A100 exports to certain regions (China, Russia, some Middle East countries) may require a US Bureau of Industry and Security (BIS) export license. VaultLayer blocks cn-north-1, cn-northwest-1, and ru-central-1 by default via OFAC screening. Use --regions to limit jobs to GDPR-compliant EU regions.

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Getting Started

pip install vaultlayer
vaultlayer init
vaultlayer run python train.py

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License

Private — © 2026 VaultLayer