# Jeremylongshore Claude Code Plugins Plus Skills Vastai Cost Tuning

> Vast.ai Cost Tuning

- Skill: `tomevault-io/jeremylongshore-claude-code-plugins-plus-skills-vastai-cost-` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds@latest add tomevault-io/jeremylongshore-claude-code-plugins-plus-skills-vastai-cost-`
- Raw SKILL.md: https://api.skillmd.com/api/skills/tomevault-io/jeremylongshore-claude-code-plugins-plus-skills-vastai-cost-/raw
- Safety review: pending (external: skill-scanner WARNING, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: tomevault-io (https://skillmd.com/u/tomevault-io)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/tomevault-io/jeremylongshore-claude-code-plugins-plus-skills-vastai-cost-

---

# Vast.ai Cost Tuning

## Overview
Minimize Vast.ai GPU cloud costs by choosing the right GPU for your workload, leveraging interruptible (spot) instances, eliminating idle compute, and implementing auto-destroy safeguards. Vast.ai pricing is dynamic and varies significantly: RTX 4090 ($0.15-0.30/hr), A100 80GB ($1.00-2.00/hr), H100 SXM ($2.50-4.00/hr).

## Prerequisites
- Vast.ai account with billing history
- Understanding of your workload's GPU requirements
- `vastai` CLI installed

## Instructions

### Step 1: GPU Selection by Cost-Efficiency

```python
# Compare cost-per-TFLOP across GPU types
GPU_SPECS = {
    "RTX_4090":  {"fp16_tflops": 82.6,  "vram": 24},
    "A100":      {"fp16_tflops": 77.97, "vram": 80},
    "H100_SXM":  {"fp16_tflops": 267,   "vram": 80},
    "RTX_3090":  {"fp16_tflops": 35.6,  "vram": 24},
    "A6000":     {"fp16_tflops": 38.7,  "vram": 48},
}

def cost_per_tflop(gpu_name, dph):
    specs = GPU_SPECS.get(gpu_name, {"fp16_tflops": 1})
    return dph / specs["fp16_tflops"]

# Often RTX 4090 is the best value for inference
# A100 is best for training large models needing >24GB VRAM
# H100 is best only when wall-clock time justifies 10x price premium
```

### Step 2: Spot vs On-Demand Analysis

```bash
# Interruptible (spot) instances are 30-60% cheaper
vastai search offers 'num_gpus=1 gpu_name=RTX_4090 rentable=true' \
  --order dph_total --limit 5
# Compare interruptible vs on-demand pricing
# Use interruptible for: batch inference, checkpointed training
# Use on-demand for: final training epochs, production inference
```

### Step 3: Auto-Destroy Safeguards

```python
import time, subprocess, json

def auto_destroy_after(instance_id, max_hours=4):
    """Destroy instance after max_hours to prevent cost overruns."""
    max_seconds = max_hours * 3600
    time.sleep(max_seconds)
    subprocess.run(["vastai", "destroy", "instance", str(instance_id)], check=True)
    print(f"Instance {instance_id} auto-destroyed after {max_hours}h")

# Run in background thread when provisioning
import threading
watchdog = threading.Thread(target=auto_destroy_after, args=(inst_id, 4), daemon=True)
watchdog.start()
```

### Step 4: Idle Instance Detection

```bash
#!/bin/bash
# Find and destroy idle instances (GPU util < 10% for >10 min)
vastai show instances --raw | python3 -c "
import sys, json
for inst in json.load(sys.stdin):
    if inst.get('actual_status') == 'running':
        gpu_util = inst.get('gpu_util', 0)
        if gpu_util < 10:
            print(f'IDLE: Instance {inst[\"id\"]} GPU util={gpu_util}% '
                  f'(\${inst.get(\"dph_total\", 0):.3f}/hr)')
"
```

### Step 5: Cost Reporting

```python
def daily_cost_report():
    """Calculate current daily burn rate from running instances."""
    result = subprocess.run(
        ["vastai", "show", "instances", "--raw"],
        capture_output=True, text=True)
    instances = json.loads(result.stdout)

    total_hourly = 0
    for inst in instances:
        if inst.get("actual_status") == "running":
            dph = inst.get("dph_total", 0)
            total_hourly += dph
            print(f"  {inst['id']}: {inst.get('gpu_name')} ${dph:.3f}/hr")

    print(f"\nTotal: ${total_hourly:.3f}/hr = ${total_hourly * 24:.2f}/day")
```

## Cost Optimization Checklist

- [ ] Always search with `--order dph_total` to find cheapest offers
- [ ] Use interruptible instances for checkpointed workloads
- [ ] Implement auto-destroy timeout on all instances
- [ ] Monitor GPU utilization; destroy idle instances
- [ ] Use RTX 4090 for workloads that fit in 24GB VRAM
- [ ] Only use H100 when wall-clock time savings justify cost premium
- [ ] Pre-install dependencies in Docker images (avoid paying for pip install)

## Output
- GPU cost-efficiency analysis by model
- Spot vs on-demand comparison
- Auto-destroy watchdog for cost protection
- Idle instance detection script
- Daily cost burn rate report

## Error Handling
| Error | Cause | Solution |
|-------|-------|----------|
| Unexpected $50+ bill | Forgot to destroy instances | Implement auto-destroy watchdog |
| GPU idle at $2/hr | Waiting for data download | Pre-stage data before provisioning GPU |
| Spot preemption mid-job | Cheapest instance reclaimed | Checkpoint frequently; auto-recover |

## Resources
- [Vast.ai Pricing](https://vast.ai/)
- [Search & Filter](https://docs.vast.ai/search-and-filter-gpu-offers)

## Next Steps
For reference architecture, see `vastai-reference-architecture`.

## Examples

**Budget cap**: Set `dph_total<=0.25` in search queries and `auto_destroy_after(inst_id, 4)` to cap any single job at $1.00.

**GPU comparison**: Run the same workload on RTX 4090 ($0.20/hr) vs A100 ($1.50/hr). If the A100 finishes in less than 1/7th the time, it's cheaper overall.

---
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