vastai-cost-tuning

name	vastai-cost-tuning
description	Optimize Vast.ai GPU cloud costs through smart instance selection and lifecycle management. Use when analyzing GPU spending, reducing training costs, or implementing budget controls for Vast.ai workloads. Trigger with phrases like "vastai cost", "vastai billing", "reduce vastai costs", "vastai pricing", "vastai budget".
allowed-tools	Read, Write, Edit, Bash(vastai:*), Grep
version	1.11.0
license	MIT
author	Jeremy Longshore <[e-mail]>
tags	["saas","vast-ai","api","cost-optimization"]
compatibility	Designed for Claude Code, also compatible with Codex and OpenClaw

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

# 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