vastai-migration-deep-dive

name	vastai-migration-deep-dive
description	Migrate GPU workloads to or from Vast.ai, or between GPU providers. Use when switching from AWS/GCP/Azure GPU instances to Vast.ai, migrating between GPU types, or re-platforming ML infrastructure. Trigger with phrases like "migrate to vastai", "vastai migration", "switch to vastai", "vastai from aws", "vastai from lambda".
allowed-tools	Read, Write, Edit, Bash(vastai:*), Bash(docker:*), Grep
version	1.11.0
license	MIT
author	Jeremy Longshore <[e-mail]>
tags	["saas","vast-ai","migration"]
compatibility	Designed for Claude Code

Vast.ai Migration Deep Dive

Current State

!vastai --version 2>/dev/null || echo 'vastai CLI not installed' !pip show vastai 2>/dev/null | grep Version || echo 'N/A'

Overview

Migrate GPU workloads to Vast.ai from hyperscaler providers (AWS, GCP, Azure) or other GPU clouds (Lambda, RunPod, CoreWeave). Also covers migrating between GPU types on Vast.ai and the reverse migration away from Vast.ai.

Prerequisites

• Existing GPU workload with Docker image
• Understanding of current GPU costs and utilization
• Checkpoint-based training pipeline (for training migrations)

Instructions

Step 1: Cost Comparison Analysis

# Compare your current GPU costs against Vast.ai marketplace prices
PROVIDER_COSTS = {
    "aws_p4d.24xlarge":      {"gpu": "A100 40GB", "gpus": 8, "hourly": 32.77},
    "aws_p3.2xlarge":        {"gpu": "V100 16GB", "gpus": 1, "hourly": 3.06},
    "gcp_a2-highgpu-1g":     {"gpu": "A100 40GB", "gpus": 1, "hourly": 3.67},
    "azure_NC24ads_A100_v4": {"gpu": "A100 80GB", "gpus": 1, "hourly": 3.67},
    "lambda_1xA100":         {"gpu": "A100",      "gpus": 1, "hourly": 1.25},
}

VASTAI_TYPICAL = {
    "RTX_4090":  ,
    :      ,
    :  ,
}

 ():
    monthly_current = current_hourly *   
    monthly_vastai = vastai_hourly * 
    savings = monthly_current - monthly_vastai
    pct = (savings / monthly_current) * 
    ()
    ()
    ()

savings_analysis(, , , )