vastai-observability

name	vastai-observability
description	Monitor Vast.ai GPU instance health, utilization, and costs. Use when setting up monitoring dashboards, configuring alerts, or tracking GPU utilization and spending. Trigger with phrases like "vastai monitoring", "vastai metrics", "vastai observability", "monitor vastai", "vastai alerts".
allowed-tools	Read, Write, Edit, Bash(vastai:*), Bash(curl:*)
version	1.11.0
license	MIT
author	Jeremy Longshore <[e-mail]>
tags	["saas","vast-ai","monitoring","observability"]
compatibility	Designed for Claude Code

Vast.ai Observability

Overview

Monitor Vast.ai GPU instance health, utilization, and costs. Key metrics: GPU utilization (idle GPUs waste $0.20-$4.00/hr), instance uptime, training progress, cost accumulation, and spot preemption events.

Prerequisites

• Vast.ai account with active instances
• vastai CLI installed
• Optional: Prometheus, Grafana, or Datadog for dashboarding

Instructions

Step 1: Instance Metrics Collector

import subprocess, json, time
from datetime import datetime

class VastMetricsCollector:
    def __init__(self, output_file="vast_metrics.jsonl"):
        self.output_file = output_file

    def collect(self):
        result = subprocess.run(
            ["vastai", "show", "instances", "--raw"],
            capture_output=True, text=True)
        instances = json.loads(result.stdout)

        metrics = {
            "timestamp": datetime.utcnow().isoformat(),
            "total_instances": len(instances),
            "running": 0, "total_hourly_cost": 0,
            "instances": [],
        }

        for inst in instances:
            status = inst.get("actual_status", "unknown")
            dph = inst.get("dph_total", 0)
            if status == "running":
                metrics["running"] += 
                metrics[] += dph

            metrics[].append({
                : inst[],
                : inst.get(),
                : status,
                : dph,
                : inst.get(, ),
                : inst.get(, ),
            })

         (.output_file, )  f:
            f.write(json.dumps(metrics) + )

         metrics

     ():
         :
            m = .collect()
            (
                  )
            time.sleep(interval)