Vercel Load Scale

# Install autocannon
npm install -g autocannon

# Test with 50 concurrent connections for 30 seconds
autocannon -c 50 -d 30 https://my-app-preview.vercel.app/api/endpoint

# Output includes:
# Latency: avg, p50, p99, max
# Requests/sec: avg, min, max
# Errors: timeouts, non-2xx responses

// load-test.js
import http from 'k6/http';
import { check, sleep } from 'k6';
import { Rate, Trend } from 'k6/metrics';

const errorRate = new Rate('errors');
const coldStartRate = new Rate('cold_starts');
const latency = new Trend('api_latency');

export const options = {
  stages: [
    { duration: '1m', target: 10 },   // Warm up
    { duration: '3m', target: 50 },   // Ramp to 50 users
    { duration: '2m', target: 100 },  // Peak load
    { duration: '1m', target: 0 },    // Cool down
  ],
  thresholds: {
    http_req_duration: ['p(95)<2000'],  // P95 < 2s
    errors: ['rate<0.01'],              // Error rate < 1%
  },
};

export default function () {
  const res = http.get('https://my-app-preview.vercel.app/api/endpoint');

  check(res, {
    'status is 200': (r) => r.status === 200,
    'latency < 2s': (r) => r.timings.duration < 2000,
  });

  errorRate.add(res.status !== 200);
  latency.add(res.timings.duration);

  // Track cold starts if your API returns this header
  if (res.headers['X-Cold-Start'] === 'true') {
    coldStartRate.add(1);
  }

  sleep(1);
}

# Run the load test
k6 run load-test.js

# Run with output to JSON for analysis
k6 run --out json=results.json load-test.js

// cold-start-test.js — specifically test cold start behavior
import http from 'k6/http';
import { sleep } from 'k6';

export const options = {
  scenarios: {
    // Scenario 1: Sustained load (warm instances)
    sustained: {
      executor: 'constant-arrival-rate',
      rate: 10,
      timeUnit: '1s',
      duration: '2m',
      preAllocatedVUs: 20,
    },
    // Scenario 2: Spike (forces new cold starts)
    spike: {
      executor: 'ramping-arrival-rate',
      startRate: 10,
      timeUnit: '1s',
      stages: [
        { target: 200, duration: '10s' },  // Sudden spike
        { target: 10, duration: '1m' },     // Return to normal
      ],
      preAllocatedVUs: 300,
      startTime: '2m',  // Start after sustained phase
    },
  },
};

export default function () {
  const res = http.get('https://my-app-preview.vercel.app/api/endpoint');
  // Log cold start timing for analysis
}

// vercel.json — configure concurrency for Fluid Compute (Pro/Enterprise)
{
  "functions": {
    "api/high-throughput.ts": {
      "memory": 1024,
      "maxDuration": 30,
      "concurrency": 10
    }
  }
}

Capacity Planning Formula:

  Required instances = Peak RPS * Avg Response Time (seconds)

  Example:
  - Peak: 500 requests/second
  - Avg response: 200ms (0.2s)
  - Required: 500 * 0.2 = 100 concurrent instances

  With Fluid Compute (concurrency=10):
  - Required: 500 * 0.2 / 10 = 10 concurrent instances

  Plan check:
  - Hobby (10 concurrent): NOT sufficient
  - Pro (1000 concurrent): Sufficient with headroom

## Load Test Report — [Date]

### Configuration
- Target: https://my-app-preview.vercel.app/api/endpoint
- Tool: k6 v0.50
- Duration: 7 minutes (ramp up → peak → cool down)
- Peak concurrent users: 100

### Results
| Metric | Value |
|--------|-------|
| Total requests | 12,450 |
| Success rate | 99.8% |
| P50 latency | 45ms |
| P95 latency | 320ms |
| P99 latency | 1,200ms |
| Max latency | 3,400ms |
| Cold start % | 8% |
| Avg cold start duration | 650ms |
| Throttled (429) | 0 |

### Recommendations
1. Cold start: 650ms avg — consider Edge Functions for latency-critical paths
2. P99 spike: caused by cold starts — Fluid Compute concurrency would help
3. No throttling at 100 concurrent — Pro plan (1000 limit) is sufficient