# Jeremylongshore Claude Code Plugins Plus Skills Vercel Load Scale

> Vercel Load & Scale

- Skill: `tomevault-io/jeremylongshore-claude-code-plugins-plus-skills-vercel-load-` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds@latest add tomevault-io/jeremylongshore-claude-code-plugins-plus-skills-vercel-load-`
- Raw SKILL.md: https://api.skillmd.com/api/skills/tomevault-io/jeremylongshore-claude-code-plugins-plus-skills-vercel-load-/raw
- Safety review: pending (external: skill-scanner PASS, 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-vercel-load-

---

# Vercel Load & Scale

## Overview
Load test Vercel deployments to identify scaling limits, cold start impact, and concurrency thresholds. Covers k6/autocannon test scripts, Vercel's auto-scaling model, Fluid Compute concurrency, and capacity planning.

## Prerequisites
- Load testing tool: k6, autocannon, or artillery
- Test environment deployment (never load test production without approval)
- Access to Vercel Analytics for monitoring during tests

## Instructions

### Step 1: Understand Vercel's Scaling Model
Vercel serverless functions scale automatically:

| Behavior | Details |
|----------|---------|
| Scale-up | New function instances spawn on demand |
| Scale-down | Idle instances shut down after ~15 minutes |
| Cold starts | First request to a new instance pays initialization cost |
| Concurrency | Each instance handles one request at a time (by default) |
| Fluid Compute | Pro/Enterprise: multiple requests per instance |

**Concurrency limits by plan:**

| Plan | Max Concurrent Functions |
|------|------------------------|
| Hobby | 10 |
| Pro | 1,000 |
| Enterprise | 100,000 |

### Step 2: Basic Load Test with autocannon
```bash
# 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
```

### Step 3: k6 Load Test Script
```javascript
// 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);
}
```

```bash
# 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
```

### Step 4: Cold Start Stress Test
```javascript
// 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
}
```

### Step 5: Fluid Compute Concurrency Tuning
```json
// vercel.json — configure concurrency for Fluid Compute (Pro/Enterprise)
{
  "functions": {
    "api/high-throughput.ts": {
      "memory": 1024,
      "maxDuration": 30,
      "concurrency": 10
    }
  }
}
```

With Fluid Compute concurrency, a single function instance handles multiple requests:
- Reduces cold starts (fewer instances needed)
- Reduces cost (shared memory across requests)
- Best for I/O-bound functions (waiting on DB/API calls)
- Not ideal for CPU-bound functions (computation blocks other requests)

### Step 6: Capacity Planning
```
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 Results Template

```markdown
## 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
```

## Output
- Load test scripts for sustained and spike traffic scenarios
- Cold start frequency and duration measured
- Concurrency limits tested and validated
- Capacity plan with scaling recommendations
- Benchmark results documented

## Error Handling
| Error | Cause | Solution |
|-------|-------|----------|
| `FUNCTION_THROTTLED` (429) | Exceeded concurrent limit | Reduce test concurrency or upgrade plan |
| Vercel blocks load test | Not from approved IP | Contact Vercel support before load testing |
| High P99 but low P50 | Cold starts on spikes | Use Fluid Compute concurrency or Edge Functions |
| All requests timeout | Function region far from test origin | Set `regions` in vercel.json closer to test source |
| Inconsistent results | Shared infrastructure variability | Run multiple test rounds, use median results |

## Resources
- [Vercel Function Limits](https://vercel.com/docs/functions/limitations)
- [Concurrency Scaling](https://vercel.com/docs/functions/concurrency-scaling)
- [Fluid Compute](https://vercel.com/docs/functions/usage-and-pricing)
- [k6 Documentation](https://k6.io/docs/)
- [Vercel Load Testing Policy](https://vercel.com/kb/guide/what-s-vercel-s-policy-regarding-load-testing-deployments)

## Next Steps
For reliability patterns, see `vercel-reliability-patterns`.

---
> Converted and distributed by [TomeVault](https://tomevault.io/claim/jeremylongshore) — claim your Tome and manage your conversions.
<!-- tomevault:4.0:skill_md:2026-04-11 -->