vercel-performance-tuning

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Optimize Vercel API performance with caching, batching, and connection pooling. Use when experiencing slow API responses, implementing caching strategies, or optimizing request throughput for Vercel integrations. Trigger with phrases like "vercel performance", "optimize vercel", "vercel latency", "vercel caching", "vercel slow", "vercel batch".

Install

mkdir -p .claude/skills/vercel-performance-tuning && curl -L -o skill.zip "https://mcp.directory/api/skills/download/7837" && unzip -o skill.zip -d .claude/skills/vercel-performance-tuning && rm skill.zip

Installs to .claude/skills/vercel-performance-tuning

About this skill

Vercel Performance Tuning

Overview

Optimize Vercel deployment performance across four levers: edge caching, bundle size reduction, serverless function cold start elimination, and Core Web Vitals improvement. Uses real Vercel cache headers, ISR, and Edge Functions for maximum performance.

Prerequisites

  • Vercel project deployed with accessible URL
  • Access to Vercel Analytics (dashboard)
  • Bundle analyzer available (@next/bundle-analyzer or similar)

Instructions

Step 1: Establish Performance Baseline

# Check deployment size and function count
vercel inspect https://my-app.vercel.app

# Run Lighthouse via CLI
npx lighthouse https://my-app.vercel.app --output=json --quiet \
  | jq '{performance: .categories.performance.score, lcp: .audits["largest-contentful-paint"].numericValue, cls: .audits["cumulative-layout-shift"].numericValue}'

# Check bundle size (Next.js)
ANALYZE=true npx next build
# Opens bundle analyzer report in browser

Enable Vercel Analytics in the dashboard under Analytics tab for ongoing monitoring.

Step 2: Configure Edge Caching

// api/cached-data.ts — cache API responses at the edge
import type { VercelRequest, VercelResponse } from '@vercel/node';

export default function handler(req: VercelRequest, res: VercelResponse) {
  // Cache at Vercel edge for 60s, serve stale for 300s while revalidating
  res.setHeader('Cache-Control', 's-maxage=60, stale-while-revalidate=300');
  res.json({ data: fetchData(), cachedAt: new Date().toISOString() });
}
// vercel.json — cache static assets aggressively
{
  "headers": [
    {
      "source": "/static/(.*)",
      "headers": [
        { "key": "Cache-Control", "value": "public, max-age=31536000, immutable" }
      ]
    },
    {
      "source": "/api/public-data",
      "headers": [
        { "key": "Cache-Control", "value": "s-maxage=3600, stale-while-revalidate=86400" }
      ]
    }
  ]
}

Cache header reference:

HeaderEffect
s-maxage=NCache at Vercel edge for N seconds
stale-while-revalidate=NServe stale while revalidating in background
max-age=NCache in browser for N seconds
immutableNever revalidate (use with content-hashed filenames)
no-cacheAlways revalidate (edge still caches)
no-storeNever cache anywhere

Step 3: Incremental Static Regeneration (ISR)

// app/products/[id]/page.tsx (Next.js App Router)
export const revalidate = 60; // Revalidate every 60 seconds

export default async function ProductPage({ params }) {
  const product = await fetchProduct(params.id);
  return <ProductView product={product} />;
}

// Generate static pages at build time, regenerate on-demand
export async function generateStaticParams() {
  const products = await fetchTopProducts(100);
  return products.map(p => ({ id: p.id }));
}

On-demand revalidation via API route:

// api/revalidate.ts
import type { VercelRequest, VercelResponse } from '@vercel/node';

export default async function handler(req: VercelRequest, res: VercelResponse) {
  const secret = req.query.secret;
  if (secret !== process.env.REVALIDATION_SECRET) {
    return res.status(401).json({ error: 'Invalid secret' });
  }

  const path = req.query.path as string;
  await res.revalidate(path);
  res.json({ revalidated: true, path });
}
// Trigger: POST /api/revalidate?secret=xxx&path=/products/123

Step 4: Reduce Cold Starts

// Lazy initialization — don't import heavy modules at top level
// BAD: Cold start loads everything
import { PrismaClient } from '@prisma/client';
const prisma = new PrismaClient(); // Runs on every cold start

// GOOD: Lazy singleton — only connects when first used
let prisma: PrismaClient | null = null;
function getDb(): PrismaClient {
  if (!prisma) {
    prisma = new PrismaClient();
  }
  return prisma;
}

export default async function handler(req, res) {
  const users = await getDb().user.findMany();
  res.json(users);
}

Move latency-critical paths to Edge Functions (zero cold starts):

// api/fast.ts
export const config = { runtime: 'edge' };

export default function handler(request: Request) {
  return Response.json({ fast: true }); // No cold start, runs globally
}

Step 5: Bundle Size Optimization

// next.config.js — tree-shaking and optimization
module.exports = {
  experimental: {
    optimizePackageImports: ['lodash', '@mui/material', '@mui/icons-material'],
  },
  // Exclude server-only deps from client bundle
  webpack: (config, { isServer }) => {
    if (!isServer) {
      config.resolve.fallback = { fs: false, net: false, tls: false };
    }
    return config;
  },
};
# Find large dependencies
npx depcheck
npx cost-of-modules

# Replace heavy libraries with lighter alternatives
# moment.js (300KB) → dayjs (2KB)
# lodash (72KB) → lodash-es with tree-shaking
# axios (29KB) → native fetch

Step 6: Image Optimization

// Use Vercel's built-in image optimization
import Image from 'next/image';

// Automatic: resizes, converts to WebP/AVIF, caches at edge
<Image
  src="/hero.jpg"
  width={1200}
  height={600}
  alt="Hero"
  priority  // Preload for LCP
  sizes="(max-width: 768px) 100vw, 1200px"
/>
// vercel.json — configure image optimization
{
  "images": {
    "sizes": [640, 750, 828, 1080, 1200],
    "domains": ["images.example.com"],
    "formats": ["image/avif", "image/webp"],
    "minimumCacheTTL": 86400
  }
}

Performance Budget Reference

MetricTargetVercel Tool
LCP< 2.5sVercel Analytics
FID/INP< 200msVercel Analytics
CLS< 0.1Vercel Analytics
TTFB< 200msEdge caching
Function cold start< 500msLazy init / Edge Functions
Bundle size (gzipped)< 200KB JSBundle analyzer

Output

  • Edge caching configured with optimal cache-control headers
  • ISR or on-demand revalidation for dynamic pages
  • Cold starts eliminated via lazy initialization and Edge Functions
  • Bundle size reduced through tree-shaking and import optimization
  • Image optimization configured

Error Handling

ErrorCauseSolution
Cache not hittingMissing s-maxage headerAdd to response or vercel.json headers
ISR page always stalerevalidate set too highLower the revalidation interval
Large bundle warningImporting entire libraryUse specific imports: import { map } from 'lodash-es'
Cold start > 1sHeavy top-level importsMove to lazy initialization pattern
Images not optimizedExternal domain not whitelistedAdd to images.domains in config

Resources

Next Steps

For cost optimization, see vercel-cost-tuning.

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