Imported from jeremylongshore/tons-of-skills-marketplace (skills/.curated/vercel-cost-tuning/SKILL.md). Install upstream with npx skills add jeremylongshore/tons-of-skills-marketplace --skill vercel-cost-tuning. Copyright stays with the author (MIT).

Vercel Cost Tuning

Overview

Optimize Vercel costs by understanding the Fluid Compute pricing model, reducing function execution time, leveraging edge caching to avoid function invocations, and configuring spend management. Covers plan comparison, cost drivers, and monitoring.

Prerequisites

• Access to Vercel billing dashboard
• Understanding of current deployment architecture
• Access to Vercel Analytics for usage patterns

Instructions

Step 1: Understand the Pricing Model

Vercel uses Fluid Compute pricing (for new projects):

Fluid Compute billing breakdown:

• Active CPU time: charged per ms of actual CPU usage
• Provisioned memory: charged per GB-second of allocated memory
• Benefit: you pay for actual work, not idle waiting (e.g., waiting for a database response)

Step 2: Identify Cost Drivers

# Check usage via API
curl -s -H "Authorization: Bearer $VERCEL_TOKEN" \
  "https://api.vercel.com/v2/usage" | jq .

# Top cost drivers on Vercel:
# 1. Serverless function execution time (CPU + memory)
# 2. Bandwidth (large responses, unoptimized images)
# 3. Edge Middleware invocations (runs on EVERY request)
# 4. Image optimizations (each unique transform costs)
# 5. Build minutes (frequent deploys or slow builds)

Step 3: Reduce Function Execution Costs

// 1. Right-size function memory — don't over-allocate
// vercel.json
{
  "functions": {
    "api/lightweight.ts": { "memory": 128 },    // Simple JSON responses
    "api/standard.ts": { "memory": 512 },       // Database queries
    "api/heavy.ts": { "memory": 1024 }          // Image processing
  }
}

// 2. Move read-only endpoints to Edge Functions (cheaper, no cold starts)
// api/config.ts
export const config = { runtime: 'edge' };
export default function handler() {
  return Response.json({ features: ['a', 'b'] });
}

// 3. Cache function responses at the edge
// Eliminates function invocations entirely for cached routes
export default function handler(req, res) {
  res.setHeader('Cache-Control', 's-maxage=3600, stale-while-revalidate=86400');
  res.json(data);
}

Step 4: Reduce Bandwidth Costs

// vercel.json — compress and cache aggressively
{
  "headers": [
    {
      "source": "/static/(.*)",
      "headers": [
        { "key": "Cache-Control", "value": "public, max-age=31536000, immutable" }
      ]
    }
  ],
  "images": {
    "sizes": [640, 750, 1080],
    "formats": ["image/avif", "image/webp"],
    "minimumCacheTTL": 86400
  }
}

Key bandwidth reducers:

• Use Vercel's image optimization (auto WebP/AVIF conversion)
• Set aggressive cache headers on static assets
• Use ISR to serve static HTML instead of SSR
• Compress API responses (Vercel auto-compresses with Brotli)

Step 5: Optimize Middleware Costs

Middleware runs on every matched request. Minimize its scope:

// middleware.ts — scope to specific paths only
export const config = {
  matcher: [
    // Only run middleware on API routes and protected pages
    '/api/:path*',
    '/dashboard/:path*',
    // Skip static files, images, and public assets
    '/((?!_next/static|_next/image|favicon.ico|public).*)',
  ],
};

export function middleware(request) {
  // Keep logic minimal — this runs on every matched request
  // Avoid: database queries, external API calls, heavy computation
  // Good: cookie checks, header modifications, redirects
}

Step 6: Configure Spend Management

In the Vercel dashboard under Settings > Billing > Spend Management:

Default budget: $200/month on-demand usage
Options:
- Set custom budget limit
- Enable hard limit (pauses all projects when reached)
- Configure email alerts at 50%, 75%, 90%, 100%

# Check current usage against budget via API
curl -s -H "Authorization: Bearer $VERCEL_TOKEN" \
  "https://api.vercel.com/v2/usage?teamId=team_xxx" \
  | jq '{period: .period, bandwidth: .bandwidth, execution: .serverlessFunctionExecution}'

Cost Optimization Checklist

Output

• Function memory right-sized per endpoint
• Edge caching reducing function invocations
• Middleware scoped to minimize invocations
• Spend management configured with budget alerts
• Usage monitoring via API

Error Handling

Examples

Reduce an invocation spike with a measurable rollback

For a high-traffic read endpoint, add a conservative s-maxage response policy in a preview deployment and compare invocation count, cache hit rate, and response correctness against the prior release using synthetic requests. Set spend alerts before rollout and avoid a hard account-wide pause unless the owner accepts its blast radius. If personalized content is accidentally cached, immediately roll back the header change, purge only the affected deployment cache where supported, and document the regression.

Resources

• Vercel Pricing
• Fluid Compute Pricing
• Spend Management
• Limits
• Usage API

Next Steps

For reference architecture, see vercel-reference-architecture.