| name | vercel-cost-tuning |
| description | Optimize Vercel costs through plan selection, function efficiency, and usage monitoring.
Use when analyzing Vercel billing, reducing function execution costs,
or implementing spend management and budget alerts.
Trigger with phrases like "vercel cost", "vercel billing",
"reduce vercel costs", "vercel pricing", "vercel expensive", "vercel budget".
|
| allowed-tools | Read, Write, Edit, Bash(vercel:*), Bash(curl:*), Grep |
| version | 1.18.0 |
| license | MIT |
| author | Jeremy Longshore <jeremy@intentsolutions.io> |
| tags | ["saas","vercel","cost-optimization","billing","monitoring"] |
| compatibility | Designed for Claude Code |
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):
| Resource | Hobby (Free) | Pro ($20/member/mo) | Enterprise |
|---|
| Bandwidth | 100 GB | 1 TB included | Custom |
| Serverless Execution | 100 GB-hrs | 1000 GB-hrs included | Custom |
| Edge Function invocations | 500K | 1M included | Custom |
| Edge Middleware invocations | 1M | 1M included | Custom |
| Image Optimizations | 1000 | 5000 included | Custom |
| Builds per day | 6000 | 6000 | Custom |
| Concurrent builds | 1 | 1 (more available) | Custom |
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
curl -s -H "Authorization: Bearer $VERCEL_TOKEN" \
"https://api.vercel.com/v2/usage" | jq .
