vercel-cost-tuning

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 <[e-mail]>
tags	["saas","vercel","cost-optimization","billing","monitoring"]
compatibility	Designed for Claude Code, also compatible with Codex and OpenClaw

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

# 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)