Edge computing pricing gets complex when you move beyond the marketing examples. These platforms optimize for different use cases, and picking wrong can double your compute costs.
Team Size Kills Vercel Budgets
Vercel's per-seat pricing becomes a nightmare for development teams. At $20/month per developer, a 10-person team pays $200/month before writing a single line of code. Compare that to Cloudflare Workers where team size doesn't affect pricing at all.
For startups scaling their engineering teams, Vercel costs can grow faster than revenue. A team that grows from 5 to 15 developers sees their base costs jump from $100 to $300/month just for platform access. Recent analysis shows this can result in 35% higher hosting costs compared to alternatives.
AWS Lambda@Edge Memory Billing Trap
AWS charges for memory allocation throughout function execution, including I/O wait time. If your function allocates 512MB but spends 80% of its time waiting for database responses, you're still paying for 512MB the entire time.
AWS Lambda@Edge pricing charges roughly $0.0000063 per 128MB-second - that's about 6 millionths of a cent, which sounds like nothing until you do the math. A function that runs for 2 seconds with 512MB allocation burns about $0.00005 per call just for memory, even if your function spends most of its time waiting for the database to respond. This billing model will spike your costs fast on I/O-heavy workloads because you're paying for memory you're not using.
Cloudflare's CPU Time Gotcha
Cloudflare bills for CPU milliseconds, not wall-clock time. This sounds great until you hit CPU-intensive tasks. Image resizing, JSON parsing of large payloads, or crypto operations eat through your CPU allowance fast.
The current Cloudflare Workers pricing includes 30 million CPU milliseconds per month on paid plans. That sounds like a lot until you realize a single complex JSON transformation might consume 50ms of CPU time. Most Workers consume 1-2 milliseconds, but heavy processing can hit limits quickly.
We hit this limit in production when we moved our JWT verification to Workers. Each token verification was burning 15-20ms of CPU time because of the crypto operations. What looked like "unlimited" requests became 1.5-2 million JWT verifications before we maxed out CPU time. Took me 5 hours of debugging to figure out why requests were getting CPU_TIME_LIMIT_EXCEEDED errors.
Real Pricing Breakdown Examples
Small API (1M requests/month):
- Cloudflare Workers: Basically free (included in $5 plan)
- AWS Lambda@Edge: Maybe $50-80 if you're lucky and don't fuck up memory allocation
- Vercel: $20 per developer plus whatever usage they decide to charge
Medium Traffic (10M requests/month):
- Cloudflare Workers: Still around $5 (seriously, this shocked me)
- AWS Lambda@Edge: Somewhere between $500-800 plus memory costs that will surprise you
- Vercel: Team costs that are already painful + maybe $5-10 in overages
High Traffic (100M requests/month):
- Cloudflare Workers: $30-40 total ($5 base + overages)
- AWS Lambda@Edge: $5,000-8,000+ plus duration costs that make you cry
- Vercel: Whatever you're paying for team seats + $50-80 in request fees
These numbers assume your functions don't break, which they will. Add actual database calls, external API timeouts, or any real processing and costs explode differently on each platform. Real cost analysis shows massive variations depending on whether your workload actually matches their pricing assumptions.
Platform Lock-in Costs
Vercel Edge Functions only work on Vercel's platform. Migrate away and you rewrite your edge logic. AWS Lambda@Edge ties you to CloudFront. Cloudflare Workers is the most portable since it runs standard JavaScript, but still uses their APIs for KV storage and other services.
Factor migration costs into your calculations because you will eventually need to move. It's not just current pricing - it's what happens when your startup scales or you realize the platform doesn't actually work for your use case. Platform lock-in costs will burn significant budget when you're forced to rewrite everything during a migration.