Flyio performance tuning

Skill jeremylongshore/claude-code-plugins-plus-skills/plugins/saas-packs/flyio-pack/skills/flyio-performance-tuning

425 plugins, 2,810 skills, 200 agents for Claude Code. Open-source marketplace at tonsofskills.com with the ccpi CLI package manager.

Install

npx -y skills add jeremylongshore/claude-code-plugins-plus-skills --skill flyio-performance-tuning

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

What its author says it does

Copied from the file, not written here

'Optimize Fly.io application performance with auto-stop/start tuning,

The file declares its own license as MIT. That is the author’s claim about this one file, and it is not the same thing as the license GitHub reports for the repository, which is listed with the other numbers below.

SKILL.md

3.0 KB, as published. Nobody here has run it

Fly.io Performance Tuning

Overview

Optimize Fly.io performance: eliminate cold starts, right-size VMs, leverage multi-region for low latency, and tune concurrency settings.

Instructions

Step 1: Eliminate Cold Starts

# fly.toml — suspend instead of stop for faster resume (~100ms vs ~5s)
[http_service]
  auto_stop_machines = "suspend"   # Suspend to RAM, not full stop
  auto_start_machines = true
  min_machines_running = 1          # Always-warm in primary region

# For latency-critical: keep machines running in all regions
# min_machines_running applies globally

Step 2: Right-Size VMs

# Check current allocation
fly scale show -a my-app

# Start small, scale up based on metrics
fly scale vm shared-cpu-1x --memory 256    # Start here
fly scale vm shared-cpu-1x --memory 512    # If memory-constrained
fly scale vm shared-cpu-2x --memory 1024   # If CPU-bound
fly scale vm performance-2x --memory 4096  # For compute-heavy workloads

Workload	VM	Memory	When
Static site / API proxy	shared-cpu-1x	256mb	Low traffic
Node.js API	shared-cpu-1x	512mb	Most apps
Heavy processing	shared-cpu-2x	1gb	Background jobs
Database / ML	performance-2x	4gb	Compute-intensive

Step 3: Multi-Region Latency Optimization

# Deploy close to your users
fly scale count 1 --region iad    # US East
fly scale count 1 --region lhr    # Europe
fly scale count 1 --region nrt    # Asia Pacific

# Fly automatically routes to nearest region via Anycast
# Verify: curl with timing
curl -w "DNS: %{time_namelookup}s, Connect: %{time_connect}s, Total: %{time_total}s\n" \
  -o /dev/null -s https://my-app.fly.dev/health

Step 4: Connection Pooling for Postgres

// Use connection pooling for Fly Postgres
// PgBouncer runs on port 5433 (pooled) vs 5432 (direct)
const pooledUrl = process.env.DATABASE_URL?.replace(':5432/', ':5433/');

// Prisma: add pgbouncer=true
// DATABASE_URL="postgres://user:[email protected]:5433/db?pgbouncer=true"

Step 5: Tune Concurrency

[http_service.concurrency]
  type = "requests"       # or "connections"
  hard_limit = 250        # Max before rejecting
  soft_limit = 200        # Start scaling at this point

Resources

Next Steps

For cost optimization, see flyio-cost-tuning.