Fly.io Machines Tech Conference 2026

Fly.io Machines

Fly.io Machines Tech Conference Edge Computing Global Deployment Firecracker MicroVM Auto-scaling Anycast Scale to Zero Live Streaming WebSocket Real-time

Platform	Edge	Scale to Zero	Boot Time	เหมาะกับ
Fly.io	30+ Regions	ใช่	300ms	Full-stack Edge
Cloudflare Workers	300+ PoPs	ใช่	0ms	Edge Functions
AWS Lambda	25+ Regions	ใช่	100-500ms	Event-driven
Railway	1 Region	ใช่	1-5s	Simple Deploy
Vercel	Edge Functions	ใช่	0ms	Frontend + API

Fly.io Setup

=== Fly.io Deployment ===

Install CLI

curl -L https://fly.io/install.sh | sh

fly auth login

Create App

fly launch --name tech-conf-2026

fly deploy

Dockerfile

FROM node:20-alpine

WORKDIR /app

COPY package*.json ./

RUN npm ci --production

COPY . .

EXPOSE 3000

CMD ["node", "server.js"]

fly.toml — Configuration

app = "tech-conf-2026"

primary_region = "sin" # Singapore

[build]

dockerfile = "Dockerfile"

[http_service]

internal_port = 3000

force_https = true

auto_stop_machines = true

auto_start_machines = true

min_machines_running = 1

[[vm]]

cpu_kind = "shared"

cpus = 2

memory_mb = 512

[processes]

app = "node server.js"

worker = "node worker.js"

Multi-region Deployment

fly regions add sin nrt lax ams

fly scale count 2 --region sin

fly scale count 1 --region nrt

fly scale count 1 --region lax

fly scale count 1 --region ams

Fly Postgres — Multi-region Database

fly postgres create --name conf-db --region sin

fly postgres attach conf-db

fly postgres connect -a conf-db

from dataclasses import dataclass

@dataclass

class FlyApp:

name: str

regions: int

machines: int

cpu: str

memory_mb: int

status: str

requests_hr: int

apps = [

FlyApp("conf-web", 4, 6, "shared-2x", 512, "Running", 15000),

FlyApp("conf-api", 4, 8, "shared-2x", 1024, "Running", 25000),

FlyApp("conf-ws", 3, 4, "shared-2x", 512, "Running", 8000),

FlyApp("conf-stream", 2, 3, "performance-2x", 2048, "Running", 5000),

FlyApp("conf-db", 2, 3, "performance-4x", 4096, "Running", 0),

]

print("=== Fly.io Apps ===")

for a in apps:

print(f" [{a.status}] {a.name}")

print(f" Regions: {a.regions} | Machines: {a.machines}")

print(f" CPU: {a.cpu} | RAM: {a.memory_mb}MB | Req/hr: {a.requests_hr:,}")

Conference Platform

=== Tech Conference Architecture ===

Components:

1. Conference Website — Next.js on Fly.io

2. API Server — FastAPI/Express on Fly.io

3. WebSocket Server — Real-time Chat & Q&A

4. Streaming — HLS/DASH Video Delivery

5. Database — Fly Postgres Multi-region

6. Cache — Fly Redis (Upstash)

7. Storage — S3-compatible (Tigris on Fly)

Auto-scaling for Keynote

fly autoscale set min=2 max=20

# Or via Machines API:

curl -X POST "https://api.machines.dev/v1/apps/conf-api/machines" \

-H "Authorization: Bearer $FLY_API_TOKEN" \

-d '{

"config": {

"image": "registry.fly.io/conf-api:latest",

"guest": {"cpus": 2, "memory_mb": 1024},

"auto_destroy": true

"region": "sin"

WebSocket — Real-time Q&A

const WebSocket = require('ws');

const wss = new WebSocket.Server({ port: 8080 });

wss.on('connection', (ws) => {

ws.on('message', (data) => {

const msg = JSON.parse(data);

if (msg.type === 'question') {

Broadcast to all clients in same session

wss.clients.forEach(client => {

if (client.readyState === WebSocket.OPEN) {

client.send(JSON.stringify({

type: 'question',

user: msg.user,

text: msg.text,

timestamp: Date.now()

}));

}

});

}

});

@dataclass

class ConferenceMetric:

metric: str

value: str

target: str

status: str

metrics = [

ConferenceMetric("Registered Attendees", "5,200", "5,000", "Exceeded"),

ConferenceMetric("Live Viewers (Peak)", "3,800", "3,000", "Exceeded"),

ConferenceMetric("API Latency (p99)", "45ms", "< 100ms", "OK"),

ConferenceMetric("WebSocket Connections", "2,100", "< 5,000", "OK"),

ConferenceMetric("Stream Bitrate", "1080p 6Mbps", "720p+", "OK"),

ConferenceMetric("CDN Cache Hit", "95%", "> 90%", "OK"),

ConferenceMetric("Error Rate", "0.02%", "< 0.1%", "OK"),

ConferenceMetric("Monthly Cost", "$280", "< $500", "Under Budget"),

]

print("\nConference Dashboard:")

for m in metrics:

print(f" [{m.status}] {m.metric}: {m.value} (target: {m.target})")

Cost และ Optimization

# === Cost Optimization ===

# Fly.io Pricing (approximate)
# shared-cpu-1x, 256MB: $1.94/mo (running 24/7)
# shared-cpu-2x, 512MB: $3.88/mo
# performance-2x, 2GB: $31.00/mo
# Scale to Zero: pay only when running

# Conference Cost Breakdown
cost_items = {
    "Web App (4 regions, 6 machines)": "$23/mo",
    "API Server (4 regions, 8 machines)": "$62/mo",
    "WebSocket (3 regions, 4 machines)": "$16/mo",
    "Streaming (2 regions, 3 machines)": "$93/mo",
    "Postgres (2 regions, HA)": "$50/mo",
    "Bandwidth (500GB)": "$25/mo",
    "Persistent Volumes": "$15/mo",
    "Total": "$284/month",
    "During Conference (scaled up)": "~$50/day extra",
}

print("Cost Breakdown:")
for item, cost in cost_items.items():
    print(f"  {item}: {cost}")

# Scale to Zero Strategy
# Before conference: min_machines = 1
# During keynote: auto-scale to 20
# After conference: scale to zero
# Cost saving: ~70% vs always-on

optimization_tips = [
    "Scale to Zero: ใช้ auto_stop เมื่อไม่มี Traffic",
    "Shared CPU: ใช้ Shared CPU สำหรับ Web และ API",
    "Performance: ใช้ Performance CPU สำหรับ Streaming เท่านั้น",
    "Region: Deploy เฉพาะ Region ที่มีผู้เข้าร่วม",
    "CDN: ใช้ Fly CDN Cache Static Assets",
    "Pre-warm: Scale up ก่อน Keynote 15 นาที",
    "Monitor: ใช้ fly logs และ fly status ติดตาม Real-time",
]

print(f"\n\nOptimization Tips:")
for i, t in enumerate(optimization_tips, 1):
    print(f"  {i}. {t}")

เคล็ดลับ

Scale to Zero: auto_stop_machines ประหยัดเงินมาก
Multi-region: Deploy ใกล้ผู้ใช้ Latency ต่ำ
Postgres: ใช้ Fly Postgres Multi-region HA
Pre-warm: Scale up ก่อนงาน Event ป้องกัน Cold Start
CLI: fly deploy ง่ายสุด ใช้ได้ทุก Language

Fly.io คืออะไร

Platform Deploy ใกล้ผู้ใช้ Firecracker MicroVM 30+ Region Docker Auto-scaling Anycast Persistent Volume Free Tier CLI ง่าย