Skip to content

PaaS Free Tier

Alternative VPS — deploy via git push, ga perlu manage server.

PaaS (Platform-as-a-Service) cocok kalo lo ga mau urus systemd, nginx, dll. Kasih repo, mereka handle deployment.

Pros vs Cons PaaS

Pros : - Deploy via git push - Auto-scaling - Built-in monitoring & logs - HTTPS otomatis - No server management

Cons : - Resource lebih kebatas vs VPS - Vendor lock-in (perlu deploy ulang kalo pindah) - Cold starts (kalo idle) - Background workers biasanya bayar

Provider yang masih punya free tier

Railway

  • $5 credit/bulan (cukup buat ~hobby bot trigger-based)
  • 1 GB RAM, 1 GB disk
  • Deploy via GitHub repo

Buat AI agent dengan tool execution : - Bisa, tapi ga continuous (credit habis dalam ~15 jam runtime/bulan) - Cocok buat bot scheduled (cron-based)

Render

  • 1 web service free
  • 512 MB RAM
  • Spin down setelah 15 menit idle = ga cocok buat bot

Buat AI agent : ❌ jangan, karena spin down.

Fly.io

  • 3 VM gratis (256 MB each)
  • Persistent volume 3 GB
  • 160 GB bandwidth

Buat AI agent : - Bisa kalo agent ringan - Pakai Dockerfile, deploy via flyctl deploy

Koyeb

  • 1 nano instance gratis
  • 256 MB RAM
  • Tidak spin down

Buat AI agent : - Cocok buat bot kecil yang continuous - Pakai Docker / Buildpack

Replit

  • Free tier dengan "Always-On" cuma bayar
  • Tanpa Always-On: spin down

Buat AI agent : - Free tier ga cocok (spin down) - Replit Hosting paid $7/bulan worth it kalo lo udah comfortable di Replit IDE

Vercel

  • Hobby tier free (untuk frontend)
  • Serverless functions (1M invocations/month free)
  • Bot continuous : ❌ serverless, max execution 10 detik per request

Buat AI agent : - Cuma cocok buat webhook handler yang fast - Ga cocok buat agentic loop yang multi-step

Cloudflare Workers

  • 100K req/day free
  • 10ms CPU per req
  • KV storage 100K read/day

Buat AI agent : - Cuma cocok buat webhook router super ringan - Ga cocok buat heavy logic

Deploy pattern PaaS

Pattern 1: Dockerfile

Dockerfile:

FROM python:3.12-slim
WORKDIR /app
COPY requirements.txt .
RUN pip install -r requirements.txt
COPY . .
CMD ["python", "main.py"]

requirements.txt:

python-telegram-bot==21.0
openai
python-dotenv

Deploy ke Fly.io:

flyctl auth login
flyctl launch  # interactive setup
flyctl deploy

Pattern 2: Procfile (Railway/Koyeb)

Procfile:

worker: python main.py

Auto-detected oleh platform.

Pattern 3: GitHub-based deploy

  1. Push repo ke GitHub
  2. Sign up Render/Railway
  3. Connect GitHub
  4. Pilih repo
  5. Auto-deploy on push

Environment variable di PaaS

PaaS kasih dashboard buat set env var:

TELEGRAM_BOT_TOKEN=...
OPENAI_API_KEY=...

JANGAN commit .env ke repo. Tambah di .gitignore:

.env
credentials/
data/

Storage di PaaS

PaaS biasanya ephemeral filesystem — file yang lo bikin saat runtime ga persist setelah restart.

Solusi:

Option A: Persistent volume (kalo tersedia)

Fly.io: flyctl volumes create data --size 1

# fly.toml
[mounts]
  source = "data"
  destination = "/data"

Option B: Eksternal database

Pake managed DB:

  • Supabase (Postgres, 500MB free)
  • Neon (Postgres, 0.5GB free)
  • PlanetScale (MySQL, 5GB free)
  • MongoDB Atlas (512MB free)
  • Firebase Firestore (1GB free)

Adapt agent storage:

# Memory di Postgres
import psycopg2
conn = psycopg2.connect(os.environ["DATABASE_URL"])
cur = conn.cursor()
cur.execute("CREATE TABLE IF NOT EXISTS memory (id SERIAL, note TEXT, ts TIMESTAMP DEFAULT NOW())")
cur.execute("INSERT INTO memory (note) VALUES (%s)", (note_text,))
conn.commit()

Option C: S3 / R2 untuk file

import boto3
s3 = boto3.client('s3', endpoint_url=os.environ['S3_ENDPOINT'])
s3.put_object(Bucket='agent-data', Key='memory.json', Body=json.dumps(memory))

Cloudflare R2 free 10GB storage + 10M read/month = sangat cukup.

Latency considerations

Source Target Latency
Indonesia Singapore (Oracle) 30-60ms
Indonesia AWS us-east-1 250ms
Indonesia GCP us-west1 200ms
Indonesia Fly.io sin (Singapore) 30-60ms
Indonesia Render Frankfurt 350ms

Telegram bot latency tolerant (user OK kalo respon 2-5 detik), tapi kalo lo bikin web UI, pilih region terdekat.

Cost projection

Skenario: AI agent jalan 24/7 setelah free tier habis.

Provider Setup Cost/bulan
Oracle Cloud A1.Flex 4 OCPU 24GB $0 forever (kalo ga di-suspend)
AWS t3.micro after free tier ~$10
GCP e2-micro after... still free $0 forever (kalo policy ga ganti)
Hetzner CX11 (paid) $4.30
Fly.io Free + paid volume $2-5
Railway Hobby plan $5
Render Standard (always-on) $7
Koyeb Standard $5
DigitalOcean $4/mo droplet $4-6

Trade-off PaaS vs VPS untuk agent

Aspek VPS (Oracle/AWS) PaaS (Fly.io/Railway)
Setup time 2-3 jam 15-30 menit
Resource Lebih besar Lebih kecil
Cost long-term Bisa $0 (Oracle) Hampir selalu bayar
Maintenance Manual (apt update, dll) Otomatis
Storage Persistent Ephemeral (kecuali volume)
systemd / nginx Setup manual Built-in / abstracted
SSH access Ya Biasanya tidak (cuma logs)
Multi-platform Bisa (banyak bot di 1 VPS) Sulit (1 deploy per app)

Saran :

  • Start dengan VPS gratis (Oracle/AWS) kalo lo comfortable dengan terminal
  • PaaS cocok kalo lo ga mau ribet, atau bot lo simple

Hybrid setup

Yang sering gua rekomen:

  1. Bot agent : VPS (Oracle/AWS) — long-running, full control
  2. Web dashboard agent : Vercel — frontend statis + serverless API
  3. Database : Supabase / Neon — managed Postgres, free tier
  4. Storage : Cloudflare R2 — backup, file attachment

Cost total: $0/bulan untuk small scale.

Migration path

Kalo lo udah pake free tier dan butuh upgrade:

Oracle (free) → Oracle paid VM → AWS / GCP production

Atau:

PaaS (free) → PaaS paid → VPS (kalo butuh control)

Plan migration dari awal — pakai env var, jangan hardcode IP, simpan state external. Migration jadi gampang.