Bottom line: a cheap 2–4 GB VPS is plenty to run AI agents and automation — as long as the heavy inference happens via an API. Local model serving on budget hardware is a different, more limited story.
The split that matters:
• Agent orchestration (calling models, chaining steps, cron, memory) → 2 GB VPS is fine.
• Local LLM serving (running a model on the box) → needs 8–16 GB + GPU for anything serious.
What runs fine on a cheap VPS
- Agent frameworks that call cloud models via API — your code just orchestrates.
- Scheduled automation and AI agent workloads that don't need a GPU.
- Tooling: web scraping, RSS, email, n8n workflows — all light on CPU/RAM.
These are exactly the patterns in self-host automation on a $5 VPS.
When you need a bigger box
If you insist on running a local model with no cloud calls, you need real hardware. A budget VPS will serve a tiny quantized model slowly at best. For serious local inference, see how much RAM you actually need and budget for 16 GB+.
The cheap, practical setup
# On a 2-4 GB VPS, run your agent as a service
docker compose up -d # agent container + postgres + redis
# Point the agent at a cloud model API
# Let it run unattended, schedule with cronAffiliate disclosure: we earn a commission on DigitalOcean signups at no cost to you.