Skip to content

What Is AI Hosting? A Developer's Guide

You've built an AI agent. It calls GPT-4, uses LangChain, runs perfectly on your laptop. Now someone says "just host it."

Host it where? How? What does "AI hosting" even mean for an agent that needs to run on a schedule, keep secrets, and not cost a fortune?

Here's what AI hosting actually means, what your options are, and how to pick the right one.

AI hosting vs regular hosting

Regular hosting runs a web server. A request comes in, your server responds, done. The server is stateless, predictable, and mostly idle.

AI hosting is different. Your agent:

  • Calls LLM APIs — each invocation burns tokens and costs money
  • Runs for minutes, not milliseconds — an agent loop with tool calls can take 30 seconds to 15 minutes
  • Needs secrets at runtime — API keys injected securely, never on disk
  • Runs on a schedule — daily research agent, hourly monitoring bot, weekly report generator
  • Needs isolation — agent A must never touch agent B's data

A standard VPS or serverless function wasn't designed for any of this.

The three models of AI hosting

1. Serverless functions (Lambda, Cloud Functions)

You write your agent as a function. The platform runs it when triggered.

Pros: No servers to manage, pay-per-execution, scales to zero.

Cons: Hard timeout (Lambda = 15 minutes max), cold starts, no built-in secrets isolation per agent, no agent-aware observability. You're wiring together 5+ services (Lambda, EventBridge, Secrets Manager, CloudWatch, IAM).

Best for: Simple, short-running agents that don't need persistent state.

2. Container platforms (Fly.io, Railway, self-managed ECS)

You containerize your agent with Docker and deploy to a platform that runs containers.

Pros: Full control over the runtime, no artificial timeout, always-on support.

Cons: You write Dockerfiles. You manage IAM roles. You set up cron yourself. Observability is on you. It's 2 weeks of infrastructure per agent.

Best for: Teams that need full control and have dedicated DevOps.

3. Purpose-built AI agent platforms (HollowHost)

A platform designed specifically for deploying AI agents. You point it at your GitHub repository and it handles the rest.

Pros: No Dockerfiles, no IAM, no cron setup. Automatic per-agent isolation, built-in secrets management, run history dashboard, always-on daemons.

Cons: Less flexibility than raw containers. No GPU support (for model inference — LLM API calls work fine).

Best for: Teams that want to ship agents, not infrastructure.

How HollowHost handles AI hosting

HollowHost is purpose-built for the AI agent use case. Here's how it maps to what your agent actually needs:

Agent need How HollowHost solves it
Run my Python agent 1 command: hollowhost ai-jobs create --repo you/agent --lang python --pm uv
Inject API keys securely hollowhost ai-jobs env import <id> --file .env — encrypted at rest, never in logs
Run every 6 hours hollowhost ai-jobs update <id> --schedule "0 */6 * * *"
Don't let agents touch each other Per-agent dedicated IAM execution role, isolated secrets and logs
Show me what happened Dashboard with run history, duration, token usage, full logs
Keep an agent always online AI Daemons — persistent VMs with a web control dashboard

The platform handles containerization, IAM provisioning, secrets injection, scheduling, and observability. You write the agent; HollowHost runs it.

Comparison: hosting options for AI agents

Lambda Containers HollowHost
Deploy from GitHub ⚠️ DIY ✅ 1 command
Per-agent IAM isolation ⚠️ DIY ⚠️ DIY ✅ Automatic
Cron scheduling ✅ EventBridge ⚠️ DIY ✅ Built-in
Secrets management ⚠️ Separate service ⚠️ DIY ✅ Built-in
Agent run dashboard
Always-on daemons
Timeout 15 min max Unlimited 15 min (configurable)
Time to first run 1-2 days 1-2 weeks 5 minutes

Which model should you choose?

  • You're prototyping, need a quick function? → Lambda
  • You need full infrastructure control? → Containers (ECS, Fly.io)
  • You want to ship agents, not manage infrastructure? → HollowHost

The right choice depends on where you want to spend your time: on your agent's logic, or on the infrastructure that runs it.


Deploy your first AI agent in 5 minutes. Start here — free tier included.