Every AI agent team hits this decision: do we host it ourselves, or use a managed platform?
The self-hosting instinct is strong. "It's just a Python script on a server." Three weeks later, you're debugging IAM policies and your agent still isn't running.
Here are the real tradeoffs — not the marketing versions.
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.
The instinct is understandable. You've built a Python AI agent. It calls OpenAI, uses LangChain, runs on your laptop. The natural next step is "I'll just put it on a server."
Three weeks later, you're still configuring IAM policies and your agent still isn't running in production.
Here's why self-hosting AI agents is harder than it looks — and five managed alternatives that ship your agent in minutes, not months.
Modal is one of the best serverless Python platforms available. If you need to run GPU inference, batch process data, or deploy ML models, it's a strong choice.
But if you're deploying autonomous AI agents — the kind that call tools, run on a schedule, and need strict isolation — Modal's general-purpose model shows its limits. Here's what to consider, and the alternatives.
Vercel's AI SDK is excellent at what it does: streaming LLM responses to the browser. But if you're building autonomous AI agents — the kind that run on a schedule, call tools, and need isolation — the AI SDK isn't a deployment platform. It's a frontend library.
Here's what you actually need, and the alternatives that deliver it.
If you're a Python developer shipping AI agents, you've probably spent more time on infrastructure than on your agent's logic. Dockerfiles. IAM roles. Container registries. Cron schedulers. Secrets management.
None of that is Python. None of it is AI. And none of it differentiates your agent.
Here's how to deploy Python AI agents to production without touching any of it.
Every AI agent platform promises "one-click deployment." But what actually happens between your code and a running agent? Understanding the pipeline matters — because when something breaks, the abstraction leaks.
Here's the real pipeline, stage by stage.
You built an AI agent. It works on your machine. Now you need it running in production — on a schedule, with proper isolation, secrets management, and logs you can actually read.
The gap between "works on my laptop" and "running in production" is where most AI agent projects die. Here's how to close it in five minutes flat.
Hello, and welcome to the HollowHost blog! 
If you've ever built an AI agent that works beautifully on your laptop but stalls the moment you try to ship it to production, this one's for you. HollowHost is the platform that takes you from a GitHub repository to a running, isolated, serverless AI agent — in one click, with zero infrastructure to manage.
What actually happens between clicking Publish and your AI Job being ready to run? Here is the short version of the pipeline.