Building an AI side project has never been easier. A weekend is enough to wire a language model API into a web front end, add a vector database for retrieval, and have something genuinely useful running locally. Getting it online is where many projects stall. The hosting decision feels minor until you are three weeks in, stitching together a static host, a separate database provider, a background worker somewhere else, and an authentication service on top.
This guide walks through how to choose hosting for an AI project based on what the project actually does, rather than which platform has the most attractive landing page.
Start with the architecture, not the platform
Most AI side projects fall into one of three shapes, and each one points towards a different kind of host.
1. Front end plus a hosted model API
The simplest and most common pattern: a React, Vue, Svelte or plain HTML front end that calls a hosted model such as OpenAI, Anthropic or Google Gemini. The only server-side logic you need is a thin layer that keeps your API key out of the browser and perhaps applies rate limits.
This shape does not need a traditional server at all. A static host with serverless functions covers it completely: the site is served from a CDN, and a small function proxies requests to the model provider.
2. Retrieval-augmented generation (RAG)
If your project answers questions about documents, it needs somewhere to store embeddings and search them. You can use a managed vector database, which keeps the architecture close to the first shape, or run PostgreSQL with the pgvector extension, which means you now need a persistent database somewhere.
3. Agents, bots and background jobs
Anything that runs continuously changes the picture entirely: a Discord or Telegram bot, a scheduled scraper that feeds your model fresh data, a queue worker processing long-running generation jobs, or an agent that polls for tasks. Serverless functions are designed to respond and exit, usually within strict time limits. Long-running processes need a platform that keeps a container alive.
The serverless trap for AI workloads
AI requests are slow compared with typical web requests. A model call that streams a long response can easily run for tens of seconds, and chains of calls in an agent loop can take minutes. Serverless platforms impose execution time limits, and while many now support streaming responses, a multi-step job can still hit a timeout part-way through.
Before committing to a platform, check three numbers in its documentation: the maximum function execution time on the plan you will actually use, whether streaming responses are supported, and how cold starts behave. A cold start of a few seconds on top of a slow model call makes an application feel broken even when it is working correctly.
Frontend-first platforms
Platforms built around the front end, with Netlify the best-known example, are the natural home for the first project shape. You push to Git and the site builds and deploys automatically, with instant rollbacks and a preview URL for every branch. That preview workflow is particularly useful for AI projects, where you often want to compare prompt changes side by side before merging them.
Netlify's free tier is a real, permanent tier rather than a trial, which makes it a sensible default for prototypes and portfolio projects. Serverless functions cover the API-proxy pattern well. What a frontend-first platform does not do is host a database or a process that runs indefinitely.
Full-stack application platforms
Platforms such as Railway take the opposite approach: connect a repository and the whole application is built and run as a long-lived service. Crucially for AI projects, you can provision PostgreSQL (with pgvector), Redis or MySQL alongside it in a few clicks. That removes the awkward step of connecting your host to a separate database provider, and makes queue workers, cron jobs and bots straightforward.
The trade-off is cost predictability. Usage-based pricing on a service that is always running behaves differently from static hosting that sits idle between requests. It is worth setting spending alerts from day one, particularly if your project calls paid model APIs in a loop.
A decision framework
One question settles most of these decisions: does your project need a database or a process that runs continuously?
- No: use a frontend-first host with serverless functions. For a typical side project, it may cost nothing.
- Yes: use a full-stack platform, and keep the database next to the application.
- Both a polished front end and a real back end: split them. Serve the front end from a CDN-backed static host and run the API, database and workers on a full-stack platform.
The split setup is legitimate and common. You keep branch previews and global CDN delivery for the interface, and a proper runtime for everything with state.
If you are weighing the two most common options directly, AI Supermarket's Netlify vs Railway breakdown compares pricing, strengths and the situations where each one becomes the wrong choice.
Practical tips before you deploy
Keep API keys server-side
Never call a paid model API directly from browser JavaScript. Anyone can open developer tools, copy the key and run up your bill. Always route requests through a function or back end that holds the key as an environment variable.
Add rate limiting early
A public AI demo is an attractive target for anyone looking for free model access. Basic per-IP rate limiting and a hard monthly spending cap with your model provider will prevent an unpleasant invoice.
Stream responses
Streaming tokens to the user as they are generated makes even a slow model feel responsive, and reduces the risk of hitting request timeouts on platforms that support streaming.
Log prompts and costs
Record the prompt, the model used, token counts and response time for every request, even in a side project. When costs rise or quality drops, those logs are the only way to find out why.
Plan for the database you will need later
Many projects start without persistence and add it the moment users want saved history. If that is likely, choosing a platform that can host a database next to your application now will save a migration later.
Conclusion
The best hosting platform for an AI side project is the one that matches its architecture. Static front ends calling hosted models belong on frontend-first platforms with generous free tiers. Anything with a database, a queue or a continuously running process belongs on a full-stack platform. Decide which shape you are building before you deploy, and you avoid the most common mistake: choosing a host for its free tier, then assembling three other services around it to make the project work.
