By the end of this guide you will have an AI app running at a public HTTPS address, with its API keys kept out of the code, spending limits in place, and a way to roll back if a release goes wrong. To deploy an AI app, you pick a hosting platform that matches what the app actually runs: a web frontend that calls a model API, a container with its own backend, or a model that needs a GPU.
Most of the effort is in choosing the right shape of hosting and then handling the unglamorous parts: secrets, timeouts, cost caps and monitoring. This guide walks through those steps and compares seven hosting options from our Developer platforms category.
Key takeaways
- Decide first whether your app only calls a hosted model API, or whether you must host the model yourself. The first needs ordinary web hosting; the second needs GPU hosting.
- Store API keys as environment variables on the platform, never in the repository, and set a spend limit or alert before you share the URL.
- Free tiers exist on Vercel, DigitalOcean's App Platform, AppDeploy and Hugging Face, but each has caps that a real launch can hit.
- Test the deployed app, not just the local one: timeouts, cold starts and missing environment variables are the usual causes of a broken first release.
What you need before you start
- A working app. It runs locally and you know its start command, its dependencies and which model API or model file it uses.
- A Git repository. Most platforms deploy from a repository, and a push to it is the usual way to release.
- Provider keys. For example, the key for the model API your app calls.
- A payment method. Needed for paid tiers and for any GPU hosting, which is billed by usage.
- Time. A simple app can be live in under an hour. Allow longer if you host your own model.
- Difficulty. Easy for a frontend or an AI-built prototype, moderate for containers, harder for GPU serving.
Which kind of AI tool fits this job
Choose by what your app has to run.
Frontend and serverless hosting for apps that call a model API. Vercel deploys web and AI applications with a preview deployment for every Git push, serverless Vercel Functions, automatic HTTPS and a web application firewall. Its documentation also covers an AI Gateway and a Sandbox that runs agents in isolated microVMs. This fits most chat and assistant apps where the model lives elsewhere.
General cloud hosting when you need a backend, database or containers. DigitalOcean offers Droplet virtual machines, an App Platform that runs apps without infrastructure work, managed databases, GPU Droplets and a serverless Inference API. It suits an app with a long-running backend or a database next to it.
Hosting for apps built inside an AI assistant. AppDeploy connects to ChatGPT, Claude, Cursor and other agents that support MCP, then returns a live HTTPS link, with a backend, database, file storage and authentication included. It is aimed at prototypes and small internal tools rather than custom infrastructure.
Model hosting and GPU compute when you serve the model yourself. Runpod rents GPU Pods and runs Serverless endpoints whose flex workers scale to zero. Mystic AI deploys models on serverless GPUs or inside your own cloud account. Hugging Face hosts demo Spaces and Inference Endpoints on dedicated hardware. If you would rather not host the model at all, Fireworks AI serves open models through an API that is compatible with OpenAI and Anthropic client formats.
Step 1: Decide what you are actually hosting
You finish this step knowing whether your app needs a web host, a backend host, a GPU, or a mix.
- List the parts: frontend, backend routes, database, vector store, model.
- Mark the model as hosted (you call an API) or self-hosted (you run weights).
- Note anything long-running, such as background jobs, streaming responses or batch work.
- Pick one platform per part, and prefer fewer platforms when you can.
Check it worked by writing one sentence: "Frontend on X, model on Y, database on Z."
Step 2: Prepare the app for production
Production differs from your laptop, so remove the local assumptions first.
- Move every key and setting into environment variables, and add a sample file that lists their names without values.
- Pin dependency versions and commit the lockfile.
- Add a health route that returns a simple OK, so the platform and your monitor can check the app.
- Set a timeout and a retry policy for every model call, and stream responses where you can so users are not left waiting.
- Cap the length of user input and the length of model output.
Check it worked by running the app from a clean checkout with only the environment variables set.
Step 3: Pick the platform and connect the repository
Choose the host that matches Step 1, then link your repository so releases happen on push.
- For a web app that calls a model API, a frontend platform such as Vercel creates a preview deployment for every Git push, which gives you a safe place to test.
- For a backend with a database, use a general cloud such as DigitalOcean, where App Platform runs apps and managed databases sit alongside.
- For a prototype built in a chat assistant, follow AppDeploy's one-time connector setup, then ask the assistant to deploy.
- For your own model, compare GPU hosts on billing: Runpod bills per second, and Hugging Face upgraded hardware is billed hourly.
- Check the free tier against your expected traffic using the table below.
Check it worked by seeing the repository connected and a first build start.
Step 4: Add secrets, limits and a budget
Keys and costs are where AI apps most often go wrong, so set them before launch.
- Add the environment variables in the platform's settings, separately for preview and production.
- Use a separate model-provider key for production, so you can revoke it without touching your development setup.
- Set a spend cap or alert with your model provider and your host. Runpod has a default spend limit and low-balance alerts, and Fireworks AI offers spend limits with prepaid credits.
- Add rate limiting or a firewall rule in front of any route that triggers a paid model call. Vercel lists a web application firewall with custom rules.
Check it worked by confirming the app fails clearly, rather than silently, when a variable is missing.
Step 5: Deploy and test the live URL
Release to a preview or staging address first, and test it the way a user would.
- Trigger the deployment from your repository, or from your assistant if you use AppDeploy.
- Open the live URL and run your normal prompts, including a long one and a slow one.
- Check the logs for errors and timeouts. Serverless functions have execution limits, so a long model call may need streaming or a background job.
- If you self-host a model, test a request after the endpoint has been idle. Runpod flex workers and Mystic AI's bring-your-own-cloud deployments can scale to zero, so the first request may be slower.
Check it worked by completing a full user journey on the live address, from a different network.
Step 6: Point your domain and promote to production
Once the preview behaves, make it the public version.
- Add your custom domain. On AppDeploy, custom domains are part of the Plus plan.
- Confirm HTTPS works on the final address.
- Promote the tested build to production.
- Keep the previous version available. AppDeploy versions each deploy with one-step rollback. On other platforms, tag your releases in Git so you can redeploy a known-good commit.
Check it worked by loading the production domain and finding no mixed-content or missing-key errors.
Step 7: Monitor cost, errors and quality
Launch is the start of the work. Keep an eye on three things.
- Errors and latency. Watch the logs and add an uptime check on the health route.
- Spend. Review the model provider's usage and the host's bill each week for the first month. Usage-based plans grow with traffic.
- Answer quality. Save a small set of test prompts and rerun them after each release.
Check it worked by receiving a test alert when you deliberately break the health route on staging.
Tools for this job compared
| Tool | Good fit for | Pricing | Stated limits |
|---|---|---|---|
| Vercel | Web and AI frontends with Git-based previews; web and CLI | Freemium, paid from $20/mo (checked 24 Oct 2026) | Hobby includes fixed monthly amounts, such as 100 GB of Fast Data Transfer and 4 hours of Active CPU; usage beyond included amounts on Pro is billed per unit |
| DigitalOcean | Droplets, App Platform, databases and GPU Droplets | Freemium, paid from $4/mo (checked 24 Oct 2026) | App Platform free tier covers only three static-site apps; GPU Droplet starting rate needs a multi-month commitment |
| AppDeploy | Publishing apps built in ChatGPT, Claude or Cursor | Freemium, paid from $20/mo (checked 24 Oct 2026) | Free plan shows an AppDeploy watermark and is limited to up to 5 users per app; credits are shared across apps |
| Hugging Face | Demo Spaces and dedicated Inference Endpoints | Freemium, paid from $20/mo (checked 24 Oct 2026) | Upgraded Space hardware and dedicated endpoints are billed hourly on top of any plan; several security features need Enterprise Plus |
| Runpod | GPU Pods and serverless endpoints, billed per second | Paid | At a $0 balance Pods stop, and those without a network volume are terminated with data lost; credits are non-refundable |
| Fireworks AI | Calling open models by API instead of hosting them | Paid, from $0.01, usage-based (checked 24 Oct 2026) | Prepaid credits are deducted as you use them; some features such as the Serverless Training API are in private preview |
| Mystic AI | Model deployment on serverless GPUs or your own cloud | Paid | Only output streaming is supported; some GPUs, such as the H100, are listed as coming soon |
Prices and plan limits change, so confirm the current terms on each vendor's site before you commit.
Common mistakes to avoid
- Committing keys to the repository. It happens when a quick test works and the key is left in the code. Fix it by using environment variables on the platform and rotating any key that was ever committed.
- Launching without a spend cap. A public route that calls a paid model can be abused or can simply go viral. Fix it by setting provider limits, rate limits and alerts before you share the URL.
- Hosting a model you could call by API. GPUs are billed by the second or hour whether or not anyone uses the app, unless the endpoint scales to zero. Fix it by starting with a hosted model API such as Fireworks AI and moving to your own GPU only when you need to.
- Ignoring timeouts. Model calls are slow, and serverless platforms have execution limits. Fix it by streaming output, shortening prompts or moving long jobs to the background.
- Relying on a free tier for launch day. Free plans have data-transfer, user or storage caps. Fix it by checking the stated limits in the table and upgrading before traffic arrives.
How we put this together
This is an evidence-based guide built from each tool's listing in our directory, which is compiled from the vendor's own site and pricing page. It is not a review based on using the tools. We don't rank tools, and nobody paid to be included. Some outbound links may be affiliate links; see our disclosure.
Frequently asked questions
Can I deploy an AI app for free?
Yes, for small projects. Vercel has a free Hobby plan, DigitalOcean's App Platform has a free tier for static sites, AppDeploy has a free plan with a watermark, and Hugging Face has a free account. All have caps, so a busy app will outgrow them. Model API usage is billed separately.
Do I need a GPU to deploy an AI app?
Not if your app calls a hosted model through an API. You need a GPU only when you run model weights yourself. In that case Runpod, Mystic AI, Hugging Face Inference Endpoints and DigitalOcean's GPU Droplets are the options in this guide.
How long does it take to deploy an AI app?
A simple app connected to a Git repository is the quickest case, since platforms such as Vercel deploy on every push. Hosting your own model, adding a database or setting up a custom domain takes longer. Plan extra time for testing the live address.
How do I keep my API keys safe when I deploy?
Store them as environment variables in the host's settings, never in your code or repository. Use a separate production key, set a spend limit with the provider, and rotate any key that has been exposed.
Can I deploy an app that ChatGPT or Claude built for me?
Yes. AppDeploy connects to ChatGPT, Claude, Cursor and other MCP-capable agents and returns a live link, with a backend and database included. Review the generated code before you put real user data into it.
Compare more options in our Developer platforms category.
