Can AI-generated apps go live directly?

Usually not directly. You still need to confirm runtime, dependencies, start command, env vars, listen port, and persistence needs.

Can AI deploy automatically?

Yes — but use bounded authorization and deploy protocols so users confirm actions and Agents' access to credentials and infrastructure is limited.

Use case · AI app deployment

After AI writes your app, how do you actually ship it?

After AI writes the code, apps still need runtime detection, dependency install, env vars, ports, builds, health checks, domains, HTTPS, and runtime monitoring.

Read full deploy guide Compare deploy platforms

The missing last mile

Four kinds of work remain after AI generates code

Running locally doesn't mean deploy-ready

Local dev servers may rely on fixed ports, implicit env vars, uncommitted files, or cache directories.

AI shouldn't get long-lived cloud credentials

Putting server SSH keys or platform tokens in chat expands leak and misoperation risk.

Build failures need to go back to code

Dependency, version, start command, and client env issues usually need AI to change source, not manual log copying.

Lifecycle continues after launch

Domains, HTTPS, logs, metrics, sleep/wake, and recovery determine whether the app is truly usable.

PocketBay approach

Let AI keep doing the contextual work it's best at

AI already knows project structure and recent code changes. PocketBay doesn't ask it to jump out of context to operate servers — it provides pairing auth, upload APIs, structured status, and fix hints.

Recommended workflow

AI reads project files and confirms static, Next.js, Node.js, Python, or Dockerfile project.
AI fetches the deploy manual and creates a one-time pairing session.
User opens the full auth link and confirms project name and deploy type.
AI packages and uploads after excluding dependency caches and sensitive local directories.
Platform generates build plan, limits resources, builds image, and runs HTTP health check.
On failure, AI reads logs and fix suggestions, modifies source after informing user, and retries.

Send directly to AI

请将当前项目部署到 PocketBay，部署手册：https://pocketbay.com/deploy

Good fit vs not yet

Better fit	Not yet
Personal tools, MVPs, landing pages, AI mini-apps, monolithic APIs, and lightweight full-stack projects	Multi-service architecture, managed databases, GPU, Workers, Cron, strict SLA, and global multi-region systems
AI already has code context and needs to keep handling deploy errors	Complex IaC, approval chains, and enterprise release governance

Start shipping

Let PocketBay handle the last mile

Send the deployment prompt to your AI coding agent, approve once, and let it package, upload, build, and return a live URL.

Open one-line deploy Read the deploy guide