How to Deploy Your JetBrains AI-Built App
Step-by-step guide to deploying your JetBrains AI app to production. Covers Java deployment, environment variables, and production configuration.
What JetBrains AI generates
JetBrains AI typically generates projects using Java, Kotlin, Python, and TypeScript within IntelliJ IDEA, WebStorm, and PyCharm. JetBrains AI is deeply integrated with the IDE's static analysis and understands project structure, frameworks, and build systems. Understanding the stack is important because it determines your deployment options and the specific configuration you'll need.
Where to deploy
JetBrains AI assists within existing projects and doesn't prescribe deployment targets. Java/Kotlin Spring Boot apps typically deploy to Kubernetes, AWS ECS, or Railway. Python FastAPI apps go to similar platforms
Step-by-step deployment
JetBrains AI doesn't generate deployment infrastructure. For AI-generated code that introduces new services, configuration values, or dependencies, update your Helm charts, Docker Compose files, or infrastructure-as-code accordingly. Ensure generated Maven/Gradle dependencies are compatible with your existing dependency tree
Environment variables
One of the most common deployment failures is missing or misconfigured environment variables. JetBrains AI apps often hardcode values during development that need to be externalized for production. Audit every file for hardcoded URLs, API keys, and configuration values. Create a .env.example file listing every required variable. Set these in your hosting platform's dashboard before deploying.
Post-deployment checklist
After deploying, verify: HTTPS is enforced on all routes, custom domain is configured and resolving, environment variables are correctly loaded, all features work in production (not just locally), error tracking is capturing exceptions, and performance is acceptable under real conditions.
Common deployment mistakes
The most frequent issues we see in JetBrains AI deployments: forgetting to set environment variables (causing startup crashes), not testing the production build locally before deploying, missing build dependencies, and using development configurations in production.
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Related guides
Common Bugs in JetBrains AI-Generated Code
The most common bugs we find in JetBrains AI apps and how to fix them.
Security Issues in JetBrains AI Code
Critical security vulnerabilities commonly found in JetBrains AI-generated apps.
Optimizing JetBrains AI-Generated Code for Performance
How to make your JetBrains AI app faster.
Adding Tests to Your JetBrains AI Project
How to add a testing framework to your JetBrains AI app.
Related technologies
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