Built with JetBrains AI? Deploy it to Vercel.
Step-by-step deployment help for JetBrains AI-built apps on Vercel. We fix deployment issues, configure Vercel correctly, and get your app live in production. From $19.
JetBrains AI issues we fix before deploying
Problems specific to JetBrains AI's code generation that affect Vercel deployments.
Over-engineered enterprise patterns generated for simple startup use cases
JetBrains AI is trained on enterprise Java and Kotlin patterns, so it tends to generate verbose factory patterns, abstract base classes, and interface hierarchies for problems that could be solved with a simple function in a startup context.
Generated Spring Boot code includes unnecessary security exposure in default configurations
Spring Boot applications generated by JetBrains AI may include actuator endpoints, management ports, or H2 console access enabled in configurations that should be disabled or secured before production deployment.
Verbose boilerplate code increases bundle size and maintenance overhead
Java code generation in particular produces verbose getter/setter patterns, checked exception hierarchies, and XML configuration that modern Kotlin or Lombok-based approaches would handle with a fraction of the code.
Generated unit tests use JUnit 4 patterns in projects that have moved to JUnit 5
JetBrains AI sometimes generates JUnit 4 annotations (@Test from org.junit, @Before, @After) in projects configured for JUnit 5, causing compilation errors and requiring annotation migration.
Vercel deployment issues we check for
Common Vercel problems that break AI-generated apps in production.
Serverless function size exceeds 50MB limit
AI tools bundle heavy dependencies into API routes without tree-shaking, pushing serverless functions past Vercel's 50MB compressed size limit and failing the build.
Edge runtime incompatible with Node.js APIs
Generated code uses fs, crypto, or other Node.js built-ins inside Edge Runtime routes, causing deployment failures since Edge only supports a subset of Web APIs.
ISR revalidation not triggering correctly
Incremental Static Regeneration configured with stale revalidate values or missing on-demand revalidation hooks, serving outdated content indefinitely.
Environment variables missing in production
Secrets added to .env.local but not configured in the Vercel dashboard, causing runtime crashes only in production while working perfectly in local dev.
Start with a self-serve audit
Get a professional review of your JetBrains AI project before deploying to Vercel.
External Security Scan
Black-box review of your public-facing app. No code access needed.
- OWASP Top 10 vulnerability check
- SSL/TLS configuration analysis
- Security header assessment
- Expert review within 24h
Code Audit
In-depth review of your source code for security, quality, and best practices.
- Security vulnerability analysis
- Code quality review
- Dependency audit
- Architecture review
- Expert + AI code analysis
Complete Bundle
Both scans in one package with cross-referenced findings.
- Everything in both products
- Cross-referenced findings
- Unified action plan
100% credited toward any paid service. Start with an audit, then let us fix what we find.
How it works
Tell us about your app
Share your project details and what you need help with.
Expert + AI audit
A human expert assisted by AI reviews your code within 24 hours.
Launch with confidence
We fix what needs fixing and stick around to help.
Frequently asked questions
Can you deploy a JetBrains AI-built app to Vercel?
Yes. We regularly deploy JetBrains AI-generated projects to Vercel. We handle the platform-specific configuration, fix deployment errors, and ensure your app runs reliably in production on Vercel.
What Vercel issues do JetBrains AI projects typically have?
JetBrains AI projects commonly have over-engineered enterprise patterns generated for simple startup use cases and generated spring boot code includes unnecessary security exposure in default configurations. When deploying to Vercel, these combine with platform-specific issues like serverless function size exceeds 50mb limit and edge runtime incompatible with node.js apis.
How do I get my JetBrains AI project live on Vercel?
Start with our code audit ($19) to get a prioritized list of deployment blockers. For JetBrains AI-built projects targeting Vercel, the typical path is: fix JetBrains AI-specific code issues, configure Vercel settings correctly, then deploy. We provide a fixed quote after the audit.
Can you handle the full deployment of my JetBrains AI app to Vercel?
Yes. We handle end-to-end deployment: auditing your JetBrains AI codebase, fixing deployment blockers, configuring Vercel correctly, setting up environment variables, and getting your app live in production. Start with our code audit ($19).
Related resources
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Tell us about your project. We'll respond within 24 hours with a clear plan and fixed quote.