Built with JetBrains AI? Deploy it to Microsoft Azure.
Step-by-step deployment help for JetBrains AI-built apps on Microsoft Azure. We fix deployment issues, configure Microsoft Azure 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 Microsoft Azure 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.
Microsoft Azure deployment issues we check for
Common Microsoft Azure problems that break AI-generated apps in production.
App Service plan on wrong pricing tier
AI tools provision Free or Basic tier App Service plans that lack autoscaling, custom domains, and SSL — causing issues when traffic grows beyond trivial levels.
CORS not configured on Azure Functions
Azure Functions require explicit CORS configuration in the portal or host.json. AI-generated frontends fail silently when API calls are blocked by missing CORS headers.
Managed Identity not used for service connections
AI-generated code embeds connection strings and secrets directly in environment variables instead of using Azure Managed Identity for secure, keyless authentication.
Deployment slots not configured for zero-downtime
Production deployments happen directly to the main slot, causing downtime during each deploy. Staging slots are available but never configured by AI tools.
Start with a self-serve audit
Get a professional review of your JetBrains AI project before deploying to Microsoft Azure.
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 Microsoft Azure?
Yes. We regularly deploy JetBrains AI-generated projects to Microsoft Azure. We handle the platform-specific configuration, fix deployment errors, and ensure your app runs reliably in production on Microsoft Azure.
What Microsoft Azure 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 Microsoft Azure, these combine with platform-specific issues like app service plan on wrong pricing tier and cors not configured on azure functions.
How do I get my JetBrains AI project live on Microsoft Azure?
Start with our code audit ($19) to get a prioritized list of deployment blockers. For JetBrains AI-built projects targeting Microsoft Azure, the typical path is: fix JetBrains AI-specific code issues, configure Microsoft Azure settings correctly, then deploy. We provide a fixed quote after the audit.
Can you handle the full deployment of my JetBrains AI app to Microsoft Azure?
Yes. We handle end-to-end deployment: auditing your JetBrains AI codebase, fixing deployment blockers, configuring Microsoft Azure correctly, setting up environment variables, and getting your app live in production. Start with our code audit ($19).
Related resources
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