Built with JetBrains AI? Deploy it to AWS.
Step-by-step deployment help for JetBrains AI-built apps on AWS. We fix deployment issues, configure AWS 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 AWS 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.
AWS deployment issues we check for
Common AWS problems that break AI-generated apps in production.
IAM policies with wildcard permissions
AI-generated infrastructure code grants Action: '*' and Resource: '*' permissions, violating least-privilege principles and creating critical security exposure.
Security groups open to 0.0.0.0/0
Database and internal service security groups allow inbound traffic from any IP address, exposing RDS instances and backend services to the public internet.
S3 bucket policy allows public read
AI tools configure S3 buckets with public-read ACLs for convenience, inadvertently exposing private user data and application secrets.
Lambda cold starts degrading API performance
Infrequently called Lambda functions experience 3-10 second cold starts. AI-generated architectures don't configure provisioned concurrency or optimize bundle size.
Start with a self-serve audit
Get a professional review of your JetBrains AI project before deploying to AWS.
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 AWS?
Yes. We regularly deploy JetBrains AI-generated projects to AWS. We handle the platform-specific configuration, fix deployment errors, and ensure your app runs reliably in production on AWS.
What AWS 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 AWS, these combine with platform-specific issues like iam policies with wildcard permissions and security groups open to 0.0.0.0/0.
How do I get my JetBrains AI project live on AWS?
Start with our code audit ($19) to get a prioritized list of deployment blockers. For JetBrains AI-built projects targeting AWS, the typical path is: fix JetBrains AI-specific code issues, configure AWS settings correctly, then deploy. We provide a fixed quote after the audit.
Can you handle the full deployment of my JetBrains AI app to AWS?
Yes. We handle end-to-end deployment: auditing your JetBrains AI codebase, fixing deployment blockers, configuring AWS correctly, setting up environment variables, and getting your app live in production. Start with our code audit ($19).
Related resources
Deploy JetBrains AI Apps
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