Built a api / backend service with GPT Engineer?
We'll make it production-ready.
APIs are the backbone of modern applications — mobile apps, SPAs, integrations, and other services all depend on your API being secure, fast, and reliable. AI tools can scaffold API endpoints quickly, but production APIs need authentication, input validation, rate limiting, documentation, and monitoring that AI tools consistently skip.
API / Backend Service challenges in GPT Engineer apps
Building a api / backend service with GPT Engineer is a great start — but these challenges need attention before launch.
Authentication and API keys
Every endpoint needs to verify the caller's identity. AI tools create endpoints without auth, or with auth that's easy to bypass. You need token-based auth, API key management, and proper session handling.
Input validation
Every parameter, request body, and header value must be validated before use. AI-generated APIs trust client data, which leads to injection attacks, data corruption, and crashes from unexpected input.
Rate limiting and abuse prevention
Without rate limits, anyone can hammer your API — brute-forcing passwords, scraping data, or running up your infrastructure costs. Rate limiting must be per-user and per-endpoint.
Error handling and status codes
APIs should return appropriate HTTP status codes (400 for bad input, 401 for unauthorized, 404 for not found, 500 for server errors) with helpful error messages. AI tools often return 200 for everything or expose internal error details.
Documentation
APIs without documentation are unusable. Auto-generated OpenAPI/Swagger docs from your code are the minimum. AI tools rarely set up API documentation.
Versioning and backwards compatibility
Once other services depend on your API, you can't change it freely. You need a versioning strategy from the start so you can evolve the API without breaking existing clients.
What we check in your GPT Engineer api / backend service
Common GPT Engineer issues we fix
Beyond api / backend service-specific issues, these are GPT Engineer patterns we commonly fix.
No authentication system
GPT Engineer generates functional UIs but typically skips authentication entirely. All routes and data are publicly accessible.
Direct database access from client
Some generated apps query databases directly from the frontend without an API layer, exposing database credentials and structure.
Incomplete feature implementations
Features that look complete in the UI but don't actually work end-to-end. Buttons that don't submit, forms that don't save, and links that go nowhere.
Missing error boundaries
A single component error crashes the entire application. No error boundaries or fallback UIs to gracefully handle failures.
Start with a self-serve audit
Get a professional review of your GPT Engineer api / backend service at a fixed price.
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.
Frequently asked questions
Can I build a api / backend service with GPT Engineer?
GPT Engineer is a great starting point for a api / backend service. It handles the initial scaffolding well, but api / backend services have specific requirements — authentication and api keys and input validation — that need professional attention before launch.
What issues does GPT Engineer leave in api / backend services?
Common issues include: no authentication system, direct database access from client, incomplete feature implementations. For a api / backend service specifically, these issues are compounded by the need for authentication and api keys.
How do I make my GPT Engineer api / backend service production-ready?
Start with our code audit ($19) to get a clear picture of what needs fixing. For most GPT Engineer-built api / backend services, the critical path is: security review, then fixing core flow reliability, then deployment. We provide a fixed quote after the audit.
How much does it cost to fix a GPT Engineer-built api / backend service?
Our code audit is $19 and gives you a complete report of issues. Fixes start at $199 with our Fix & Ship plan. For larger api / backend service projects, we provide a custom fixed quote after the audit — no hourly billing.
Get your GPT Engineer api / backend service production-ready
Tell us about your project. We'll respond within 24 hours with a clear plan and fixed quote.