Built a api / backend service with Pythagora?
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 Pythagora apps
Building a api / backend service with Pythagora 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 Pythagora api / backend service
Common Pythagora issues we fix
Beyond api / backend service-specific issues, these are Pythagora patterns we commonly fix.
Weak input validation
Pythagora validates inputs at the UI level but often skips server-side validation, allowing malicious requests to bypass frontend checks.
Insecure session management
Sessions and tokens are sometimes stored insecurely or don't expire properly, allowing unauthorized access to persist.
Step-by-step accumulation of bugs
Each development step is reviewed individually, but the interaction between steps can introduce bugs that aren't visible in isolation.
Redundant code from iterations
Pythagora's iterative approach sometimes leaves behind old code paths, unused functions, and deprecated approaches from earlier steps.
Start with a self-serve audit
Get a professional review of your Pythagora 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 Pythagora?
Pythagora 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 Pythagora leave in api / backend services?
Common issues include: weak input validation, insecure session management, step-by-step accumulation of bugs. For a api / backend service specifically, these issues are compounded by the need for authentication and api keys.
How do I make my Pythagora api / backend service production-ready?
Start with our code audit ($19) to get a clear picture of what needs fixing. For most Pythagora-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 Pythagora-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 Pythagora api / backend service production-ready
Tell us about your project. We'll respond within 24 hours with a clear plan and fixed quote.