Building a ai wrapper / llm app with FastAPI? Let us review it.
Expert code review for ai wrapper / llm apps built with FastAPI. We fix FastAPI-specific security gaps, optimize performance, and handle deployment. From $19.
Common FastAPI issues we find
Real problems from FastAPI codebases we've reviewed.
Blocking calls inside async endpoints
Synchronous database calls, file operations, or HTTP requests inside async def endpoints block the entire event loop, making your API unresponsive under load.
Missing authentication on endpoints
API routes that handle sensitive data or actions without any auth middleware — Depends() for authentication is simply not included in the route definition.
No CORS configuration
CORSMiddleware either missing (blocking all cross-origin requests) or set to allow_origins=['*'] (allowing any website to call your API).
Pydantic models without validation constraints
Request models that accept any string length, any number range, and any format. No Field() constraints, so invalid data flows through your system.
AI Wrapper / LLM App challenges to solve
Key ai wrapper / llm app concerns that AI-generated code often misses.
API cost management
LLM API calls are expensive. A single unoptimized prompt can cost cents per request — which adds up fast with real users. You need token counting, cost tracking, usage limits per user, and prompt optimization to stay profitable.
Response time variability
LLM responses take 1-30 seconds depending on prompt complexity, model load, and output length. Your UI needs streaming responses, loading states, and timeout handling. Users abandon apps that feel slow.
Error handling for AI responses
The AI model might: return an error, time out, return malformed output, refuse to answer, or hallucinate. Each case needs specific handling. AI tools build the happy path but not the many failure modes.
Prompt injection and security
Users can manipulate your AI's behavior through carefully crafted inputs — making it ignore instructions, reveal system prompts, or produce harmful output. Input sanitization and output validation are essential.
What we check
Key areas we review for FastAPI ai wrapper / llm app projects.
API key security — LLM API keys stored server-side, never exposed to client
Cost controls — per-user limits, token counting, usage monitoring
Streaming implementation — proper SSE/streaming for LLM responses
Error handling — timeouts, rate limits, model errors, malformed output
Not sure if your app passes? Our code audit ($19) checks all of these and more.
Start with a self-serve audit
Get a professional review of your FastAPI ai wrapper / llm app project 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.
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 review a ai wrapper / llm app built with FastAPI?
Yes. We regularly audit FastAPI ai wrapper / llm app projects and understand the specific patterns and pitfalls of this combination. Our review covers security, performance, and deployment readiness.
What issues do you find in FastAPI ai wrapper / llm apps?
Common issues include blocking calls inside async endpoints and missing authentication on endpoints on the FastAPI side, combined with ai wrapper / llm app-specific concerns like api cost management and response time variability. We check for all of these and more.
How do I make my FastAPI ai wrapper / llm app production-ready?
Start with our code audit ($19) to get a prioritized list of issues. For FastAPI ai wrapper / llm app projects, the typical path is: fix security gaps, address ai wrapper / llm app-specific requirements, optimize FastAPI performance, then configure deployment. We provide a fixed quote after the audit.
How long does it take to audit a FastAPI ai wrapper / llm app?
Our code audit delivers a full report within 24 hours. For FastAPI ai wrapper / llm app projects, we check security, architecture, performance, and deployment readiness across all FastAPI-specific patterns. Fixes are scoped separately with a fixed quote.
Related resources
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