Node.js in the backend of an AI platform: what we learned with LetsAI
When we started building LetsAI we needed a backend that could handle async AI requests, binary data streaming and hundreds of simultaneous connections without collapsing. Node.js let us do it with a small team and a maintainable codebase.
Why Node.js and not Python for the backend
We asked ourselves immediately. We use Python for RAG Enterprise and everything that's pure ML — it makes sense there. But for LetsAI the problem was different: we needed a server that orchestrated calls to external AI providers, handled heavy file uploads and kept WebSocket connections open for result streaming. Node.js with its non-blocking event loop was the natural choice. In practice: a single Node process comfortably handles 500+ simultaneous connections while waiting for external API responses. With Python we would have had to set up asyncio, manage the GIL and add complexity we didn't want. Our frontend team already wrote TypeScript — having the same language across the whole stack cut onboarding time for new developers in half.
Managing streaming: from AI chunks to the browser
The most critical use case in LetsAI is streaming. When a user generates a video or image, they can't sit for 30 seconds staring at a white spinner. They need to see real-time progress. We implemented a pattern with Server-Sent Events (SSE) where Node.js receives chunks from the AI provider and forwards them immediately to the client. The backend acts as an intelligent proxy: adds metadata (completion percentage, estimated cost, remaining time), handles automatic retries if the provider drops the connection and logs everything for the analytics dashboard. The advantage of Node here is that readable streams are native to the language. We didn't have to import external libraries — Node's pipe() pattern does exactly what's needed. In production we handle about 2,000 streams per day with average latency under 50ms.
Job queue and heavy processes: the pattern we use
Node.js is lightning fast at I/O but not built for CPU-intensive calculations. We know that. For heavy tasks — video transcoding with FFmpeg, image resizing, thumbnail generation — we use BullMQ with Redis as broker. The flow is simple: the API receives the request, creates a job in the queue, returns an ID to the client. Separate workers pick up the job, execute it and update the status. The client receives updates via SSE. This pattern let us scale horizontally without touching the code. When load increases, we add workers. When it drops, we shut them down. Infrastructure cost follows actual traffic.
Security and rate limiting in a multi-user platform
A platform like LetsAI that handles credits and payments must be bulletproof. On Node.js we implemented: JWT with refresh tokens and automatic rotation, per-user rate limiting with Redis-based sliding window, input validation with Zod on every endpoint, sanitization of all prompts before sending to AI providers, restrictive CORS and helmet for HTTP headers. Rate limiting was crucial. Without it, a single user could generate hundreds of AI requests in seconds and burn the provider budget. Now every plan has clear limits: X requests per minute, Y per day, with real-time feedback.
Lessons learned and when NOT to use Node
After two years of LetsAI in production: Node is perfect for: external API orchestration, real-time streaming, I/O-bound applications, fast prototyping when the team is full-stack JavaScript. Node is NOT good for: heavy math (we use Python), ML model training, tasks requiring real multithreading. Biggest mistake? Initially we handled FFmpeg transcoding in the main Node process. Server froze every time. Moving to job queues solved it in a day. The right question isn't "Node or Python?" but "what does the backend need to do?". If orchestration and I/O, Node. If ML and computation, Python. We use both.
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