My Python RAG pipeline choked at 50 concurrent users. So I ripped out the orchestration layer and rebuilt it in Node.js. Unpopular opinion: Python is the king of training. But for serving? It’s too heavy. When you move from a Jupyter notebook to real-world WebSockets, things break. I didn't just need inference. I needed: • To handle 1,000+ concurrent embeddings. • Non-blocking streams. • Zero serialization headaches. Python’s GIL (Global Interpreter Lock) fought me every step. Node’s event loop ate the load for breakfast. The new stack: 1. Training: Python (obviously). 2. API/Orchestration: Node.js + TypeScript. 3. Vector DB: Pinecone. The result? 40% lower latency and no thread-blocking nightmares. Use the right tool for the layer, not just the language you learned first. What is the biggest bottleneck in your current stack? #VectorDatabase #RAG #Javascript
Anuraag khare
2mo
Seems more like a bad architecture, Python’s GIL is hardly the bottleneck for I/O heavy tasks.
Matthew Hook
2mo
100%. It's a very slow option for building an API. My personal experience also.
Python (and the GIL) is very unlikely to be your bottleneck. At IntelliProve we are live streaming video frames over WebSockets from thousands of users at a time at times to an API that is written in Python without a problem. And during a live stream we do a lot of heavy processing, ML and video processing at 30 fps. I am guessing you had your API setup or deployed wrong if it chocked on 50 users just doing RAG, especially if just switching to Node.JS fixed or improved your problem. The GIL is rarely a bottleneck for APIs as the network and IO overhead is usually much larger and that is why you use async (just like in Node.JS) to more efficiently process requests.