LangChain vs Building From Scratch.When to Use What
I've shipped production systems with both LangChain and custom code. Here's when each approach wins.and when it'll burn you.
The LangChain debate is one of the most polarizing in the AI builder community. Some swear by it. Others call it bloated abstraction hell. After shipping production systems with both approaches, here's my honest take.
When LangChain wins
LangChain is unbeatable for prototyping. When you need to test an idea in an afternoon.chain a few prompts, add a retriever, plug in a tool.nothing gets you there faster. The abstractions that feel heavy in production are liberating in exploration mode.
It's also great when your team is new to AI engineering. The framework encodes best practices and common patterns. It's like Rails for AI.opinionated, sometimes frustrating, but it keeps you on the rails.
When to build from scratch
The moment you need fine-grained control over token usage, custom retry logic, non-standard agent architectures, or performance optimization beyond what the framework allows.build your own. At ClideOps, we started with LangChain and rewrote everything within three months.
The abstraction layers that save time early become walls later. When you're debugging a production issue at 2am and the error is three abstraction layers deep inside a chain you didn't write, you'll wish you had built it yourself.
Use frameworks to learn. Build from scratch to ship.
My recommendation
Start with LangChain for your first prototype. Learn the patterns. Understand what the abstractions are doing. Then, when you're ready to go to production, rewrite the critical path in plain Python with direct API calls. Keep LangChain for the non-critical tooling. Best of both worlds.
Written by
Dayssam Bakaar