Never Use Langchain in Production - Agentic AI Best Practices скачать в хорошем качестве

Never Use Langchain in Production - Agentic AI Best Practices

Why You Should Avoid LangChain for Production (And What to Use Instead) In this video, I break down why LangChain — while great for prototypes — often becomes a bottleneck in production. If you’re a founder, CTO, or AI engineer scaling SaaS or enterprise AI products, you’ll want to avoid these pitfalls before they cost you time, money, and customers. You’ll learn: 1. Why LangChain struggles beyond the MVP stage 2. Real-world performance bottlenecks and cost issues 3. Debugging and scaling challenges in production 4. Safer, faster alternatives to LangChain 5. A phased plan to replace LangChain without halting development Timestamps: 0:00 Introduction – Why this topic matters 2:14 Built for prototyping, not production 3:13 Performance bottlenecks that kill UX 4:09 Debugging nightmares 5:02 Concurrency and scaling limitations 5:48 Heavy dependency and architectural lock-in 6:35 Memory bloat, costs, and latency 7:22 Fragile integrations that break easily 8:00 Leaner alternatives for orchestration 8:46 How to replace LangChain in phases 9:50 Closing – Building smart from the start Links & Resources: LinkedIn Newsletter Smart SaaSy Tuesday:   / smart-saasy-tues-de-6718436229253263360   Website: https://www.swarnendu.de AllRide Apps: https://allrideapps.com Innofied Solutions: https://www.innofied.com Hashtags: #LangChain #AIProductDevelopment #SaaSArchitecture #AIEngineering #LLMOps #AIProductStrategy #SaaSDevelopment