Developers building with LLMs, how are you actually handling memory, context persistence, and multi-model routing? Genuinely curious what everyone's doing [D](reddit.com)

Been building an AI product for a few months and honestly the part that's eaten most of my time has nothing to do with the actual product, it's all the plumbing around context management, memory persistence, and dealing with multiple LLM providers. Wanted to see how other developers are solving this because I feel like everyone's rebuilding the same infrastructure from scratch independently and there has to be a better way. A few specific things I'm struggling with and curious if you are too: On memory and context: How are you handling persistence across sessions? Rolling your own vector DB setup, using a managed service, something else entirely? How long did it actually take to build? What percentage of your codebase is context plumbing vs. actual product? Has anything broken badly in production around memory retrieval? What went wrong? On multi-model routing: Are you using more than one LLM or have you switched providers at some point? How painful was it to rewrite integrations when you switched or added a new model? Or are you fully locked into one provider and have no reason to change? On third party tooling: Have you tried any existing tools for this like Supermemory, Mem0, LangChain memory, anything else? What did you like, what was missing, what made you build your own instead? What would make you trust a third party enough to route production traffic through it? On cost: What are you spending monthly on memory/vector DB infrastructure? Is this a meaningful line item for you or essentially negligible? submitted by /u/No_Caregiver_2922 [link] [Kommentare]