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At what point do you outgrow n8n and need to build a local/physical RAG?

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Hey guys,

Quick question for those of you dealing with AI architecture and scaling.

Right now, we rely pretty heavily on n8n at our startup. It’s been amazing for moving fast, tying APIs together, and automating workflows. But looking ahead, I'm trying to figure out the long-term play.

At what exact point does it make sense to move away from n8n (and paying for third-party LLM APIs) to actually build out a specialized, local RAG on physical hardware?

A few things are pushing me in that direction:

  • Privacy: Handling sensitive user data or internal legal docs through external APIs is starting to feel like a liability.

  • Costs: Paying per token is fine for now, but I'm wondering when a dedicated server just becomes cheaper.

  • Control: Not having to worry about external rate limits or downtimes.

On the flip side, I know setting up bare metal and maintaining local models adds a ton of operational overhead. n8n is great specifically because we don't have to deal with that right now.

For those who have made the jump to a local setup: what was your breaking point? And what hardware/software stack did you go with to keep maintenance from becoming a nightmare?

Would love to hear your experiences!

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Here's what we learned building a local-first app:

..go hybrid, not all-in-local. Keep inference in the cloud (it's not worth the maintenance burden), but go local for:

  • Data that must never leave the device (embeddings, indexing, caching)

  • Real-time operations that can't tolerate network latency

  • Fallbacks when APIs are down

..privacy doesn't require local inference. Process data locally first, send only what you need to APIs, store identity/context on-device..that's the real win.

The actual breaking point is when ur per-user API costs exceed the operational overhead of self-hosting. and most of the time that's not where you think it is. n8n + APIs stays competitive longer than it looks.

its not necesarily "convenience" vs"control" its more "managed complexity" (APIs) vs "operational complexity" (self-hosted). running a hybrid model lets you pick which complexity matters for your specific constraints... and like..

stay with what lets you iterate fast. Move infrastructure only when it becomes the constraint, not before.

 Thank you so much for sharing this, Zack! Your perspective on 'managed complexity' versus 'operational complexity' is incredibly insightful. I completely agree with your point about n8n and APIs staying competitive longer than most people think—it’s exactly what happens when building out automated workflows and managing integrations. Keeping inference in the cloud while handling embeddings and caching locally feels like the perfect sweet spot for iterating fast without getting bogged down by infrastructure. I really appreciate you distilling your experience into this!