Ilian Madzharov

Looking at the raw per-token math renting an A10G or L4 on AWS/Vultr looks way cheaper than paying per token to OpenAI at scale. What I'm worried about is the hidden time sink vLLM crashes prompt tuning across model updates latency optimization load balancing and keeping up with new quantization releases. As a solo founder my time is already stretched thin. Did switching to self-hosted models actually save you net money or did the extra dev/maintenance hours wipe out any hosting savings?

My co-founder and I love how fast we can ship using OpenAI endpoints, but the unpredictable monthly bill is forcing us to cap feature usage. It feels terrible having to limit user experiments just to keep costs under control.

We're tempted to switch to self-hosted open-source models for predictable hardware costs but taking on server maintenance and potential drops in output quality gives us pause.

Our dev cycle has turned into a endless loop of patching one bug only to watch another feature break right after.

It feels like we spend more time re-testing existing features than actually building new ones.

We re trying to set up a lighter workflow so every update doesn't trigger a full manual check of the entire app.

What basic QA routines or lightweight tools keep your builds stable without slowing down your weekly ship date?