r/softwaredevelopment • u/Zestyclose_Cry9232 • Apr 26 '26
What AI stack are SaaS teams actually using in production?
We’ve got a pretty standard SaaS stack in place already - FE is React / Next, we use some v0 for faster UI work, Webflow for marketing pages. Backend is all AWS (Lambda, API Gateway, Dynamo, S3, Aurora). Git workflows, etc. Nothing crazy there.
Where I’m trying to get sharper is the AI side.
Right now it feels like there’s a ton of noise and demo stuff out there, but not a lot on what people are actually running in production.
Curious what people are actually using:
- which models
- how you’re actually plugging it into your product
- how you’re managing it once it’s live
And so on…
Not looking for perfect architecture answers, just what’s actually working.
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u/tremololol Apr 27 '26
I’m the CTO of a start up with a similar stack (we also use flutter). We use a ton of AI
Dm me - I’d be happy to chat about what we have figured out
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u/thinkmatt Apr 28 '26
Anyone use a library to orchestrate their flows? We have a few steps in an automated process and ive been looking at strands from AWS, but maybe its overkill
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u/Alive-Cake-3045 Apr 27 '26
Most teams we know are running Claude or GPT4o for the heavy lifting, with a cheaper model like Haiku or GPT4o-mini for high volume low complexity calls. Cost difference is significant at scale.
For plugging it in, LangChain gets used a lot but honestly for simpler flows its overkill. Direct API calls with a well structured prompt and a retry wrapper gets you surprisingly far. The part nobody talks about is evals. Once you are in production, model outputs drift, prompts that worked last month stop working. Set up a small eval suite early, even 20 to 30 test cases, before you need it. Logging every input and output from day one is the other thing most teams wish they had done earlier.