What’s the one thing that always breaks first when you take an AI project from demo to production?
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We’ve all been there- the prototype works beautifully, the demo looks magical… and then everything breaks when real users hit it.
Sometimes it’s context loss.
Sometimes it’s API chaos.
Sometimes it’s that mysterious “it works on my machine” moment.
While building GraphBit, we saw the same pattern again and again, great ideas collapsing under real-world load.
So I’m curious to hear from you:
What’s the #1 failure point you’ve hit when scaling an AI workflow to production?
Is it infrastructure, orchestration, data freshness, observability… or something totally unexpected?
Let’s compare notes, maybe we can make production AI a little less painful together.
— Musa
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Replies
Great question, Musa — and painfully relatable 😅. For us at Growstack, the biggest “demo-to-production” breaker has been orchestration under real user load. LLMs behave so differently once concurrency and messy inputs hit. It’s rarely a model issue — it’s the glue (context passing, retries, fallbacks) that starts cracking first.
Would love to hear how GraphBit handles that side of the chaos 👀
WhereFlight
GraphBit
@whereflightteam Haha, that’s a real one, we’ve all had that “who wrote this?” moment. That’s exactly why GraphBit focuses on traceable, auditable execution, so every AI action is logged, explainable, and safe before it ever hits production, try here: https://github.com/InfinitiBit/graphbit