What’s the biggest hidden cost you’ve faced when running AI in production?
It’s easy to measure latency or accuracy.
But the real costs often hide in the background- compute burn, idle tokens, redundant calls, or that “temporary” caching fix that quietly eats your budget.
We’ve seen it again and again:
AI projects don’t collapse because of complexity…
They collapse because of inefficiency.
While building GraphBit, we kept asking —
Can we make agents faster, cheaper, and lighter without cutting corners on reliability?
That question led us down the path of Rust, concurrency, and smarter orchestration.
But I’m curious —
👉What’s the biggest invisible inefficiency you’ve run into with AI systems?
- Is it compute waste, model overcalls, messy retries, or data bloat?
Let’s compare notes.
Because in the race to make AI powerful, efficiency might be the real innovation.
— Musa
Replies
minimalist phone: creating folders
I can see complaints from many users that some companies have just automated their customer support, and when they need to solve any specific problem, they get a general, vague AI answer that doesn't solve anything. In such a case, they appreciate human touch.
GraphBit
@busmark_w_nika that’s such an important point. AI should enhance the human touch, not erase it. The real efficiency is when automation handles repetition, so humans can focus on real problem-solving.
@busmark_w_nika @musa_molla I've noticed this too, it's getting very annoying.
For us, the biggest hidden cost is redundant context loading, agents re-processing the same information across different tasks instead of maintaining a smart state.
We've been obsessed with making agents that don't just work, but work efficiently.
GraphBit
@viriava Exactly, Viktor. Redundant context loading is brutal. That’s one of the core things we optimized in GraphBit, shared memory and state tracking so agents don’t waste cycles relearning the same info. You can check how we’re tackling it here: github.com/InfinitiBit/graphbit
Triforce Todos
Inefficiency is the real villain here.
GraphBit
@abod_rehman Inefficiency quietly drains both compute and creativity. That’s why GraphBit’s core goal is to make every cycle count: github.com/InfinitiBit/graphbit
Pretty Prompt
LLMs in Production are a super interesting topic, especially as it's very different from running them in a Development environment... @cerwindcharlie, my co-founder, wrote a great article about this not long ago.
Exactly the things you mentioned about, speed, latency, cost, back up, observability and more.
GraphBit
@cerwindcharlie @ilaiszp Totally, Ilai. Running LLMs in production is a whole different game. Would love to read that article by Charlie, it’s a topic we care deeply about at GraphBit, especially around observability and resilience under real workloads.
Pretty Prompt
I'm building https://picxstudio.com (AI image generator) with a feature that creates 30-40 unique images per click. My testing costs are spiraling - an intern generated $100+ worth of images without realizing the cost, and the same happened with friends testing it. I need to figure out how to manage these expensive testing phases until paying users can help offset the costs.
GraphBit
@yash_patidar_ That’s a real challenge, Yash. Cost creep during testing can be brutal. We’ve seen similar issues across AI teams, which is why we’re building fine-grained monitoring and caching layers into GraphBit to help control exactly that. You can peek under the hood here: github.com/InfinitiBit/graphbit
More server/database costs on your project indeed unlees you don't retouch and optimize to your reveived code of course.
GraphBit
@ercan_throner Absolutely, Ercan. Server and DB costs sneak up fast if you don’t keep optimizing. That’s exactly why we focused on concurrency and caching in GraphBit, to cut that hidden overhead early. You can check how we approached it here: github.com/InfinitiBit/graphbit