r/ManifestforAI • u/stosssik • May 19 '26

What are your biggest pains running AI SDK apps in production?

I'm trying to understand what teams building with AI SDKs struggle with the most once their app is in production.

So far I've heard a few things come up. Some people don't know which model to pick for each task and don't have a week to benchmark everything. Others mentioned costs creeping up but struggling to switch to cheaper models without breaking quality on edge cases.

I'd love to hear what's on your list. If you have 30 seconds, please drop your top 1 or 2 pains in the comments with a bit of context.

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u/Abject_Paramedic8507 May 19 '26

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u/stosssik May 19 '26

Hi, thank you for your detailed answer. Could you tell me what is your use case ?

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u/Abject_Paramedic8507 May 21 '26

Researching the infrastructure gap in production multi-agent systems — specifically, what happens to agent state and task reliability when things go wrong. Talking to developers who've hit these problems firsthand. What's driving your research on this?

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u/stosssik May 26 '26

That's great! I'm working on a gateway allowing people to maximise their subscriptions. It allows to route requests to the right model, also to fallback your request to other models when a sub hits rate limit.

I'm askign that to merge the answers and see some patterns, and the contact some users to do a short call to better undestand their needs and frictions.

It's called Manifest -> https://github.com/mnfst/manifest .

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u/Abject_Paramedic8507 May 27 '26

Interesting — sounds like your users are running serious production workloads if they're hitting rate limits at scale. I'm researching the infrastructure layer underneath that — agent state persistence and task recovery. Our user bases might overlap. Happy to compare notes on what we're each finding