u/monrow_io

Building a tool for AI apps that:

- stops infinite AI retry loops
- catches users accidentally burning hundreds in API costs
- alerts you when one feature suddenly gets expensive
- lets you pause all AI calls if something goes wrong

before the bill gets out of control.

Free version works locally and blocks bad calls automatically.

Paid version adds:

- Slack alerts
- visibility across all servers
- see which users/features are costing the most
- remote kill switch for production apps

Trying to figure out if this is actually painful enough for people to pay $49/mo for.

If you run AI features in production and this sounds useful, DM me.

Going to be straight with you. I built this. I want you to use it. If that's not your vibe, scroll on.

But look at this first.

https://preview.redd.it/q5jsdri0in1h1.png?width=1138&format=png&auto=webp&s=444a0f74d128c98f4eb234b4fa5fd0f9bfc7a89d

18 users. $4,377.60 in AI cost this month.

412 users. $89.04.

That triangle next to summarization means something is wrong. 18 users should not cost 49 times more than 412 users. That is not a pricing problem. That is a bug, a retry loop, or a feature that is structurally broken and nobody noticed because nothing crashed.

This is the thing that gets AI SaaS founders. Normal bugs break things visibly. AI bugs succeed silently while the bill climbs.

I got hit by this. One user stuck in a loop hitting a summarization endpoint nearly 3,000 times in 14 minutes. App was fine. No errors. No alerts. Just a climbing Anthropic bill I noticed too late.

So I built Monrow.

You tag your Anthropic or OpenAI calls with a feature name. One extra line. Monrow tracks cost per feature locally and shows you the breakdown with one command. Free. No account. No network calls.

It also watches every call your app makes and throws an error before the next one fires when something looks like a runaway loop. Your code catches it and handles it gracefully. The disaster does not happen.

Three lines to add to your existing code. Runs inside your process. Not a proxy. Calls still go directly to Anthropic or OpenAI. Zero latency.

Free forever. No card. No signup.

The honest reason to pay:

If you run more than one server the free detector breaks. Each server sees part of the traffic and none of them trip the threshold. Pro at $29 a month connects all your servers so detection works across your whole system. That is the only real difference. I am not going to pretend otherwise.

If you are building AI SaaS right now install the free tier. Three minutes. You will immediately see which features are costing you and whether any of your users are in a loop right now.

Search monrow sdk on npm. Search devmonrow Monrow on GitHub to read the full source before installing anything. Landing page is monrow.io.

Ask me anything. I will answer everything honestly.

Going to be straight with you. I built this. I want you to use it. If that's not your vibe, scroll on.

But look at this first.

https://preview.redd.it/6zhseqsugn1h1.png?width=1138&format=png&auto=webp&s=0465f1354dde3e84fd211d2491e4956e356bcf80

18 users. $4,377.60 in AI cost this month.

412 users. $89.04.

That triangle next to summarization means something is wrong. 18 users should not cost 49 times more than 412 users. That is not a pricing problem. That is a bug, a retry loop, or a feature that is structurally broken and nobody noticed because nothing crashed.

This is the thing that gets AI SaaS founders. Normal bugs break things visibly. AI bugs succeed silently while the bill climbs.

I got hit by this. One user stuck in a loop hitting a summarization endpoint nearly 3,000 times in 14 minutes. App was fine. No errors. No alerts. Just a climbing Anthropic bill I noticed too late.

So I built Monrow.

You tag your Anthropic or OpenAI calls with a feature name. One extra line. Monrow tracks cost per feature locally and shows you the breakdown with one command. Free. No account. No network calls.

It also watches every call your app makes and throws an error before the next one fires when something looks like a runaway loop. Your code catches it and handles it gracefully. The disaster does not happen.

Three lines to add to your existing code. Runs inside your process. Not a proxy. Calls still go directly to Anthropic or OpenAI. Zero latency.

Free forever. No card. No signup.

The honest reason to pay:

If you run more than one server the free detector breaks. Each server sees part of the traffic and none of them trip the threshold. Pro at $29 a month connects all your servers so detection works across your whole system. That is the only real difference. I am not going to pretend otherwise.

If you are building AI SaaS right now install the free tier. Three minutes. You will immediately see which features are costing you and whether any of your users are in a loop right now.

Search monrow sdk on npm. Search devmonrow Monrow on GitHub to read the full source before installing anything. Landing page is monrow.io.

Ask me anything. I will answer everything honestly.

A retry loop against a normal API costs basically nothing.

A retry loop against Claude or GPT-4 can quietly run up a massive bill before anyone notices.

I hit a bug recently where one user got stuck in a loop and spammed a summarization endpoint a few thousand times in a short window. Nothing crashed. Requests were succeeding. The only signal was the API spend climbing.

What surprised me most is a lot of the normal protections don’t really help once traffic is distributed across multiple instances.

One server sees 40 requests.
Another sees 40.
Another sees 40.

Individually everything looks fine even though the aggregate pattern is obviously wrong.

I ended up getting interested in this enough that I started building some internal tooling around it, but I’m curious how other people are handling it today.

Curious how people are dealing with this in production right now.

Are you:
- building custom middleware around the SDKs?
- relying on provider-side limits?
- pushing this into observability tooling?
- just eating the occasional mistake?

A retry loop against a normal API costs basically nothing.

A retry loop against Claude or GPT-4 can quietly run up a massive bill before anyone notices.

I hit a bug recently where one user got stuck in a loop and spammed a summarization endpoint a few thousand times in a short window. Nothing crashed. Requests were succeeding. The only signal was the API spend climbing.

What surprised me most is a lot of the normal protections don’t really help once traffic is distributed across multiple instances.

One server sees 40 requests.
Another sees 40.
Another sees 40.

Individually everything looks fine even though the aggregate pattern is obviously wrong.

I ended up getting interested in this enough that I started building some internal tooling around it, but I’m curious how other people are handling it today.

Curious how people are dealing with this in production right now.

Are you:
- building custom middleware around the SDKs?
- relying on provider-side limits?
- pushing this into observability tooling?
- just eating the occasional mistake?

A retry loop against a normal API costs basically nothing.

A retry loop against Claude or GPT-4 can quietly run up a massive bill before anyone notices.

I hit a bug recently where one user got stuck in a loop and spammed a summarization endpoint a few thousand times in a short window. Nothing crashed. Requests were succeeding. The only signal was the API spend climbing.

What surprised me most is a lot of the normal protections don’t really help once traffic is distributed across multiple instances.

One server sees 40 requests.
Another sees 40.
Another sees 40.

Individually everything looks fine even though the aggregate pattern is obviously wrong.

I ended up getting interested in this enough that I started building some internal tooling around it, but I’m curious how other people are handling it today.

Curious how people are dealing with this in production right now.

Are you:
- building custom middleware around the SDKs?
- relying on provider-side limits?
- pushing this into observability tooling?
- just eating the occasional mistake?

Been building an AI SaaS and honestly didn’t expect the hardest part to be understanding the AI costs once usage became real lol

Not even crazy traffic either.

Just noticed weird stuff starts happening:

- certain features suddenly costing way more than expected

- few users using WAY more AI than everyone else

- retries / loops burning requests

-prompts slowly getting bigger over time

At first I thought watching total OpenAI/Anthropic spend was enough but it honestly doesn’t explain much.

Still hard to tell what part of the product or what users are actually causing the cost spikes.

Curious if other people building AI products are dealing with this too or if I’m overthinking it

One thing I think a lot of AI SaaS founders underestimate is how weird unit economics get once real users show up.

It’s easy to get excited about growth until you realize your “power users” are costing you way more in inference than they pay every month.

Feels like AI products now have to balance usefulness vs survivability way earlier than traditional SaaS did.

Curious how people here are thinking about AI cost per user long term.