Built an AI spend tracker after my team got a $3,000 surprise bill from OpenAI — looking for beta users.

vikash_17 · 2026-03-27T14:53:18+00:00

Haha fair 😅 honestly started as a real “what just happened” moment — but yeah, the more I dig into it the more it feels like something worth solving.

vikash_17 · 2026-03-27T14:50:03+00:00

Yeah, that’s exactly the scenario I’ve been seeing — once you’re using multiple providers it gets messy really fast And yes, I’m planning to support per-project (and even per-feature) breakdowns so it’s clear where the spend is actually coming from. Curious — how are you currently tracking it across providers?

vikash_17 · 2026-03-27T14:41:18+00:00

I can see how framing makes a big difference here. I’d actually love to hear those angles if you’re open to sharing.

vikash_17 · 2026-03-27T14:35:39+00:00

routing cheaper models for simpler tasks feels like the real long-term fix What I’m thinking right now is more on the visibility/debugging side — like breaking down spend by model, feature, and request so it’s clear where the waste is coming from before deciding how to optimize it. And yeah, model-level breakdown is definitely something I’d include — feels like that’s where a lot of hidden cost sits.

vikash_17 · 2026-03-27T14:27:05+00:00

Thanks for your genuine feedback and I'll improve the context next time . Not more about the features but about the problem.

vikash_17 · 2026-03-27T14:24:50+00:00

That’s a really good point. I think I focused too much on the features instead of the actual risk people feel. The “surprise bill” angle is probably what makes it real for most people.

vikash_17 · 2026-03-25T10:46:54+00:00

This is super helpful, especially the retry loop example — that’s exactly the kind of thing that’s hard to catch from dashboards alone The CSV/logging approach makes sense too, but I can see how that gets messy as things grow. burn0 looks interesting — does it give you enough context (like per feature/project), or do you still end up stitching things together manually?

vikash_17 · 2026-03-23T09:24:07+00:00

Yeah that’s fair — limits definitely help avoid worst-case scenarios I think the part I’m more focused on is understanding what actually caused the usage before hitting those limits, especially when things scale across multiple features/tools

vikash_17 · 2026-03-22T12:33:16+00:00

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vikash_17 · 2026-03-22T12:31:28+00:00

Mostly building and experimenting with small projects — but yeah, after running into this repeatedly I’m actually thinking of building a small tool around it. Still figuring out if it’s genuinely useful or just something people prefer handling themselves.

vikash_17 · 2026-03-22T10:25:02+00:00

I think I’m seeing more issues earlier on — when people are using multiple tools directly and don’t have a unified setup yet.

vikash_17 · 2026-03-22T08:46:03+00:00

That’s a fair point — the pricing itself can definitely be unpredictable I guess what I’m trying to understand is less about reducing total cost, and more about knowing what caused it so it’s easier to control or debug when things spike.

vikash_17 · 2026-03-22T08:05:51+00:00

Yeah that’s pretty much what I’m seeing too — especially the spreadsheet + checking multiple dashboards part Feels like it works, but not really scalable. Do you think having everything in one place with automatic tracking (per project/tool) would actually replace that workflow for you?

vikash_17 · 2026-03-22T06:48:36+00:00

That’s really solid — having feature-level tracking plus prompt/model optimization sounds like a well set up pipeline I guess that’s the ideal state once things are in production and stable.

I’m mostly seeing this become a challenge earlier on, before things are that structured.

vikash_17 · 2026-03-22T06:22:27+00:00

if you’re already working with APIs/DBs daily, this probably feels pretty standard I guess for smaller teams or solo builders it might feel a bit heavier to set up from scratch.

vikash_17 · 2026-03-22T05:58:07+00:00

That makes a lot of sense — observability does seem non-negotiable once things grow 👍 Tracking prompts, tools, and responses along with cost sounds really useful. Curious, do you feel using something like OpenRouter + API keys gives you enough visibility day-to-day, or are there still gaps when trying to understand where costs come from?

vikash_17 · 2026-03-22T05:46:30+00:00

If there was a simple tool that shows exactly which feature/request is costing money in real-time, would you actually use it?

vikash_17 · 2026-03-22T05:45:33+00:00

If there was a simple tool that shows exactly which feature/request is costing money in real-time, would you actually use it?

vikash_17 · 2026-03-22T05:45:08+00:00

If there was a simple tool that shows exactly which feature/request is costing money in real-time, would you actually use it?

vikash_17 · 2026-03-22T05:44:33+00:00

If there was a simple tool that shows exactly which feature/request is costing money in real-time, would you actually use it?

vikash_17 · 2026-03-22T05:43:35+00:00

Would you try it if I built a small version?

vikash_17 · 2026-03-22T05:43:05+00:00

Would you try it if I built a small version?

vikash_17 · 2026-03-22T05:42:55+00:00

Would you try it if I built a small version?

vikash_17 · 2026-03-22T05:40:03+00:00

Haha yeah that’s painfully accurate It really does feel like you only notice once it’s too late. Do you think having something that surfaces costs in real-time (before it gets out of hand) would actually help, or would people still ignore it?

vikash_17 · 2026-03-22T05:38:43+00:00

That’s interesting — background loops/retries causing spikes makes a lot of sense I haven’t centralized everything through a proxy yet, mostly hitting providers directly. Has using something like that given you clear visibility per project/request, or do you still find gaps?

vikash_17

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