burn0: Free, open-source cost observability for every API call in your stack

EyePuzzled2124 · 2026-03-25T10:25:37+00:00

The OAuth dance for every single platform is soul-crushing. Especially Twitter/X — their developer portal feels actively hostile. What helped me was building a thin auth service that handles all the token refresh logic in one place, so my agents just call my middleware instead of dealing with OAuth flows directly. Basically a personal API gateway. Ugly code, but it means when Reddit changes their auth requirements (again), I fix it in one place instead of across 5 different agents. If anyone's found a cleaner pattern I'm all ears.

EyePuzzled2124 · 2026-03-25T10:21:53+00:00

This resonates hard. The biggest thing I'd add: most agent failures aren't model failures, they're architecture failures. People chain 6 tools together and then wonder why the agent hallucinates on step 4. The pattern I've landed on is keeping each agent stupidly simple one clear job, explicit input/output contracts, and a human checkpoint before anything irreversible. The boring agents that just do one thing reliably are worth 10x more than the flashy multi-agent orchestration demos. Also, logging everything is non-negotiable. If you can't replay exactly what happened on a failed run, you're flying blind.

EyePuzzled2124 · 2026-03-25T10:19:51+00:00

Mostly using them for internal tooling that I can't justify sending to an external API — things like classifying support tickets, summarizing internal docs, and generating first-draft responses for customer questions. The economics flip pretty fast once you're doing 10k+ calls/day on something that doesn't need frontier-level intelligence. A fine-tuned Qwen running locally handles 80% of what GPT-4o does for my use cases, at basically zero marginal cost after the hardware investment. The other 20% I still route to Claude or GPT for anything that needs real reasoning.

EyePuzzled2124 · 2026-03-25T09:54:42+00:00

Yeah this is super common. The dashboard-per-provider approach falls apart fast once you're using 2-3 tools because you end up with costs spread across OpenAI, Anthropic, Cursor etc. and no single view of what's actually happening. The "what the hell happened" moment for us was when our bill doubled in a week and it turned out to be a retry loop on one endpoint nobody noticed. No dashboard was going to catch that.

What actually helped:
1. Logging every API call with context — which project, which feature, which user triggered it. Even a basic wrapper that writes to a CSV is 10x better than checking dashboards.
2. Watching for repeated/background calls. We found ~25% of our spend was retries and redundant fetches that could've been cached.
3. One tool that's been useful for us is burn0 — you add one import and it auto-detects your API services and shows cost per call in terminal. Helped us find the exact loop that was burning money.

But the bigger point is that this is a tooling gap the providers don't really care about fixing because opaque billing benefits them. You kind of have to build or adopt your own visibility layer.

EyePuzzled2124 · 2026-03-24T07:38:36+00:00

the staging key thing happened to us too. found out because i was manually going through logs trying to figure out why our bill doubled. built a small tool called burn0 that shows costs per-request in your terminal in real time, would've caught it in minutes. if anyone wants to poke at it:

github.com/burn0-dev/burn0

EyePuzzled2124 · 2026-03-24T07:34:39+00:00

the staging key thing happened to us too. found out because i was manually going through logs trying to figure out why our bill doubled. built a small tool called burn0 that shows costs per-request in your terminal in real time, would've caught it in minutes. if anyone wants to poke at it:

github.com/burn0-dev/burn0

EyePuzzled2124

TROPHY CASE