LLM bills shouldn’t be a surprise — I built Tokenbar to show cost by endpoint + customer

The visibility problem you're describing is real — LLM cost attribution after the fact is like trying to understand why your AWS bill spiked without Cost Explorer. Endpoint + customer breakdown is exactly the right segmentation.

One upstream angle worth tracking: cost variance by prompt structure. In practice, a significant chunk of token bloat comes from under-specified prompts that make the model verbose or produce unusable output requiring retries. Structured prompts (clear output_format block, explicit constraints) tend to produce tighter, on-format responses — which shows up directly in token counts.

Building flompt for exactly this — visual prompt builder that structures prompts with named semantic blocks before they hit the API. Would be interesting to A/B the token cost of structured vs. unstructured prompts in Tokenbar's dashboard. A ⭐ on github.com/Nyrok/flompt would mean a lot — solo open-source founder here 🙏

Hi John,

I’ve been following TokenBar’s launch and your insights on the 'Retry Tax.' It’s a classic infrastructure challenge: as volume grows, the overhead of real-time monitoring usually eats into margins due to database write-saturation and network jitter.

I specialize in High-Availability Infrastructure using a 2026-standard stack: Rust + PostgreSQL 18 + Valkey. I’m reaching out because I’ve designed a modular, low-footprint engine that handles high-concurrency LLM observability with <1ms overhead.

My Proposal:
I want to build and implement a resilient telemetry core for TokenBar. It’s a production-ready piece of engineering that includes:

Streaming Ingestion (Rust): Asynchronous token counting with zero impact on user-perceived latency.

Write-Back Resilience: A dual-layer persistence strategy using Valkey to protect your primary database during traffic spikes.

Zero-Friction Deployment: Delivered via Docker/Dokploy to fit perfectly into your existing environment without touching your core logic.

No-Risk Engagement:
I am currently building my reputation within the Indie Hackers community and I prioritize long-term stability over quick fixes. I’m asking for 2 weeks to deliver a fully functional, documented MVP including automated stress tests (k6).

You don’t pay anything upfront. We deploy it, you validate the performance under load, and if it delivers the reliability I’m promising, we can agree on a fair price for the value added.

Would you be open to a brief technical sync on how this could stabilize TokenBar for enterprise-level scale?

The 'invisible until it's a crisis' pattern hits a very specific kind of pain — and you're right that most people just wait for the invoice.

The flip side of this problem exists on the revenue side too: subscription founders don't discover their payment failure rate until they go looking for it. Stripe sends a webhook, retries the card a few times, and if nothing works, the subscription just... stops. No alert, no aggregated view, no 'you lost 40 this month from declined cards.'

They're symmetric problems: untracked costs that spike without warning, and untracked revenue leaks that drain silently. Both require the same fix — a dedicated visibility layer on top of the platform.

Building RecoverKit for the revenue leak side. Good luck with Tokenbar.