Carbon
The analytics layer of AI
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Carbon - The analytics layer of AI

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Know exactly what your AI is doing and what it costs. Carbon captures every LLM and tool call in 2 lines of code and turns it into a queryable ledger — cost over time, spend by model, heaviest users, tool failures, where agents stall. Every provider, 1,000 events free.

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Hi Product Hunt — Pranjal here. We started building with LLMs like everyone else: wire up a few providers, ship fast, watch it work. Then the bill came — one number, no story behind it. We couldn't tell which feature, which agent, or which user was driving it, and our logs were a million rows nobody wanted to read. The questions that actually decide the roadmap went unanswered: • Why did our AI bill double this month? • Which agent burned $300 last Tuesday? • Which model is actually cheapest for this task? We were optimizing our most expensive system blindly. So we built Carbon. How it works: • Wrap your existing client in 2 lines of code — every call is captured automatically • One ledger across every provider: OpenAI, Anthropic, Google, Vercel, Mistral, OpenRouter, DeepSeek and more • Drill into clusters — cost, models, your heaviest users, and where agents stall • Built for humans and the agents in your stack: we track tool calls, not just chat It's free forever for 1,000 events/month, no credit card. I'll be here all day. Tell me the one question about your AI you can never answer today, and I'll show you the Carbon view that answers it.

 Cost is the obvious first use case, but quality matters too. Are you planning to connect spend with outcomes, so teams can compare cheapest model versus best model for a task?

Maker

 Hi Jerome — yes, that’s exactly the direction.

Cost is the wedge, but the bigger idea is value: cost + latency + reliability + outcome for each task.

Today Carbon gives you the ledger across models, users, agents, tool calls, failures, and duration. The next step is tying that to evals/outcomes, so teams can answer: “is the cheaper model actually good enough here?”

Because optimizing AI spend shouldn’t mean lowering quality — it should mean finding the best model for the job.
You can try it free without a card, or just browse the demo. Happy to show you what this view could look like for your own workflows.