If you have been building with LLMs, you have probably had one of these moments: A surprise bill at the end of the month A model silently returning garbage without an error No idea which of your services is driving the cost spike I built Torrix to fix that. A self-hosted LLM observability platform that logs every call, calculates costs token by token, and flags anomalies automatically. The problem with self-hosted tools : you can't easily try before you install. You need Docker, a server, credentials, 10 minutes of setup. Most people bounce. So I built a live demo . No signup. No Docker. No installation . Just click and explore. Here is what's in it. The setup The demo loads 30 days of LLM traces across 3 simulated projects: Production API : GPT-4o and Claude Sonnet handling user requests Data Pipeline : batch summarisation, GPT-4o-mini doing the heavy lifting Customer Support Bot : mixed model routing, Haiku for simple queries, Sonnet for complex ones 640 runs. 5 models. Real cost and token data.…