Engineers who got tired of guessing.
Debugging an AI feature in production meant stitching together four dashboards, a log search, and a bill that showed up a month later. We got tired of it, and built the single place we wanted.
One platform for everything running in production
Trasys watches your application the way your team actually debugs it — following a single request from the browser, through your services and databases, into the model call, and back out with a cost attached. When something breaks, the alert reaches whoever is on call, with the context already gathered.
The Intelligence Layer for AI Teams
Most observability tools were designed before anyone shipped a language model to production. They can tell you a request was slow — not which prompt caused it, what it cost, or why the answer changed overnight. These are the three things we think a platform for AI teams has to get right.
One place, not five
Traces, logs, cost, and infrastructure belong in one product. Correlating them across four vendors is where incidents go to die.
Cost is a first-class signal
Token spend sits next to latency and errors — not in a billing dashboard nobody opens until the invoice arrives.
Cause found: Prompt v18.2 deployed at 2:45 PM — added +448 tokens.
Answers, not dashboards
Ask what happened in plain English and get an answer pulled from your own data, instead of hunting across six tabs.
From a client project to a platform
Trasys didn't start as a product idea. It started as a problem we hit on someone else's project, and the realisation that every tool built to solve it was too expensive, too complex, or aimed at a DevOps team we didn't have.
Every tool was too heavy
Building observability into a client project, we tried what was already out there. Everything was priced for enterprises, assumed a dedicated DevOps team, or took weeks of learning before it told us anything useful.
A roadmap before a line of code
We wrote down what the platform had to do before building any of it — which signals to capture, how they should connect, and what stack could carry traces, logs, and model calls at volume without a team to babysit it.
Built the thing we needed
Tracing, cost tracking, on-call rotations, one query language across two databases, synthetic monitoring. Each piece shaped by the same test: would this have helped us on the project that started all of it?
Opening it up to everyone
Trasys goes live this August — quick to integrate, priced for teams without a DevOps department, and useful on day one instead of week three. That was the whole point.
The people behind Trasys
Three developers with fourteen years of engineering between them, building the tool they went looking for and couldn't find.
Shagun Monga
Founding EngineerFour years building production systems.
Deepak Sharma
Founding EngineerFive years building production systems.
Hardik Upadhyay
Founding EngineerFive years building production systems.