CustomersTeams who trust Trasys
Teams who trust Trasys
with production AI.
From on-call engineers debugging a broken trace to platform teams defending a budget, these are the teams running Trasys against real production traffic.
Track every language, framework, and database
JavaScript
Python
TypeScript
Go
Node.js
PostgreSQL
MongoDB
JavaScript
Python
TypeScript
Go
Node.js
PostgreSQL
MongoDB
Redis
Prisma
AWS
GCP
Azure
Kubernetes
Docker
Redis
Prisma
AWS
GCP
Azure
Kubernetes
Docker
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Peloton“Trasys cut our AI incident MTTR from over an hour to under ten minutes. Deep Search finds the broken request before our on-call engineer has even opened a dashboard.”
Elena RuizStaff Engineer, Platform
83%faster MTTR
B
Booking.com“We were flying blind on LLM cost until AI Monitoring showed us one agent quietly driving 40% of spend. That single dashboard paid for itself in a week.”
Marcus WebbEngineering Manager, AI Platform
40%cost driver identified in week 1
S
Spotify“TQL let us stop maintaining two query dialects for ClickHouse and Postgres. Our on-call rotation now writes one query instead of two.”
Priya AnandSRE Lead
2→1query languages to maintain
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Pinterest“Nudges caught a slow token-usage drift three weeks before it would have crossed any alert threshold we'd configured.”
Jordan LeeHead of ML Infrastructure
3wksearlier warning than any alert rule