What is Kadryn?
Understand how Kadryn helps teams monitor, govern and optimize AI usage.
Overview
Kadryn is an AI FinOps and LLMOps platform for teams that need to understand and control how AI is used across products, teams and environments.
It connects usage from gateway traffic, direct ingestion events and integrations, then turns that raw activity into normalized cost, operational alerts, policy decisions, optimization opportunities and audit trails.
What Kadryn tracks
Kadryn tracks AI activity at the level your team can act on:
- Provider, model and endpoint usage.
- Project, team, feature and environment metadata.
- Normalized cost and allocation.
- Gateway request IDs, trace IDs and status.
- Alerts, incidents and runbook context.
- Guardrail decisions, approvals, exceptions and audit events.
Who uses Kadryn
Developers use Kadryn to debug provider calls, verify ingestion and trace failed requests. Platform teams use it to standardize routing, provider keys, metadata and reliability workflows. Finance teams use it to understand spend, allocation, forecasts and reporting. Admins use it to configure access, policies and workspace-level controls.
Gateway versus direct ingest
Kadryn supports two complementary ingestion models. Gateway mode sits in the request path and can enforce controls before a request reaches a provider. Direct ingest mode sends usage events after the fact and is better when you want observability without changing runtime traffic.
Most teams start with one model, then combine both as their AI footprint grows.
What good setup looks like
A good Kadryn setup has consistent metadata, server-side secrets, one owner per budget or policy, a clear alert routing path and a reliable way to connect usage records back to application traces.
When those pieces are in place, Kadryn becomes more than a dashboard. It becomes the control plane for AI cost, reliability and governance.