02 · Failover
Another compatible worker can take over.
If a worker disappears, the request can be routed to another worker able to serve the same model.
Technology · KairosQ
Distributed inference without giving up interaction.
KairosQ is distributed inference infrastructure for teams running multiple models or serving capacity across several hosts. Applications send requests to one endpoint; KairosQ discovers compatible workers, routes work and streams results back.
The worker pool can contain different kinds of models and capacity. Routing, failover, session affinity and discovery stay inside the infrastructure instead of being reimplemented by each application.
Capabilities
Keep inference available as the system changes.
03 · Sticky sessions
Session affinity can stay stable across calls.
Related requests can remain attached to the same worker when continuity matters.
04 · Model discovery
Workers announce what they can serve.
The available model catalog changes as workers join or leave, so routing follows the capacity that actually exists.
Consumption controlsAliasesPer-application testsOpenAI-compatible APIsOn-premise deployment
KairosQ is already used inside RelevAI infrastructure.
It is available for technical evaluation and on-premise deployment discussions.