Continuum Hub:
Unified Management for Distributed LLM Usage
Continuum's architecture, governance features, and adoption criteria
As more teams and sites adopt LLMs, policy, API key, and cost information scatters across gateways, while consolidating every request into a single central gateway creates a single point of failure for all inference traffic. Continuum separates the two paths: Routers deployed near your applications handle inference requests, and Hub centrally manages policies and costs. Learn why a control-plane failure does not spread into the request path, how external LLM APIs and Backend.AI inference endpoints come under one governance standard, and the criteria for evaluating adoption.
Who Is This Brief For?
Continuum Hub is designed for organizations that operate external LLM APIs and self-hosted models together across multiple teams, sites, and clouds. It serves environments where a central platform team manages model access policies per user and workload, where costs must be attributed and budgeted per team, project, or customer, and where policy changes must be rolled out in stages with an auditable change history, extending to air-gapped and regulated environments that need to track service health and spending without storing prompt or response bodies.
Continuum Hub provides policy management, policy rollouts, cost monitoring and settlement, and metadata-based observability in a single management plane. See the full solution brief for an operational comparison with central-proxy tools such as LiteLLM, a step-by-step evaluation sequence, and recommended specifications by scale.
Download Resource
Please fill out the form below.
Related Services
Backend.AI is a vendor-agnostic accelerated workload hosting platform based on our own home-grown orchestration and job scheduler, running on top of either cloud or on-premises (air-gapped) clusters.
Explore service →