The right model for every task. At the lowest cost.
Enterprise agentic workflows burn tokens on the wrong models, using GPT-4-class inference for tasks a smaller model handles just as well. The Cendriix Model Router classifies every task and dispatches it to the right model, enforcing cost caps before spend happens.
Intelligent routing, enforced cost control.
Task-aware model selection
Every agent task is classified by complexity, latency requirement, and output type before a model is selected. Simple retrieval tasks never hit GPT-4-class models when a smaller model does the job.
Cost cap enforcement
Set hard token budgets per run, per workflow, or per team. The router enforces them before dispatch, not after the bill arrives. Overages are blocked, not just logged.
Multi-provider routing
Route across OpenAI, Anthropic, Azure OpenAI, AWS Bedrock, Google Vertex, and self-hosted models from one unified interface. Switch providers without changing agent code.
Latency-aware fallback
If a primary model is slow or unavailable, the router falls back to an equivalent model automatically, maintaining SLAs without manual intervention.
Per-call audit log
Every model call is logged with the model used, token counts, latency, cost, and the routing decision that selected it. Full cost attribution by agent, workflow, and team.
Prompt caching integration
Shared system prompts and knowledge graph context are cached across calls, eliminating redundant token spend on repeated context injection across a multi-step workflow.
See how tasks get routed.
Pick a task type. The router classifies complexity, latency needs, and data sensitivity before dispatching to the optimal model.
Token costs go down. Output quality stays up.
Cut your LLM spend without touching your agents.
Model Router is part of the Cendriix platform. Works with any agent framework. First workflow live in days.
Book a pilot