Intelligent Routing for Specialized AI Model Portfolios

Routing not only maximizes quality; it controls costs and latency budgets.

Token Budgeting#

• Use request classifiers to estimate token usage before sending to a model. If the expected cost exceeds thresholds, re-route to a more efficient model or ask users to refine requests.
• Apply budget envelopes per customer tier; warn or throttle when usage approaches limits.

Response Time Goals#

Set service level objectives (SLOs) for each request class. For example, quick knowledge lookups might have a 1-second SLO, while complex analyses can accept 10 seconds. The controller should select models compatible with these targets and monitor actual latency distributions.

Caching and Reuse#

Cache frequent queries or intermediate embeddings. For content retrieval combined with generation, precompute index lookups and reuse them across models to reduce redundant work.

Batching and Parallelism#

Group similar requests to exploit batching capabilities. Parallelize multi-step workflows—run retrieval, reasoning, and formatting models simultaneously when dependencies allow.