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Also known as: Routing Policy, Model Routing Policy
A set of rules that directs requests across models or endpoints based on criteria like cost, latency, or performance.
DESCRIPTION
In AI systems, a router policy determines how requests are dispatched among multiple models, versions, or providers. Policies can be rules-based (e.g., route by task, user, or cost) or learned (e.g., bandits optimizing reward). They enable canary testing, failover, and dynamic load distribution across heterogeneous backends. Implementations often leverage service meshes for traffic shaping and observability. Good policies combine business constraints with model health metrics, safety filters, and budget caps. Continuous evaluation and feedback loops refine routing decisions over time.
ALTERNATIVE DEFINITIONS
Istio
You can configure rules to control how requests are routed to services within an Istio service mesh.
Emphasizes policy-driven request routing in service meshes.
KServe
You can split traffic between different model revisions by specifying traffic percentages for each revision.
Highlights model version routing via percentage-based policies.
LangChain
Router chains dynamically select from multiple destination chains based on the input.
Shows input-aware routing for LLM toolchains.
RELATED TERMS
Model Routing A/B Testing Multi-armed Bandit Load Balancer Service Mesh Traffic Shaping Ensemble Prompt Router Canary Release Cost Optimization
COMPETING TERMS
Static Routing Manual Model Selection
RELATIONS
- Broader: Policy
KNOWLEDGE AREAS
MLOps AI Data Integration & Interoperability Observability & Telemetry Software Engineering
TARGET AUDIENCE
MLOps Engineer AI Engineer DevOps Engineer Data Architect Software Architect
EXTERNAL SOURCES
- Istio - Request routing with rules
- KServe - Traffic splitting for models
- Ambassador - Gateway traffic policies
TOP QUESTIONS
Why is a Router Policy important in multi-model AI systems?
It optimizes cost, latency, and quality by directing each request to the best-fit model or endpoint, improving reliability and ROI.
Category: Relevancy
How does a Router Policy differ from a load balancer?
Load balancers distribute traffic across identical instances; router policies consider model capabilities, costs, and metrics to route to heterogeneous backends.
Category: Misunderstanding
What signals inform a Router Policy for LLMs?
Task type, prompt length, latency budget, cost per token, model health, and historical quality scores typically drive routing decisions.
Category: Requirements
How can we experiment with Router Policies safely?
Use canarying and A/B tests with guardrails and rollback, tracking user-facing metrics and error budgets.
Category: Testing
What risks exist with Router Policies?
Poorly tuned policies can route to underperforming or expensive models, degrade UX, or violate compliance constraints.
Category: Risks
When should we use learned routing versus rules?
Start with clear rules; adopt bandits or learned policies when sufficient data exists to optimize reward under dynamic conditions.
Category: Opinion
How do SLAs interact with Router Policies?
Policies must respect latency/availability SLAs, incorporating failover, circuit breakers, and budget-aware routing.
Category: Management
Can Router Policies enforce data residency or privacy constraints?
Yes, policies can route based on region, provider trust levels, and content classification to satisfy regulatory requirements.
Category: Compliance
What tools support implementing Router Policies?
Service meshes (Istio), API gateways, KServe, and LLM orchestration frameworks (e.g., LangChain) provide primitives for dynamic routing.
Category: Overview