AI Model Routing & Cost Optimization. @ai-model-routing

The AI Model Routing & Cost Optimization sprint puts a routing layer between your product and the model providers. Most AI features send every request — trivial or hard — to one premium model, paying frontier prices for work a model a tenth the cost handles identically. The router classifies requests and sends each to the cheapest model that clears its measured quality bar, falls back on provider outages, and caches where semantics allow. The finish state is the router live in production, per-feature spend visible, quality verified unchanged by evaluation — and a before/after cost report on your real traffic.

Scrums.com tracks token cost, latency, and quality as first-class delivery metrics on GenAI work; this sprint turns those metrics into an active control system rather than a monthly bill surprise.

Workload & Model Benchmarking

Your request traffic profiled and segmented, then candidate models — commercial and open — benchmarked per segment on quality, latency, and cost, so routing rules rest on your data, not leaderboard folklore.

Routing Layer Build

A gateway in your stack (or a configured open router hardened for production) implementing the routing policy: rules or classifier-driven, provider-agnostic, with semantic caching where safe.

Failover & Availability Handling

Automatic fallback chains across providers and regions for outages, degraded-mode behavior under rate limits, and timeout budgets per feature — availability becomes a property of the layer, not of one vendor.

Spend & Outcome Tracking

Token and dollar spend attributed per feature, per model, and per customer segment, dashboards with budget alerts, and quality sampling that confirms routing changes never silently degrade output.

1. Scope — Profile traffic, fix quality bars per segment, and benchmark the candidate model set.
2. Build — Build the routing layer, shadow-test against production traffic, and tune until cost falls with quality held.
3. Handover — Production cutover, spend dashboards live, and a re-benchmarking playbook for when new models ship.

The AI Model Routing & Cost Optimization is a menu item on the Scrums.com delivery catalog, available at every plan tier. Add it to your plan backlog and your delivery team schedules it like any other item — scoped, tracked, and reported through the SEOP. See Delivery Plan Tiers.

How much can we expect to save?

It depends on how much of your traffic is over-served today — the benchmarking step tells you before the build commits. The deliverable is a measured before/after on your traffic, not a generic percentage.

How do we know quality does not drop?

Routing changes ship behind evaluation: shadow testing before cutover and ongoing quality sampling after. If you have no evaluation sets yet, the LLM Evaluation & Guardrails Package is the natural prerequisite.

New models launch constantly. Does the setup rot?

The router is provider-agnostic and the benchmarking harness is handed over, so adding a new model is a re-run, not a rebuild. The Model Ops Retainer on the menu keeps that loop running for you if you want it managed.