Production use cases

End-to-end implementation guides for turning catalog intelligence into routing, reliability, release, and cost controls.

Choose a workflow

Resolve model identity: Record what actually served a request: canonical model, provider, deployment, region, API surface, and rate card.
Build reliable failover: Precompute a compatible fallback set before a provider incident starts.
Route by constraints: Apply hard product constraints first, then rank only eligible deployments.
Price a SaaS feature: Convert representative token usage into unit economics, credits, and margin guardrails.
Track model EOL: Surface scheduled retirement dates, evidence, and replacement IDs before migration becomes an incident.
Catch release drift: Make lifecycle or compatibility drift fail CI instead of production.
Attribute AI costs: Attach normalized cost to customers and features across providers and retries.

Shared implementation pattern

Discover: query canonical models and concrete deployments.
Constrain: reject incompatible lifecycle, region, limit, and capability states.
Decide: compare the remaining candidates using representative usage.
Record: persist the deployment ID, catalog release, evidence, and assumptions.
Re-evaluate: run the same decision again when the catalog release or lifecycle data changes.

ALLM is the decision layer

ALLM does not proxy model traffic or perform runtime health checks. Your gateway executes the request; ALLM provides the deployment-level facts used to build and audit the routing plan.

Before production

Use a server-side API key and a bounded timeout.
Treat unknown as a first-class state, not as supported, unlimited, or free.
Persist x-allm-catalog-release with every durable decision.
Test empty candidate sets, missing pricing, provider failure, and stale lifecycle coverage.
Keep human review for lifecycle conflicts and large price deltas.