AI Cost Optimization at the Gateway.

The router reads three signals on every request: the policy classification of the payload, the expected complexity of the task, and the latency budget the calling application has declared. Simple summarization, classification, extraction, and templated rewrite traffic routes to a small-tier model such as Claude Haiku 4.5. Multi-step reasoning, code synthesis, and high-stakes generation routes to a flagship such as Claude Opus 4.7.

Routing rules are written once in the control plane and apply across every provider the gateway is configured against. A rule that says “route classification tasks to the small-tier model” resolves to the appropriate small-tier model on whichever provider the caller is allowed to use, so the enterprise does not lock the rule to a specific vendor.

Sensitivity overrides cost. A prompt that contains regulated data classifications routes to the model that the active policy permits for that data class, even when a cheaper model would be available. Cost is the optimization target; the policy is the constraint.

Provider outages are a recurring operational reality. When OpenAI returns 5xx for a sustained window, applications that talk to OpenAI directly stop working until OpenAI recovers. Applications that talk to DeepInspect keep working, because the gateway reroutes traffic to a configured secondary provider such as Anthropic or AWS Bedrock without changing the calling code.

Failover decisions are policy-bound. The control plane defines, per route, which providers are eligible substitutes and under what conditions a substitute is allowed. A route that handles regulated workloads can be configured to fail closed rather than fail over, so the gateway returns a block instead of routing to a provider the policy does not permit for that data class.

Health checks are continuous and per-provider. Latency, error rate, and rate-limit signals feed the routing decision so the gateway shifts traffic before a hard outage manifests at the application layer.

The gateway records input-token count, output-token count, and provider-reported cost for every forwarded request, attributed to the corporate identity that issued the call. A finance leader can pull a quarterly report of AI spend by department, application, or project, joined to the same identity context that the security policy evaluated.

Per-actor accounting also surfaces shadow AI. When a department’s spend exceeds expectation or a previously-unseen application begins issuing AI traffic, the cost record is the first signal that catches it. Cost telemetry and security telemetry come from the same record in the forensic store, so the same query that answers “who is spending the most” also answers “who is sending sensitive data.”

Webhooks push the token-and-cost stream to existing finance and observability stacks (Snowflake, BigQuery, Datadog, Grafana) so the enterprise does not need to operate yet another dashboard.

Cost optimization that lives in application code requires every team to implement routing, failover, and accounting on their own, and produces inconsistent coverage as soon as a second application ships. Cost optimization that lives in a separate sidecar adds a second hop to the request path and a second control plane to operate.

The gateway already evaluates identity, policy, and data sensitivity on every request. Routing, failover, and accounting are the same evaluation extended with cost as an objective. One control plane, one record per request, one place to change the rule.