SaaS Cost Control With Tiered Model Routing

Inference Spend Is a Product Decision

For a SaaS company, AI inference cost scales directly with usage. Sending every request to a full panel is expensive and often unnecessary. A tiered routing strategy using the SPRAPP suite keeps quality where it matters and cost low where it does not.

Tier One: TinyLM On-Device or On-Server

The bulk of requests are routine. Routing these to TinyLM — whose eeny and meeny models run on-device or on your own server — removes per-call cloud cost entirely for that tier. The economics of on-device inference are simply different: the marginal request is effectively free.

Tier Two: Panel for the Hard Requests

Reserve SPRAPP Panel for requests where being wrong is costly. Asking a panel once and converging gives higher reliability than a single model, and you only pay for it on the fraction of traffic that needs it. Where the panel disagrees, you have surfaced ambiguity worth a human's attention.

Guarding Every Tier With Filter

Regardless of tier, Sprappy Filter screens inbound requests across its 25 categories first. A cheap tier should not be a soft target — Filter ensures untrusted content does not slip through just because it was routed to a smaller model.

Shrinking the Request Log With smoltext

Request logs are dominated by short strings — request IDs, route tags, status codes. smoltext compresses these short strings well where gzip stumbles on small payloads, cutting the storage cost of keeping a full request history for analytics and billing.

The Net Effect

Most traffic runs effectively free on TinyLM, the costly Panel is reserved for high-stakes requests, Filter guards every path, and smoltext trims the log bill. Cost tracks value.

How to Phase It

Start by classifying your traffic, route the clearly-routine slice to TinyLM, and only then tune the threshold for escalating to Panel.