At Cashcrown, we analyze the query profile: which tasks require a large model and which can be handled by a smaller, cheaper one. We build an LLM router with semantic cache and batching; PII is masked before sending to the cloud, and volumes justifying self-hosting are calculated based on your real data, not estimates.
FAQ
#How much does this service cost, and when does it pay off?
#We work in tiers based on query volume and your current stack. Token savings depend on task distribution and your existing setup; we measure them before deployment, so we only guarantee savings after analyzing your profile. You can calculate the exact ROI using the ROI calculator; we don’t provide upfront pricing because it depends on the actual scope and chosen deployment model.
Is the integration GDPR-compliant, and how does it handle our data?
#The router does not store query content: PII is masked before any external API call, and the cache stores only anonymized vectors. Sensitive data paths are routed to local or self-hosted models, eliminating cross-border EU data transfers. GDPR and AI Act requirements are incorporated into the architecture design before anything goes into production.
Where do we start?
#With an inference log audit: we measure task distribution, cost per query, and points where quality can be maintained with a cheaper model. The pilot covers one query stream with a before-and-after cost/quality comparison. More on the approach: how to choose the first process.