The Hidden Cost of AI Analytics: Inference, governance, and data control in production AI

Description

AI analytics is showing up in places where it can actually move the needle: customer-facing apps, internal workflows, self-service BI, executive reporting. Once it's there, architecture matters as much as the feature.

Inference stops being a technical detail the moment AI touches the user experience. It decides cost, latency, which models you can use, and how good the experience actually feels. Governance matters just as much: what data the AI can see, which metrics it should trust, where that data is allowed to move, and how much control your team needs over the path from question to answer.

This session is for teams past the prototype stage, figuring out which use cases are worth scaling, what to govern first, and how inference strategy, sovereignty, and semantic layers fit together. We're also sharing first benchmark results: our inference stack against a self-hosted open-source setup. Real latency, throughput, and cost numbers. Not token-price math.

What we'll cover:

• Why inference becomes a product and architecture decision as AI usage grows.
• How model choice, caching, routing, and orchestration hit cost, latency, and UX.
• Where hallucinations, metric drift, and weak business context create risk.
• What access control and data sovereignty mean once AI is built into analytics.
• How semantic layers give AI governed metrics and business definitions to work with.
• First benchmark results: our stack vs. a self-hosted open-source stack.
• Which use cases are worth scaling: semantic search and anomaly detection, self-service BI, executive summaries, customer-facing analytics.