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The SLM vs. LLM choice affects latency, token costs, governance, and deployment flexibility. See which one fits your embedded analytics needs

AI token cost is now a line item in the CIO’s budget, especially for SaaS teams shipping AI-powered embedded analytics. Every natural language query, generated dashboard, and automated insight inside your embedded analytics layer burns tokens from large language models. Across a multi-tenant SaaS platform with thousands of users, that adds up fast. Controlling AI token consumption requires real governance: guardrails, model flexibility, and usage monitoring. Reveal built these controls into its AI-powered embedded analytics from day one, so your team can scale AI analytics without watching costs spiral.

Learn how users can create AI-generated dashboards by asking natural-language questions right inside your product. A step-by-step guide

How AI-powered analytics accelerates data insights and why Reveal offers secure, customer-controlled embedded AI analytics for modern apps.

Learn how to ensure security with embedded analytics before implementing AI-powered analytics from a third-party provider.

Learn how AI turns natural language into insights and how conversational analytics boost Saas and ISV products adoption and monetization