Stop choosing between AI and data security | GoodData.AI Native Inference

Description

More companies want AI on their data without sending that data to a public LLM. Getting an open-weight model running on your own infrastructure takes an afternoon. The harder questions come after that. Are its answers about your business correct? Will they still be correct after the next model update? And what will it cost once hundreds of people use it every day?

GoodData Native Inference is our answer. It sits between your analytics and the model, runs inside your own environment, and checks answers against your certified metrics. You can keep your existing LLM providers alongside it.

This session is for data and platform teams who need AI that meets their security requirements and still gets the numbers right.

What we’ll cover:

• How we choose models for analytics questions, and why the biggest model is often not the best fit.
• What an inference layer built for analytics adds to a plain model endpoint: predictable latency, deployment inside your own boundary, and answers checked against your semantics.
• How we run evaluations on every change, so you can show the answers are still correct next week.
• What changes for your data team when inference runs in your environment, including costs you plan in GPU time instead of tokens.

Join us live to see how it works and bring your questions. Can’t make it? Register anyway, and we’ll send you the recording.