Firebase provides developers with a powerful suite of tools for integrating generative AI, but choosing the right architecture is critical. This talk presents a direct comparison of two primary approaches: using the Firebase AI Logic client SDKs versus implementing the Genkit backend framework.

We will begin with Firebase AI Logic, which provides secure SDKs for calling Gemini models directly from the client device (web, iOS, or Android). We'll explore its most advanced features, including the Gemini Live API for real-time, bidirectional voice and text streaming, enabling truly natural, conversational experiences. Critically, we will also cover its "hybrid AI" strategy, which intelligently blends fast on-device models (for privacy, offline access, and speed) with more powerful cloud-hosted models, ensuring your app's AI features are both responsive and reliable.

Next, we will dive deep into Genkit, an open-source framework for building, deploying, and monitoring robust AI flows on the backend. Genkit is designed for complex, server-side tasks:

Retrieval-Augmented Generation (RAG): Connecting AI to your own data in Firestore or other sources.

Multi-step "Flows": Orchestrating multiple model calls, APIs, or database lookups.

Tooling: A local UI for testing, tracing, and observability.

Deployment: Seamless integration with Firebase Functions and App Hosting.

This session will feature practical code examples for both methods and will clearly define the decision point: when is the client-side power of AI Logic sufficient, and at what point should you adopt Genkit to build scalable, testable, and production-ready AI backends?