This article compares MongoDB, Firebase, and Supabase for AI applications in 2026, focusing on vector embedding storage and LLM pipeline integration. It highlights MongoDB's Atlas Vector Search and Supabase's pgvector capabilities, contrasting with Firebase's lack of native storage and MongoDB's potential cost at scale.
The article describes how to build a zero-cost AI feature in Flutter apps by combining on-device inference with Gemma 4 and cloud sync with Firebase. This approach eliminates API fees and server costs, allowing AI-powered apps to scale without draining resources.
This content describes migrating an application to the new Gemini 3.1 Flash TTS Preview model for AI audio generation, which supports expressive emotions and pace. The application uses Firebase AI Logic to analyze images, generate obscure facts, and convert them into expressive audio via Firebase Cloud Functions, streamed to an Angular app.