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Switch to open multimodal embeddings to cut your RAG infrastructure costs
◆ 85RelevanceOn a story from Google DeepMind11h ago
If you are building search or RAG systems, this provides a lightweight, self-hostable alternative to proprietary embedding APIs. It allows you to process diverse data types locally without the latency or cost of larger models.
Takeaways
- Self-hostable multimodal embeddings reduce reliance on expensive proprietary API calls.
- Lightweight design enables deployment on smaller, more cost-effective compute instances.
- Unified indexing of text and visual data simplifies multimodal search architectures.
Read the original at deepmind.google
EmbeddingGemma 2: an open, lightweight multimodal embedding model
fmode.me/n/embeddinggemma-2-an-open-lightweight-multimodal-embedding-model
Written by Founder Mode using gemini-3-flash-preview, from the publisher's own summary. We link the original rather than reproduce it — the reporting belongs to Google DeepMind.
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