Google EmbeddingGemma 2
EmbeddingGemma 2 is Google DeepMind's open embedding model that maps text, code, images, video and audio into one shared space, so you can search across them from a single query.
EmbeddingGemma 2 is Google DeepMind's open embedding model that maps text, code, images, video and audio into one shared space, so you can search across them from a single query.
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Google EmbeddingGemma 2 is a practical fit for buyers evaluating Artificial Intelligence, Audio, and Video tools.
Product information
What this product does
EmbeddingGemma 2 is Google DeepMind's open embedding model that maps text, code, images, video and audio into one shared space, so you can search across them from a single query. At 740M parameters it runs on-device, needing about 567MB of RAM on a Pixel 11 Pro for the full multimodal model (191MB for text only). Apache 2.0 licensed, with weights on Hugging Face and Kaggle. MTEB Code score rises from 68.76 to 78.68.
Company & Maker
Who built Google EmbeddingGemma 2?
- Product
- An open, lightweight multimodal embedding model
- Live since
- Oct 2026
The maker hasn't published a public statement we can verify yet.
Researched with AI · Data refreshed 6 hours ago · How we score
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EmbeddingGemma 2 is an open-weights multimodal embedding model from Google DeepMind that maps text, code, images, video and audio into one shared vector space. It is used by ML engineers and developers who need on-device cross-modal search or retrieval without calling a hosted API.
Who should use
Buyer-fit is derived from approved personas and use-case evidence. Search-volume noise stays labeled separately.
Best-fit workflows
- Generates a single vector index across text documents, screenshots and audio clips for cross-modal search.
- Embeds source code and natural-language queries into one space to power code search over a local repository.
- Runs offline semantic search inside an Android app without sending user content to a server.
- Builds a local RAG pipeline that retrieves images and video frames alongside text chunks.
- Prototypes a multimodal recommendation index on a single workstation before scaling.
Pricing reality
Vendor stated
Apache 2.0 open weights, free to download from Hugging Face and Kaggle
Actual cost
No license fee, but real cost is compute: on-device inference needs ~567MB RAM for the multimodal model, and server-side batch embedding requires GPU capacity you provision yourself.
Hidden costs
3 hidden cost warnings — unlock to view
Pricing reality
Last updated Oct 2026
Starting price
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LIVE ESTIMATEEstimated spend
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Activity signals
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HOW THIS TOOL SCORES
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Functionality Score™
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Data sources
Traffic & search proof
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Google Trends live interest
Live Google Trends data for Google EmbeddingGemma 2: interest over time, regions, topics, and related queries.
Tracks 12-month Google search interest on a 0-100 relative interest scale.
Includes regional demand, so readers can see where search interest is strongest.
Includes related topics and related queries to surface adjacent demand and search intent.
FAQ
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Sources and methodology
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