EmbeddingGemma 2
Open 740M multimodal embedding model that unifies text, code, images, video, and audio in one vector space.
Updated 2026-10-07
Yes. EmbeddingGemma 2 is free to use.
Listed pricing: Free and open-source (Apache 2.0); self-host via Hugging Face, Kaggle, Ollama, and similar.
Overview
EmbeddingGemma 2 is Google DeepMind’s open 740M-parameter multimodal embedding model that maps text, code, images, video, and audio into a single shared vector space. It is built for retrieval, semantic search, and RAG pipelines where you need one index that can mix modalities instead of bolting separate embedders together.
The audience is developers and research teams who want strong multimodal retrieval without sending every query to a hosted API. Weights ship under Apache 2.0 on Hugging Face and related distribution channels, so you can run locally, on-device, or in private infra—useful when latency, cost, or data residency rules rule out cloud-only embedding services.
What sets it apart from typical text-only open embedders is the unified multimodal space at a relatively compact 740M size. That makes cross-modal search (image-to-text, audio-to-code snippets, etc.) practical on smaller hardware, but you still own the serving stack: quantization, index choice, and evaluation against your domain remain your problem, not Google’s managed product surface.
Is EmbeddingGemma 2 free?
Yes. EmbeddingGemma 2 is free to use.
What the free tier covers: Fully free under Apache 2.0; you pay only for your own compute and hosting.
Listed pricing: Free and open-source (Apache 2.0); self-host via Hugging Face, Kaggle, Ollama, and similar.
Pricing on this page has not been re-verified. The entry was last edited on , and no separate pricing check has been run since. Treat the figures as a record of what was published then and confirm on the official site.
EmbeddingGemma 2 pricing
| Plan | Price | What's included |
|---|---|---|
| Open source | Free | Apache 2.0 weights; self-host via Hugging Face, Kaggle, Ollama, and similar; no hosted SaaS tier from the model release itself |
Apache 2.0 weights; self-host via Hugging Face, Kaggle, Ollama, and similar; no hosted SaaS tier from the model release itself
Pricing on this page has not been re-verified. The entry was last edited on , and no separate pricing check has been run since. Treat the figures as a record of what was published then and confirm on the official site.
Is EmbeddingGemma 2 worth it?
You can test that on the free tier before paying anything. The recorded trade-offs are listed below, and any one of them can settle the question on its own.
The 8.7/10 AI Score is an editorial read of published capability, price and shipping pace. Nobody here has hands-on hours with EmbeddingGemma 2. How we verify.
Worth it if
The strengths recorded against this entry.
- Single embedding space across text, code, images, video, and audio for true cross-modal retrieval
- Apache 2.0 open weights—self-host, fine-tune, and redistribute without a closed API
- 740M scale suits on-device and private RAG better than giant multimodal stacks
- Distribution across Hugging Face, Kaggle, and Ollama lowers the barrier to trying it in existing MLOps flows
Not worth it if
Any one of these blocks your use case.
- You must run and evaluate it yourself—no turnkey managed embedding API from this release
- Multimodal quality still depends on your chunking, index, and domain eval; open weights are not a guarantee for every corpus
- Serving, quantization, and hardware choices fall on your team, unlike hosted competitors that hide ops
Key features
Unified embeddings
Maps text, code, images, video, and audio into one vector space so a single index can support cross-modal retrieval and multimodal RAG without separate modality-specific models.
On-device RAG
Sized and positioned for local and on-device search workflows, letting teams keep embeddings and retrieval close to the data for privacy and offline or edge use.
Open weights
Released under Apache 2.0 with weights available through channels such as Hugging Face, Kaggle, and Ollama, so you can inspect, fine-tune, and self-host without a proprietary API lock-in.
Compact multimodal
At roughly 740M parameters it targets a middle ground between tiny text-only embedders and heavy multimodal stacks, aiming for usable quality without enterprise-scale GPUs.
How it compares
| Tool | Best for | Pricing | Score |
|---|---|---|---|
| EmbeddingGemma 2 | — | Free and open-source (Apache 2.0); self-host via Hugging Face, Kaggle, Ollama, and similar | 8.7/10 |
| Perplexity AI | Knowledge workers and researchers who want cited, synthesized answers instead of a list of links to click through. | Freemium | 9.4/10 |
| NotebookLM | people who want cited answers and Studio outputs from a specific set of sources. | Free tier; Google AI Pro $19.99/mo | 9.1/10 |
| Inkling-Small | — | Free — open weights download; Tinker Playground access | 8.8/10 |
Compare head-to-head
Comparison explorer Put EmbeddingGemma 2 up against any three tools Opens with EmbeddingGemma 2 already loaded. Add up to three more from the full index and read pricing, features, pros and cons in one table. →Related reading
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Ready to try EmbeddingGemma 2?
Head to the official site to start with EmbeddingGemma 2 — pricing and plans are listed above.
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