---
title: "Deploy Embedding Gemma"
description: "Generate high-quality text embeddings with Google's best model"
category: "AI/ML"
url: https://railway.com/deploy/embedding-gemma
---

# Deploy Embedding Gemma

Generate high-quality text embeddings with Google's best model

**[Deploy Embedding Gemma on Railway](https://railway.com/template/embedding-gemma)**

- **Creator:** ISDev
- **Category:** AI/ML
- **Total deploys:** 8

## Template content

### Embedding Gemma https://registry.npmmirror.com/@lobehub/icons-static-png/latest/files/dark/gemma-color.png

- **Image:** ollama/ollama
- **Start command:** `/bin/sh -c "ollama serve & pid=$!; sleep 5; ollama pull $MODEL; wait $pid"`

### Auth Proxy https://img.icons8.com/?size=80&id=AZ2LjCeHqRan&format=png

- **Source:** https://github.com/FraglyG/CaddyAuthProxy
- **Health check:** /health
- **Public domain:** Yes

## Documentation

# Deploy and Host EmbeddingGemma AI on Railway

EmbeddingGemma is Google's state-of-the-art embedding model that generates vector representations of text for search and retrieval tasks. With just 308M parameters, it achieves #1 ranking on the MTEB leaderboard among models under 500M parameters, making it perfect for privacy-focused, on-device applications that require high-quality embeddings.

## About Hosting EmbeddingGemma

Hosting EmbeddingGemma provides access to the highest-ranking open multilingual text embedding model under 500M parameters, trained on 100+ languages and optimized to run on less than 200MB of RAM with quantization. This deployment handles vector embedding generation, multilingual text processing, and semantic similarity computations. With sub-15ms inference latency for 256 tokens, it's ideal for real-time applications requiring fast, accurate text embeddings without sending data to external services.

## Common Use Cases

- **Semantic Search Systems**: Build powerful search engines that understand meaning rather than just keywords
- **Document Classification**: Automatically categorize and organize text content across multiple languages
- **Recommendation Engines**: Create content recommendation systems based on semantic similarity
- **RAG Applications**: Power retrieval-augmented generation systems with high-quality embeddings
- **Clustering and Analytics**: Group similar documents and perform advanced text analytics
- **Privacy-First AI**: Run embedding generation on your own infrastructure without data leaving your environment

## Dependencies for EmbeddingGemma Hosting

- **Ollama Runtime**: Serves the EmbeddingGemma model through standardized API endpoints
- **Authentication Proxy**: Secures access to your embedding generation service
- **Vector Processing**: Handles high-dimensional embedding computations and similarity calculations

### Deployment Dependencies

- [EmbeddingGemma Model Documentation](https://developers.googleblog.com/en/introducing-embeddinggemma/)
- [Ollama EmbeddingGemma Model](https://ollama.ai/library/embeddinggemma)
- [MTEB Benchmark Results](https://huggingface.co/spaces/mteb/leaderboard)
- [Railway Deployment Guide](https://docs.railway.com/)

### Implementation Details

This template is a distro of the [Ollama API](https://railway.com/deploy/ollama-api) template but comes pre-configured with EmbeddingGemma. 

Usage example:

```
POST /api/embeddings

Headers: 
    Authorization: Bearer your-api-key  
    Content-Type: application/json

Body: {
  "model": "embeddinggemma:300m",
  "prompt": "Your text to embed"
}
```

The model uses bi-directional attention architecture, effectively functioning as an encoder optimized specifically for embedding generation rather than text completion.

## Why Deploy EmbeddingGemma on Railway?

Railway is a singular platform to deploy your infrastructure stack. Railway will host your infrastructure so you don't have to deal with configuration, while allowing you to vertically and horizontally scale it.

By deploying EmbeddingGemma on Railway, you are one step closer to supporting a complete full-stack application with minimal burden. Host your servers, databases, AI agents, and more on Railway.

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