Deploy BGE-M3 multilingual embeddings with complete control
Run BAAI's 7B parameter model privately to generate 1,024-dimensional embeddings across 100+ languages. Power semantic search, QA, and retrieval workflows with fixed pricing and data privacy.
Why BGE-M3 excels at multilingual understanding
100+ language support
Generate high-quality 1,024-dimensional embeddings across over 100 languages with consistent semantic understanding and cross-lingual capabilities.
Multi-vector outputs
Choose between dense embeddings for similarity search or optional multi-vector outputs for advanced retrieval and reranking workflows.
Agent-ready design
Built specifically for retrieval, reranking, and agent workflows. Seamlessly integrate into RAG systems and hybrid search pipelines.
Built for advanced semantic search and retrieval
Multilingual embeddings
Generate consistent, high-quality embeddings across 100+ languages with unified semantic space representation.
1,024-dimensional vectors
Standard dense embedding output with 1,024 dimensions optimized for semantic similarity and retrieval accuracy.
Retrieval optimization
Purpose-built for information retrieval with excellent performance on semantic search and question-answering tasks.
Reranking capabilities
Advanced reranking functionality to improve search result relevance and ranking accuracy in multi-stage pipelines.
Multi-vector support
Optional multi-vector outputs for complex retrieval scenarios requiring fine-grained document representation.
Agent workflows
Designed for integration with AI agents, RAG systems, and hybrid search architectures requiring semantic understanding.
Perfect for multilingual AI applications
Semantic search
Cross-language information retrieval
- Deploy multilingual semantic search across documents in different languages. BGE-M3's unified embedding space enables cross-lingual similarity matching and retrieval.
Question answering
Multilingual QA systems
- Build QA systems that work across language barriers. Generate embeddings for questions and documents in different languages with consistent semantic understanding.
RAG systems
Retrieval-augmented generation
- Power RAG applications with high-quality multilingual embeddings. Retrieve relevant context across languages for more accurate and contextual AI responses.
Hybrid pipelines
Multi-stage retrieval workflows
- Implement sophisticated retrieval pipelines combining dense embeddings, multi-vector outputs, and reranking for maximum search accuracy and relevance.
How Inference works
AI infrastructure built for performance and flexibility with BGE-M3
01
Choose your configuration
Select from pre-configured BGE-M3 instances or customize your deployment based on performance and multilingual requirements.
02
Deploy in 3 clicks
Launch your private BGE-M3 instance across our global infrastructure with smart routing optimized for embedding generation.
03
Scale without limits
Generate unlimited embeddings at a fixed monthly cost. Scale your multilingual applications without worrying about per-request API fees.
With Inference, you get enterprise-grade infrastructure management while maintaining complete control over your multilingual embedding deployment.
Ready-to-use solutions
Multilingual search
Build cross-language search systems with consistent semantic understanding across 100+ languages.
RAG applications
Power retrieval-augmented generation with high-quality multilingual embeddings for contextual AI responses.
Agent workflows
Integrate embedding generation into AI agent systems for advanced reasoning and retrieval capabilities.
Frequently asked questions
What makes BGE-M3 different from other embedding models?
BGE-M3 is specifically designed as a multilingual embedding model supporting 100+ languages with 1,024-dimensional dense outputs. It offers optional multi-vector representations and is optimized for retrieval, reranking, and agent workflows, making it ideal for complex multilingual AI applications.
How many languages does BGE-M3 support?
BGE-M3 supports over 100 languages with consistent embedding quality. The model creates a unified semantic space where similar concepts across different languages have similar vector representations, enabling cross-lingual search and retrieval.
What are multi-vector outputs and when should I use them?
Multi-vector outputs provide multiple embedding representations per input, offering more granular document understanding. Use them for advanced retrieval scenarios requiring fine-grained similarity matching or when building sophisticated reranking pipelines.
Can I use BGE-M3 for cross-language semantic search?
Yes, BGE-M3 excels at cross-language tasks. You can search for documents in one language using queries in another language, as the model maps all languages into a unified semantic space with consistent similarity relationships.
How does private deployment ensure my data security?
With private deployment, your documents and queries never leave your controlled infrastructure. BGE-M3 runs entirely within your environment, ensuring complete data privacy while generating high-quality multilingual embeddings.
Deploy BGE-M3 today
Get multilingual embedding capabilities with complete privacy and control. Start with predictable pricing and unlimited usage.