How to choose the best model for semantic search
Semantic search decodes intent, context, and word relationships — unlike keyword matching which compares exact strings.
Key distinction: semantic vs. search embeddings
- Semantic embeddings: capture meaning for classification, translation
- Search embeddings: optimized specifically for retrieval — query and document embeddings align in vector space
5 factors for model selection
1. Results relevancy
Consider multilingual support, multimodal data, domain-specific performance. Larger models → better accuracy but higher cost; smaller models can be competitive.
2. Search performance (latency)
- Local models: ~10ms (no external API round trips)
- Cloud-based services: ~800ms
3. Indexing performance
Varies by API rate limits, batch processing, model dimensions — from <1 minute (optimized cloud) to several hours (local models without GPU).
4. Pricing
- Local models: free (but require compute)
- Cloud: 0.18 per million tokens (OpenAI, Cohere, Mistral, VoyageAI, Jina)
5. Optimization techniques
- Model presets for query vs. document embedding tuning
- Domain-specific models
- Reranking functions
- Quantization for reduced data transfer
Recommendation
Cloud-based solutions (Cohere, OpenAI) are optimal for most cases. As scale grows, local/self-hosted solutions may become worthwhile.