I'm building an image similarity feature, should I use a vector database?

Yes—if you’re doing similarity search over image embeddings at any meaningful scale, a vector database is usually a good fit.

When a vector DB makes sense

Use one if you need:

• “Find images like this” search
• Deduplication / near-duplicate detection
• Recommendation / visual search
• Fast nearest-neighbor lookup over thousands to millions of images
• Metadata + similarity filtering together (e.g. “similar to this shirt, size M, under $50”)

How it typically works

1. Convert each image into an embedding vector using a model (e.g. CLIP, ResNet, ViT, etc.).
2. Store the vector in a vector index / vector database.
3. At query time, embed the query image and retrieve the nearest vectors by cosine similarity, dot product, or Euclidean distance.

When you might not need one

A vector DB may be overkill if:

• You have only a few thousand images
• Search is rare/infrequent
• You can tolerate slower brute-force search
• Your similarity logic is simple and can be handled in memory or in a basic database

In those cases, you might start with:

• FAISS (great for local/in-memory indexing)
• pgvector if you already use Postgres
• A small search service with embeddings stored in обычный DB + approximate search library

Common options

• FAISS: very fast, library-based, not a full DB
• pgvector: easiest if you already use PostgreSQL
• Pinecone / Weaviate / Milvus / Qdrant: full vector DBs with indexing, scaling, filtering, APIs
• Elastic/OpenSearch: if you already use them and need hybrid keyword + vector search

Rule of thumb

• Prototype / small dataset: FAISS or pgvector
• Production, scale, filtering, multi-tenant, updates: vector DB
• Hybrid search (text + image): consider a system that supports both vectors and metadata well

If you want, I can help you choose between FAISS, pgvector, Qdrant, Pinecone, or Milvus based on your expected dataset size, update rate, and latency target.