do i need Qdrant for filtered similarity search?

Not necessarily.

You need a vector database like Qdrant if you want similarity search over embeddings with metadata filters at scale and with good performance.

When Qdrant makes sense

Use Qdrant if you want:

• Semantic/vector similarity search
• Filtering by metadata like tenant_id, category, date, language, etc.
• Fast search over large collections
• Hybrid retrieval patterns for RAG / recommendation / deduplication

Example:

“Find the 10 most similar documents to this query embedding, but only for tenant_id = 42 and status = published.”

Qdrant is built for that.

When you don’t need Qdrant

You may not need it if:

• Your dataset is small
• You only need keyword search or SQL filters
• You can do vector search in your app/DB and then filter afterward
• You’re using another system that already supports vectors + filters

Alternatives

Depending on your stack, these can also do filtered similarity search:

• Postgres + pgvector
• Pinecone
• Weaviate
• Milvus
• Elasticsearch / OpenSearch (vector + filters)
• MongoDB Atlas Vector Search

Rule of thumb

• Need semantic search + structured filters? Use Qdrant or another vector DB.
• Need only simple filtering or small-scale search? A regular DB may be enough.

If you want, I can help you choose between Qdrant vs pgvector vs Elasticsearch for your exact use case.