Qdrant vs. FAISS on Scalability
No. Only scale at the server level.
No. Can not scale beyond single node.
No distributed data replacement
With static sharding, if your data grows beyond the capacity of your server, you will need to add more machines to the cluster and re-shard all of your data. This can be a time-consuming and complex process. Additionally, imbalanced shards can introduce bottlenecks and reduce the efficiency of your system.
Without any distributed data replacement, FAISS is not able to scale beyond a single node
Qdrant vs. FAISS on Functionality
Performance is the biggest challenge with vector databases as the number of unstructured data elements stored in a vector database grows into hundreds of millions or billions, and horizontal scaling across multiple nodes becomes paramount.
Furthermore, differences in insert rate, query rate, and underlying hardware may result in different application needs, making overall system tunability a mandatory feature for vector databases.
No. Authentication only
Yes (combine vector and traditional indices)
FLAT, IVS_FLAT, IVF_SQ8, IVF_PQ, HNSW, BIN_FLAT and BIN_IVF_FLAT
Qdrant uses three types of indexes to power the database. The three indexes are a Payload index, similar to an index in a conventional document-oriented database, a Full-text index for string payload, and a vector index. Their hybrid search approach is a combination of vector search with attribute filtering.
FAISS functionality FAISS is an algorithm to support kNN search.
Qdrant vs. FAISS on Purpose-built
What’s your vector database for?
A vector database is a fully managed solution for storing, indexing, and searching across a massive dataset of unstructured data that leverages the power of embeddings from machine learning models. A vector database should have the following features:
- Scalability and tunability
- Multi-tenancy and data isolation
- A complete suite of APIs
- An intuitive user interface/administrative console
Python, Go, Rust
Qdrant vs. FAISS: what’s right for me?
Qdrant is maintained by a single commercial company offering a cloud version of Qdrant. License: Apache-2.0 license
Faiss is a powerful library for efficient similarity search and clustering of dense vectors, with GPU-accelerated algorithms and Python wrappers, developed at FAIR, the fundamental AI research team at Meta License: MIT license