FAISS vs Qdrant vs Weaviate vs ChromaDB: Vector DB Comparison

Vector Database Overview

Vector databases store and search high-dimensional embeddings generated by models like BGE, E5, and BERT. Choosing the right vector store for your dedicated GPU server affects query latency, scalability, and integration complexity. GigaGPU provides hosting for all four: FAISS, Qdrant, Weaviate, and ChromaDB.

Search Performance Benchmarks

We benchmarked all four stores on a 1-million-vector index (1024 dimensions) running on an RTX 3090 server. FAISS-GPU uses GPU search; the others use CPU-based HNSW. All return top-10 results.

*FAISS-GPU throughput is GPU-bound, not CPU-thread-bound.

FAISS-GPU delivers 7-12x faster search than CPU-based alternatives at 1 million vectors. At 10 million vectors (benchmarked in our vector database GPU guide), the gap widens further.

Filtered Search Comparison

Filtered search (e.g., find similar documents where category = “legal” and date > 2024) is a critical production requirement. This is where the databases diverge significantly.

*FAISS filtered search requires over-fetching and post-filtering, which is inefficient.

Qdrant leads in filtered search performance and flexibility. If your application requires filtering alongside similarity search, Qdrant is the strongest choice.

Scalability and Index Size

FAISS handles the largest single-node indexes thanks to efficient memory usage and GPU offloading. Qdrant and Weaviate scale horizontally for distributed deployments. ChromaDB is best for datasets under 5 million vectors.

GPU Acceleration Support

Only FAISS supports GPU-accelerated search natively. The other databases run on CPU. However, all four benefit from GPU acceleration for the embedding generation step, which often takes more time than the search itself.

For most RAG pipelines, the LLM generation step dominates total query time, not the vector search. This means GPU selection should prioritise LLM throughput. See our RAG pipeline GPU guide and embedding generation benchmarks for details.

RAG Pipeline Integration

All four vector databases integrate with LangChain and LlamaIndex. For framework selection, see our LangChain vs LlamaIndex comparison.

FAISS lacks a built-in server, so it runs in-process or behind a custom API wrapper. Qdrant, Weaviate, and ChromaDB run as standalone services with REST APIs, making them easier to deploy as part of a microservices architecture.

Which Vector DB Should You Choose?

Choose FAISS if: You need maximum search speed, run large indexes (10M+ vectors), and do not require complex filtering. FAISS-GPU on an RTX 3090 handles 6,100 qps at $0.45/hr. Best for batch processing and speed-critical applications.

Choose Qdrant if: You need production-grade filtered search, a managed service option, and horizontal scalability. Qdrant is the best all-around choice for production RAG deployments on GigaGPU dedicated servers.

Choose Weaviate if: You need hybrid search (vector + keyword), built-in reranking, or GraphQL-style queries. Good for applications that combine semantic and keyword search.

Choose ChromaDB if: You want the simplest possible setup for prototyping and small-scale RAG. ChromaDB runs embedded in your Python process with zero configuration. Deploy on GigaGPU ChromaDB hosting.

For GPU selection for your vector database stack, see our guides on the best GPU for vector database workloads, best GPU for LangChain, and the best GPU for LLM inference.