Reddit - Vector Database Selection
In 2025, Reddit’s engineering team ran a systematic evaluation to replace fragmented ANN vector search solutions with a single company-wide vector database. After rigorous qualitative scoring and quantitative benchmarking, they selected Milvus over Qdrant.
Problem
Reddit teams were using multiple incompatible ANN solutions:
- Google Vertex AI Vector Search
- Apache Solr ANN for larger datasets
- Facebook FAISS in vertically-scaled sidecars for smaller datasets
This fragmentation created inconsistent capabilities, high operational overhead, and gaps in feature support (e.g., FAISS has no filtering).
Solution
Selected Milvus as the unified vector database for ANN search across Reddit teams.
Evaluation Process
- Requirements collection from all interested teams (functional + non-functional); requirement bias was actively challenged
- Qualitative scoring of 11 candidates on ~60 weighted criteria
- Quantitative benchmarking: Qdrant v1.12 vs Milvus v2.4 at 340M post vectors (384 dims, HNSW M=16, efConstruction=100)
- Open-source as a hard constraint (eliminated Vertex AI, Pinecone)
Key Results
- Milvus’s heterogeneous node architecture (separate query/ingest/index) isolated ingest from query traffic better than Qdrant’s homogeneous nodes
- At RF=2, Milvus sustained higher QPS with acceptable latency
- Qdrant had better raw latency in some single-replica tests
- Milvus won on organizational fit: Go codebase (matches Reddit’s stack), automatic rebalancing, stronger project velocity
Scale
- ~340M Reddit post vectors
- 384 dimensions
- Testing at 100–400 QPS with filtering and ingest load
Company
Tools
- Milvus Vector DB — selected solution
- Qdrant Vector DB — main alternative
People
- Chris Fournie — evaluation lead