Purpose-Built Vector Database Comparison

2 posts · Sep 2020 – Mar 2026

What this trail covers

Milvus continues to be a self-hosted, purpose-built vector database solution for large-scale, multi-tenant applications. This post highlights its distributed nature, independent scaling of ingestion, indexing, and query execution, and its ability to meet strict performance and availability targets. The architecture emphasizes strong tenant isolation through partitioning strategies, with a focus on balancing recall, index size, and performance through index selection (e.g., HNSW). The evaluation process used by Reddit demonstrates a rigorous approach to selecting a vector database based on specific organizational needs and engineering culture, including a strong preference for open-source solutions and robust metadata filtering capabilities.

All Milvus trails

Mar 2026Beyond Naive RAG: Build Smarter Systems with Query Routing and Hybrid RetrievalThis post introduces query routing and unified hybrid retrieval as key advancements for modern RAG systems. It details a four-node architecture (Query Routing, Query Rewriting, Retrieval Strategy Selection, Minimal-Context Generation) to build smarter RAG pipelines. It highlights the benefits of unified hybrid retrieval, where dense and sparse vectors (e.g., BM25) can coexist in a single Milvus collection, simplifying operations and reducing overhead compared to maintaining separate systems. The post also emphasizes the importance of evaluating each RAG pipeline stage independently using specific metrics (e.g., F1 for routing, Recall@K for retrieval) to enable faster debugging and performance tuning.
Mar 2026Productionizing Semantic Search: How We Built and Scaled Vector Infrastructure at AirtableAirtable details their experience using Milvus for semantic search, focusing on architectural decisions and operational challenges. Key contributions include: a detailed explanation of their partitioning strategy (one base per partition, capped at 1000 partitions per collection to manage performance degradation), their index selection trade-offs (choosing HNSW over IVF-SQ8 and DiskANN for recall and performance), and the ingestion and query flows. Operational challenges addressed include node rotation with strict disruption budgets, cold partition offloading to manage memory costs, and a data recovery plan involving bringing up a fresh cluster and lazily re-embedding data. They also mention deploying Milvus via its Kubernetes CRD with the Milvus operator and their observability strategy.
Nov 2025Choosing a vector database for ANN search at RedditThis post details Reddit's evaluation process for selecting a vector database for ANN search. It outlines their methodology, including collecting team context, qualitative evaluation of solutions (Milvus, Qdrant, Weaviate, OpenSearch, Pgvector, Redis, Cassandra, Solr, Vespa, Pinecone, Vertex AI), and quantitative evaluation. Key requirements prioritized by Reddit included open-source nature, scale (100M+ vectors), community velocity, expressive metadata types and filtering, and support for multiple index types. Milvus was evaluated against these criteria, with the post noting its strengths in areas like hybrid search, multiple indexing methods, robust metadata attributes and filtering, and multi-cloud support, while also acknowledging areas where other solutions might have scored higher in specific aspects of the evaluation. The post emphasizes that the selection was specific to Reddit's culture and needs, serving as an inspiration for other organizations.
May 2025Why AI Databases Don't Need SQLThis post argues that SQL is becoming obsolete for modern AI applications, advocating for natural language interfaces and AI agents as the future of database interaction. It positions purpose-built vector databases like Milvus as superior to traditional SQL databases with bolted-on vector features, highlighting native multimodal support, agent-friendly APIs, semantic intelligence, and optimized relevance over raw speed. The post contrasts the fundamental philosophies of relational and vector databases, emphasizing that vector databases are designed from the ground up for AI-native workloads.
Mar 2025What Exactly is a Vector Database and How Does It WorkThis post provides a foundational explanation of vector databases, their purpose in bridging the semantic gap for AI applications, and the core concepts of vector embeddings and distance metrics. It contrasts vector databases with traditional relational databases, highlighting the limitations of simply adding vector indexes to existing systems. The post details the typical four-tier architecture (Storage, Index, Query, Service) of a production vector database and outlines the vector search workflow from data transformation to result retrieval. It also touches upon consistency challenges and mentions Raft for distributed consensus.
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Aug 2023Why and When Do You Need a Purpose-Built Vector Database?This post compares Milvus as a specialized vector database against vector search libraries (like FAISS) and vector search plugins within traditional databases. It highlights the limitations of libraries (scalability, real-time import) and plugins (lack of full-stack approach, limited tunability) and positions Milvus as a more robust, scalable, and feature-rich solution for large-scale vector search applications. It emphasizes Milvus's user-friendly APIs, broader index support, and cloud-native features.
Sep 2020Item-based Collaborative Filtering for Music Recommender SystemThis post details the implementation of an item-based collaborative filtering recommender system for a music sharing community (Wanyin App). It explains the process of extracting song feature embeddings using CNNs and then utilizing Milvus for embedding similarity search. The authors chose Milvus over Faiss due to its ease of use, abstract APIs, and comprehensive documentation. The post also describes the deployment of a highly available Milvus cluster using Mishards and Milvus-Helm on Kubernetes for production. Additionally, Milvus is applied to a duplicate song filtering scenario, where it's used as a preliminary step to filter candidate duplicate songs based on similar feature vectors before more computationally intensive audio fingerprint matching.