# Survey of Vector Database Management Systems

arXiv: https://arxiv.org/abs/2310.14021 · HTML: https://mlsanity.com/paper/2310.14021/

## Key facts

- Cited by 39 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Oct 2023.

## Built on by (most cited first, 30 of 39)

- [When Large Language Models Meet Vector Databases: A Survey](https://mlsanity.com/paper/2402.01763/) (2024)
- [vCache: Verified Semantic Prompt Caching](https://mlsanity.com/paper/2502.03771/) (2025)
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- [Top Ten Challenges Towards Agentic Neural Graph Databases](https://mlsanity.com/paper/2501.14224/) (2025)
- [Semantic Caching for Low-Cost LLM Serving: From Offline Learning to Online Adaptation](https://mlsanity.com/paper/2508.07675/) (2025)
- [Human-inspired Perspectives: A Survey on AI Long-term Memory](https://mlsanity.com/paper/2411.00489/) (2024)
- [VELO: A Vector Database-Assisted Cloud-Edge Collaborative LLM QoS Optimization Framework](https://mlsanity.com/paper/2406.13399/) (2024)
- [MicroNN: An On-device Disk-resident Updatable Vector Database](https://mlsanity.com/paper/2504.05573/) (2025)
- [Balancing the Blend: An Experimental Analysis of Trade-offs in Hybrid Search](https://mlsanity.com/paper/2508.01405/) (2025)
- [Category-Aware Semantic Caching for Heterogeneous LLM Workloads](https://mlsanity.com/paper/2510.26835/) (2025)
- [Cracking Vector Search Indexes](https://mlsanity.com/paper/2503.01823/) (2025)
- [Asynchronous Verified Semantic Caching for Tiered LLM Architectures](https://mlsanity.com/paper/2602.13165/) (2026)
- [WIP: Large Language Model-Enhanced Smart Tutor for Undergraduate Circuit Analysis](https://mlsanity.com/paper/2506.08962/) (2025)
- [Multimodal Data Storage and Retrieval for Embodied AI: A Survey](https://mlsanity.com/paper/2508.13901/) (2025)
- [REAL: A Reasoning-Enhanced Graph Framework for Long-Term Memory Management of LLMs](https://mlsanity.com/paper/2606.10694/) (2026)
- [Optimizing Domain-Specific Image Retrieval: A Benchmark of FAISS and Annoy with Fine-Tuned Features](https://mlsanity.com/paper/2412.01555/) (2024)
- [Leveraging Approximate Caching for Faster Retrieval-Augmented Generation](https://mlsanity.com/paper/2503.05530/) (2025)
- [ML-Asset Management: Curation, Discovery, and Utilization](https://mlsanity.com/paper/2509.23577/) (2025)
- [Covering the Unseen: Information Demand Coverage Optimization for Retrieval-Augmented Generation](https://mlsanity.com/paper/2606.29328/) (2026)
- [Translation Aligned Sentence Embeddings for Turkish Language](https://mlsanity.com/paper/2311.09748/) (2023)
- [Delta Tensor: Efficient Vector and Tensor Storage in Delta Lake](https://mlsanity.com/paper/2405.03708/) (2024)
- [Scalable k-Means Clustering for Large k via Seeded Approximate Nearest-Neighbor Search](https://mlsanity.com/paper/2502.06163/) (2025)
- [Fast 3D point clouds retrieval for Large-scale 3D Place Recognition](https://mlsanity.com/paper/2502.21067/) (2025)
- [MINT: Multi-Vector Search Index Tuning](https://mlsanity.com/paper/2504.20018/) (2025)
- [Infinity Search: Approximate Vector Search with Projections on q-Metric Spaces](https://mlsanity.com/paper/2506.06557/) (2025)
- [Text2VectorSQL: Towards a Unified Interface for Vector Search and SQL Queries](https://mlsanity.com/paper/2506.23071/) (2025)
- [Optimizing Product Deduplication in E-Commerce with Multimodal Embeddings](https://mlsanity.com/paper/2509.15858/) (2025)
- [Individualized non-uniform quantization for vector search](https://mlsanity.com/paper/2509.18471/) (2025)
- [Trading Vector Data in Vector Databases](https://mlsanity.com/paper/2511.07139/) (2025)
- [Exqutor: Extended Query Optimizer for Vector-augmented Analytical Queries](https://mlsanity.com/paper/2512.09695/) (2025)

## Abstract (arXiv)

There are now over 20 commercial vector database management systems (VDBMSs), all produced within the past five years. But embedding-based retrieval has been studied for over ten years, and similarity search a staggering half century and more. Driving this shift from algorithms to systems are new data intensive applications, notably large language models, that demand vast stores of unstructured data coupled with reliable, secure, fast, and scalable query processing capability. A variety of new data management techniques now exist for addressing these needs, however there is no comprehensive survey to thoroughly review these techniques and systems. We start by identifying five main obstacles to vector data management, namely vagueness of semantic similarity, large size of vectors, high cost of similarity comparison, lack of natural partitioning that can be used for indexing, and difficulty of efficiently answering hybrid queries that require both attributes and vectors. Overcoming these obstacles has led to new approaches to query processing, storage and indexing, and query optimization and execution. For query processing, a variety of similarity scores and query types are now well understood; for storage and indexing, techniques include vector compression, namely quantization, and partitioning based on randomization, learning partitioning, and navigable partitioning; for query optimization and execution, we describe new operators for hybrid queries, as well as techniques for plan enumeration, plan selection, and hardware accelerated execution. These techniques lead to a variety of VDBMSs across a spectrum of design and runtime characteristics, including native systems specialized for vectors and extended systems that incorporate vector capabilities into existing systems. We then discuss benchmarks, and finally we outline research challenges and point the direction for future work.