SingleStore vs Deep Lake Choosing the Right Vector Database for Your AI Apps
What is a Vector Database?
Before we compare SingleStore and Deep Lake, let's first explore the concept of vector databases.
A vector database is specifically designed to store and query high-dimensional vectors, which are numerical representations of unstructured data. These vectors encode complex information, such as the semantic meaning of text, the visual features of images, or product attributes. By enabling efficient similarity searches, vector databases play a pivotal role in AI applications, allowing for more advanced data analysis and retrieval.
Common use cases for vector databases include e-commerce product recommendations, content discovery platforms, anomaly detection in cybersecurity, medical image analysis, and natural language processing (NLP) tasks. They also play a crucial role in Retrieval Augmented Generation (RAG), a technique that enhances the performance of large language models (LLMs) by providing external knowledge to reduce issues like AI hallucinations.
There are many types of vector databases available in the market, including:
- Purpose-built vector databases such as Milvus, Zilliz Cloud (fully managed Milvus)
- Vector search libraries such as Faiss and Annoy.
- Lightweight vector databases such as Chroma and Milvus Lite.
- Traditional databases with vector search add-ons capable of performing small-scale vector searches.
SingleStore is a distributed, relational, SQL database management system with vector search as an add-on and Deep Lake is a data lake optimized for vector embeddings. This post compares their vector search capabilities.
SingleStore: Overview and Core Technology
SingleStore has made vector search possible by putting it in the database itself, so you don’t need separate vector databases in your tech stack. Vectors can be stored in regular database tables and searched with standard SQL queries. For example, you can search similar product images while filtering by price range or explore document embeddings while limiting results to specific departments. The system supports both semantic search using FLAT, IVF_FLAT, IVF_PQ, IVF_PQFS, HNSW_FLAT, and HNSW_PQ for vector index and dot product and Euclidean distance for similarity matching. This is super useful for applications like recommendation systems, image recognition and AI chatbots where similarity matching is fast.
At its core SingleStore is built for performance and scale. The database distributes the data across multiple nodes so you can handle large scale vector data operations. As your data grows you can just add more nodes and you’re good to go. The query processor can combine vector search with SQL operations so you don’t need to make multiple separate queries. Unlike vector only databases SingleStore gives you these capabilities as part of a full database so you can build AI features without managing multiple systems or dealing with complex data transfers.
For vector indexing SingleStore has two options. The first is exact k-nearest neighbors (kNN) search which finds the exact set of k nearest neighbors for a query vector. But for very large datasets or high concurrency SingleStore also supports Approximate Nearest Neighbor (ANN) search using vector indexing. ANN search can find k near neighbors much faster than exact kNN search sometimes by orders of magnitude. There’s a trade off between speed and accuracy - ANN is faster but may not return the exact set of k nearest neighbors. For applications with billions of vectors that need interactive response times and don’t need absolute precision ANN search is the way to go.
The technical implementation of vector indices in SingleStore has specific requirements. These indices can only be created on columnstore tables and must be created on a single column that stores the vector data. The system currently supports Vector Type(dimensions[, F32]) format, F32 is the only supported element type. This structured approach makes SingleStore great for applications like semantic search using vectors from large language models, retrieval-augmented generation (RAG) for focused text generation and image matching based on vector embeddings. By combining these with traditional database features SingleStore allows developers to build complex AI applications using SQL syntax while maintaining performance and scale.
DeepLake: Overview and Core Technology
Deep Lake is a specialized database built for handling vector and multimedia data—such as images, audio, video, and other unstructured types—widely used in AI and machine learning. It functions as both a data lake and a vector store:
- As a Data Lake: Deep Lake supports the storage and organization of unstructured data (images, audio, videos, text, and formats like NIfTI for medical imaging) in a version-controlled format. This setup enhances performance in deep learning tasks. It enables fast querying and visualization of datasets, making it easier to create high-quality training sets for AI models.
- As a Vector Store: Deep Lake is designed for storing and searching vector embeddings and related metadata (e.g., text, JSON, images). Data can be stored locally, in your cloud environment, or on Deep Lake’s managed storage. It integrates seamlessly with tools like LangChain and LlamaIndex, simplifying the development of Retrieval Augmented Generation (RAG) applications.
Deep Lake uses the Hierarchical Navigable Small World (HNSW) index, based on the Hnswlib package with added optimizations, for Approximate Nearest Neighbor (ANN) search. This allows querying over 35 million embeddings in less than 1 second. Unique features include multi-threading for faster index creation and memory-efficient management to reduce RAM usage.
By default, Deep Lake uses linear embedding search for datasets with up to 100,000 rows. For larger datasets, it switches to ANN to balance accuracy and performance. The API allows users to adjust this threshold as needed.
Although Deep Lake’s index isn't used for combined attribute and vector searches (which currently rely on linear search), upcoming updates will address this limitation to improve its functionality further.
Deep Lake as a Vector Store: Deep Lake provides a robust solution for storing and searching vector embeddings and their associated metadata, including text, JSON, images, audio, and video files. You can store data locally, in your preferred cloud environment, or on Deep Lake's managed storage. Deep Lake also offers seamless integration with tools like LangChain and LlamaIndex, allowing developers to easily build Retrieval Augmented Generation (RAG) applications.
Key Differences
Search Methodology
Both tools support Approximate Nearest Neighbor (ANN) search for fast, large-scale vector queries.
- SingleStore: Offers both exact k-Nearest Neighbor (kNN) search for precision and ANN search for scale, supporting multiple vector indexes (e.g., HNSW_FLAT, IVF_PQ). It combines vector search with SQL operations, which is useful if you need to filter vectors using attributes (like price or tags) alongside similarity scores.
- Deep Lake: Uses an optimized HNSW index for ANN search, achieving impressive performance (querying 35M+ embeddings in under a second). It defaults to linear search for smaller datasets (<100k rows) and switches to ANN as data grows. However, combined attribute and vector searches currently rely on linear search—an area for improvement.
If you’re working with mixed data—like filtering embeddings alongside structured data—SingleStore’s integrated SQL support gives it an edge.
Data Handling
The two systems take different approaches to managing data types:
- SingleStore: Designed as a full-featured relational database that natively supports vectors within columnstore tables. It’s ideal for structured or semi-structured data combined with vector operations, such as product recommendations or semantic search with additional filters.
- Deep Lake: Specializes in managing unstructured data—images, audio, video, and text—alongside vector embeddings. It acts as both a data lake and vector store, making it a strong choice for AI/ML workflows that need versioned, multimedia datasets.
Choose SingleStore for applications requiring structured data with SQL operations. Opt for Deep Lake if your use case focuses on AI/ML tasks with unstructured or multimedia data.
Scalability and Performance
- SingleStore: Built for scalability through distributed nodes, it handles billions of vectors and grows linearly as you add more nodes. ANN indexing enables near-instantaneous response times at scale, balancing speed and accuracy.
- Deep Lake: Handles massive vector datasets efficiently by optimizing memory usage during indexing (e.g., multi-threading for HNSW index creation). However, performance may dip in combined queries involving metadata.
For highly scalable, multi-node performance with structured operations, SingleStore shines. Deep Lake performs best for unstructured AI datasets where vector searches are the main focus.
Flexibility and Customization
- SingleStore: Offers flexibility through SQL queries, supporting a mix of exact and approximate vector search strategies. Developers can leverage the full power of SQL for complex operations.
- Deep Lake: Allows flexibility in embedding storage (local, cloud, or managed storage) and integrates seamlessly with LangChain, LlamaIndex, and deep learning tools.
If SQL-based workflows are central, SingleStore provides a familiar and robust approach. For developers building RAG applications or deep learning pipelines, Deep Lake’s flexibility stands out.
Integration and Ecosystem
- SingleStore: Integrates well into traditional database-driven ecosystems. You can combine vector search with existing relational data workflows, enabling applications like hybrid search (vectors + attributes).
- Deep Lake: Tailored for AI/ML ecosystems. Its integrations with LangChain, LlamaIndex, and model training pipelines make it ideal for developers building AI applications like Retrieval-Augmented Generation (RAG).
Choose SingleStore if your project requires a general-purpose database with vector capabilities. Deep Lake fits better in specialized AI/ML ecosystems.
Ease of Use
- SingleStore: Setting up vector indexes requires some familiarity with database schemas (e.g., columnstore tables) and vector-specific syntax. However, developers comfortable with SQL will find it intuitive.
- Deep Lake: Offers a simpler onboarding experience for AI developers, especially those using Python-based tools. The API is straightforward, but combining metadata filters with vector search requires additional effort.
Cost Considerations
- SingleStore: Operational costs depend on database size, query complexity, and node scaling. SingleStore’s value comes from its dual role as a vector store and relational database.
- Deep Lake: Offers flexible pricing for its managed storage. Costs vary based on where you store your data (local, cloud, or Deep Lake’s service).
Security Features
- SingleStore: Includes robust security features such as encryption, authentication, and role-based access control—standard for enterprise databases.
- Deep Lake: Provides essential security features but focuses more on developer flexibility and performance.
When to Choose SingleStore
SingleStore is the best when you have large distributed data and vector search, especially when you need to mix structured data queries with vector similarity searches. Its SQL integration lets you do hybrid queries—filter vector embeddings by attributes like price, category or tags—without adding complexity to the stack. Applications like recommendation systems, semantic search and AI powered chat systems benefit from SingleStore’s ability to do exact kNN and approximate ANN searches at scale. If performance, scalability and consolidating vector search into a full featured relational database is key, SingleStore is the way to go.
When to Choose Deep Lake
Deep Lake is best for AI/ML workflows where unstructured data—images, audio, video, text—plays a big role. Its ability to be both a data lake and vector store makes it great for building and managing high quality, version controlled datasets for training machine learning models. Developers working on Retrieval-Augmented Generation (RAG) applications, embedding search for multimedia data or large scale deep learning projects will benefit from Deep Lake’s integration with tools like LangChain and LlamaIndex. For projects where vector search is focused on AI use cases rather than hybrid SQL operations, Deep Lake is a more streamlined and flexible solution.
Conclusion
SingleStore and Deep Lake both have vector search but serve different purposes. SingleStore is great for structured or hybrid data applications where you need to combine SQL based operations with scalable vector search. Deep Lake shines in AI/ML environments where unstructured data and multimedia embeddings are the focus, with optimized performance and integrations for modern deep learning pipelines. The choice is yours: for structured, distributed data with SQL support, go with SingleStore. For AI driven, unstructured data tasks, Deep Lake is the winner.
Read this to get an overview of SingleStore and Deep Lake but to evaluate these you need to evaluate based on your use case. One tool that can help with that is VectorDBBench, an open-source benchmarking tool for vector database comparison. In the end, thorough benchmarking with your own datasets and query patterns will be key to making a decision between these two powerful but different approaches to vector search in distributed database systems.
Using Open-source VectorDBBench to Evaluate and Compare Vector Databases on Your Own
VectorDBBench is an open-source benchmarking tool for users who need high-performance data storage and retrieval systems, especially vector databases. This tool allows users to test and compare different vector database systems like Milvus and Zilliz Cloud (the managed Milvus) using their own datasets and find the one that fits their use cases. With VectorDBBench, users can make decisions based on actual vector database performance rather than marketing claims or hearsay.
VectorDBBench is written in Python and licensed under the MIT open-source license, meaning anyone can freely use, modify, and distribute it. The tool is actively maintained by a community of developers committed to improving its features and performance.
Download VectorDBBench from its GitHub repository to reproduce our benchmark results or obtain performance results on your own datasets.
Take a quick look at the performance of mainstream vector databases on the VectorDBBench Leaderboard.
Read the following blogs to learn more about vector database evaluation.
Further Resources about VectorDB, GenAI, and ML
Chloe WilliamsChloe Williams is a technical writer at Zilliz.
- What is a Vector Database?
- SingleStore: Overview and Core Technology
- DeepLake: Overview and Core Technology
- Key Differences
- Using Open-source VectorDBBench to Evaluate and Compare Vector Databases on Your Own
- Further Resources about VectorDB, GenAI, and ML
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