Zilliz Cloud vs Deep Lake Choosing the Right Vector Database for Your AI Apps
What is a Vector Database?
Before we compare Zilliz Cloud 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.
Zilliz Cloud is a purpose-built vector database. Deep Lake is a data lake optimized for vector embeddings with vector search capabilities as an add-on. This post compares their vector search capabilities.
Zilliz Cloud: Overview and Core Technology
Zilliz Cloud is a fully managed vector database service built on top of the open-source Milvus engine. It helps developers and organizations to handle large scale AI applications by storing, managing and searching vector embeddings efficiently. It takes care of infrastructure for you, so you can focus on building AI features instead of managing databases.
One of the key advantages of Zilliz Cloud is the automatic performance optimization. The system has AutoIndex technology which will choose the best indexing method for your data and use case. So you don’t have to spend time tuning parameters or comparing different index types. The platform also uses IVF (Inverted File) and graph-based techniques to speed up similarity search across large datasets.
The platform has enterprise features. You can deploy your vector databases across AWS, Azure or Google Cloud, with options to use Zilliz’s fully managed service or bring your own cloud account (BYOC). For organizations that handle sensitive data, Zilliz Cloud has security controls like encryption, access management and compliance tools. The system also supports different consistency levels so you can balance between fast updates and strong data consistency based on your needs.
Cost management is another important aspect of Zilliz Cloud. The platform uses tiered storage to automatically move less accessed data to cheaper storage options, so you can reduce cost without affecting performance. You can also choose compute resources that match your workload - for example, use more powerful instances for heavy processing tasks and lighter ones for simple queries. This flexibility helps you to optimize your spending while maintaining good performance.
For AI applications that need to search different types of data together, Zilliz Cloud supports hybrid search. You can search across text embeddings, image vectors and other data types in a single query. The platform also supports various similarity metrics like Cosine, Euclidean and Inner Product so it’s suitable for different machine learning models and use cases. As your data grows, the system can scale horizontally by adding more resources automatically so you can maintain good performance even under heavy workload.
Deep Lake: 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
Zilliz Cloud: Powered by Milvus, Zilliz Cloud uses Inverted File (IVF) and graph-based methods. It optimizes similarity search, it’s fast and efficient on large datasets. AutoIndex automatically selects the best indexing strategy for your data, so you don’t have to guess.
Deep Lake: Uses Hierarchical Navigable Small World (HNSW) algorithm for Approximate Nearest Neighbor (ANN) search, sub-second query time on massive datasets. Linear search is good for smaller datasets, it seamlessly switches to ANN for larger data, balance speed and accuracy. But attribute-based filtering with vectors is limited to linear methods for now.
Data Handling
Zilliz Cloud: Focuses on vector embeddings and supports hybrid search across text and image data. Optimized for AI-driven applications, supports various similarity metrics (Cosine, Euclidean, Inner Product). Tiered storage for cold data.
Deep Lake: Good at multimedia data, supports unstructured data types like images, audio, video. Also a data lake, with version control and dataset visualization tools. So good for applications that need rich metadata and multiple data formats.
Scalability and Performance
Zilliz Cloud: Horizontal scalable. Can auto-scale resources to handle growing data and workload. Performance is built-in, with AutoIndex and tiered storage that adjust to changing demands.
Deep Lake: Optimized for large scale AI workloads, HNSW can query tens of millions of embeddings with low latency. But scalability features for dynamic workloads are less automated than Zilliz Cloud.
Flexibility and Customization
Zilliz Cloud: Flexible indexing and query customization for various AI and machine learning use cases. Also supports BYOC (Bring Your Own Cloud) model, so you have control over your infrastructure.
Deep Lake: Gives developers full control when working with multimedia data and custom datasets. But its vector search capabilities, especially for hybrid queries, are less flexible than Zilliz Cloud.
Integration and Ecosystem
Zilliz Cloud: Integrates with various machine learning frameworks and tools. Good for cloud-native workflow, supports AWS, Azure, GCP.
Deep Lake: Integrates with LangChain and LlamaIndex, so good for Retrieval-Augmented Generation (RAG) tasks. Supports local, cloud, managed storage.
Ease of Use
Zilliz Cloud: Managed service model with AutoIndex makes it easy to use. Developers can focus on their application rather than database.
Deep Lake: Interface is simple, but as a data lake and vector store, it requires more initial setup especially for users who are not familiar with multimedia dataset handling.
Cost
Zilliz Cloud: Tiered storage and resource allocation to optimize cost. Pay-as-you-go, so you only pay for what you use, good for dynamic workload.
Deep Lake: Supports storage options (local, cloud, or managed) but costs can increase if large multimedia data is stored and queried frequently.
Security
Zilliz Cloud: Has full security controls, encryption, access management, compliance tools so good for enterprise with high security requirements.
Deep Lake: Secure storage but less enterprise-level security features than Zilliz Cloud.
When to use Zilliz Cloud
Zilliz Cloud is for applications that require large scale distributed data management and efficient vector search. With managed service model and advanced indexing techniques using IVF and graph-based algorithms, it’s a good fit for organizations that have massive datasets where performance, scalability and ease of use matters. If your use case involves hybrid search - combining vectors with structured or semi-structured data types or requires robust security and cost management features, Zilliz Cloud has the tools to simplify deployment and maintain high performance.
When to use Deep Lake
Deep Lake is for scenarios around multimedia datasets and deep learning workflows. As a vector store and a version-controlled data lake, it’s a good fit for projects involving unstructured data like images, audio and video. For developers building Retrieval-Augmented Generation (RAG) systems or dealing with rich metadata, Deep Lake has seamless integration with tools like LangChain and LlamaIndex to boost productivity. Its strengths are in dataset visualization and managing AI focused data pipelines.
Summary
Zilliz Cloud and Deep Lake are both powerful, but for different use cases. Zilliz Cloud is for large scale distributed vector data with enterprise level security and hybrid search, Deep Lake is for multimedia rich AI applications with data versioning and integration with machine learning tools. Choose based on your use case, data types and performance requirements so the technology aligns with your development goals.
Read this to get an overview of Zilliz Cloud 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?
- Zilliz Cloud: Overview and Core Technology
- Deep Lake: 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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