Zilliz Cloud vs Aerospike Choosing the Right Vector Database for Your AI Apps
What is a Vector Database?
Before we compare Zilliz Cloud and Aerospike, 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. Aerospike is a distributed, scalable NoSQL database 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.
Aerospike: Overview and Core Technology
Aerospike is a NoSQL database for high-performance real-time applications. It has added support for vector indexing and searching so it’s suitable for vector database use cases. The vector capability is called Aerospike Vector Search (AVS) and is in Preview. You can request early access from Aerospike.
AVS only supports Hierarchical Navigable Small World (HNSW) indexes for vector search. When updates or inserts are made in AVS, record data including the vector is written to the Aerospike Database (ASDB) and is immediately visible. For indexing, each record must have at least one vector in the specified vector field of an index. You can have multiple vectors and indexes for a single record, so you can search for the same data in different ways. Aerospike recommends assigning upserted records to a specific set so you can monitor and operate on them.
AVS has a unique way of building the index, it’s concurrent across all AVS nodes. While vector record updates are written directly to ASDB, index records are processed asynchronously from an indexing queue. This is done in batches and distributed across all AVS nodes, so it uses all the CPU cores in the AVS cluster and is scalable. Ingestion performance is highly dependent on host memory and storage layer configuration.
For each item in the indexing queue, AVS processes the vector for indexing, builds the clusters for each vector and commits those to ASDB. An index record contains a copy of the vector itself and the clusters for that vector at a given layer of the HNSW graph. Indexing uses vector extensions (AVX) for single instruction, multiple data parallel processing.
AVS queries during ingestion to “pre-hydrate” the index cache because records in the clusters are interconnected. These queries are not counted as query requests but show up as reads against the storage layer. This way, the cache is populated with relevant data and can improve query performance. This shows how AVS handles vector data and builds indexes for similarity search so it can scale for high-dimensional vector searches.
Key Differences
Vector search has become a fundamental technology for recommendation systems, semantic search and AI powered analytics. Choosing the right tool for your vector database needs requires understanding how different platforms handle vector indexing, data management and operational scalability. Here’s a comparison of Zilliz Cloud and Aerospike Vector Search (AVS) to help you decide.
Search Methodology
Zilliz Cloud: Zilliz Cloud is built on Milvus engine which uses multiple indexing methods like IVF (Inverted File System) and graph-based. Its AutoIndex feature automatically selects the best indexing method for your data, so you don’t have to manually tune it. This makes Zilliz suitable for various machine learning and AI use cases.
Aerospike: Aerospike’s vector search is powered by Hierarchical Navigable Small World (HNSW) indexing which is known for its performance in high-dimensional similarity search. But AVS is still in preview and only supports HNSW, no auto index selection. Developers have to configure and manage index parameters manually.
Data
Zilliz Cloud: Zilliz is great with multimodal data. It supports hybrid search, you can query text, image and numerical vectors in one operation. The system also supports different consistency levels so you can balance between fast updates and strong data consistency based on your needs.
Aerospike: Aerospike has good support for structured and semi-structured data, since it’s a NoSQL database. But vector search in Aerospike is more rigid, it’s designed for structured workflow where each record has pre-defined vector fields. Hybrid search is not mentioned.
Scalability and Performance
Zilliz Cloud: Zilliz has horizontal scalability, you can scale as your data grows. Its distributed architecture ensures consistent performance under heavy load. Tiered storage further optimizes performance by offloading less frequently accessed data to cheaper storage tiers.
Aerospike: Aerospike’s concurrent indexing is designed for scalability. Index records are processed asynchronously and distributed across nodes, using all CPU cores in the cluster. But scalability is heavily dependent on system memory and storage configuration which can add complexity in scaling.
Flexibility and Customization
Zilliz Cloud: The platform has flexibility in data modeling and supports multiple similarity metrics like cosine, Euclidean and inner product. This makes Zilliz suitable for various AI and machine learning use cases without much customization.
Aerospike: Aerospike supports multiple vectors per record and indexing strategies for different query paths. While this is useful, it requires manual configuration and doesn’t have Zilliz’s auto-tuning features.
Integration and Ecosystem
Zilliz Cloud: Zilliz sits atop major cloud providers like AWS, Azure and Google Cloud. Developers can choose between fully managed services or BYOC (Bring Your Own Cloud) model. It also integrates well with AI frameworks so it’s suitable for end-to-end machine learning workflows.
Aerospike: Aerospike is part of a broader NoSQL ecosystem so it’s a good choice for applications that combine real-time analytics with traditional database capabilities. But its vector search is relatively standalone and still in preview.
Ease of Use
Zilliz Cloud: Zilliz makes setup easy with managed services and features like AutoIndex. Developers can focus on building applications without worrying about infrastructure or parameter tuning. Documentation is comprehensive and the platform is easy to use.
Aerospike: Aerospike’s steep learning curve is due to its technical complexity and manual configuration. Setting up and optimizing AVS requires deep understanding of the system architecture, especially for vector data workflows.
Cost
Zilliz Cloud: Zilliz has cost saving features like tiered storage and workload specific compute. These features allow you to optimize cost based on your usage patterns.
Aerospike: Aerospike doesn’t have detailed cost management for AVS. Cost is likely tied to resource intensive configuration like memory and storage, which will increase as you scale.
Security Features
Zilliz Cloud: Zilliz has robust security features like encryption, access management and enterprise standards compliance. This makes it a good choice for organizations that handle sensitive data.
Aerospike: Aerospike has encryption and authentication but lacks detailed documentation on advanced security features for AVS. This might be a limitation for enterprises with high security requirements.
When to Use Zilliz Cloud
Zilliz Cloud is for applications that have large scale distributed data and vector search as a core feature. It excels in AI and machine learning workflows that require efficient handling of vector embeddings, hybrid search across multimodal data and scalability. Automatic indexing, hybrid search and support for multiple similarity metrics makes it a good fit for recommendation engines, semantic search and real-time analytics. And managed services and ease of use are good for teams that care about fast deployment and low operational overhead.
When to Use Aerospike
Aerospike is for organizations that already use its NoSQL capabilities and need vector search as an add-on. Applications that require real-time transactional processing along with vector similarity search will benefit from Aerospike’s indexing and low-latency architecture. It’s good when vector search can be combined with structured data operations like inventory management or financial transactions. But its preview-stage vector search requires deeper understanding of its architecture and configuration so it’s more suitable for technical teams.
Conclusion
Both Zilliz Cloud and Aerospike have their own strengths. Zilliz Cloud is for developers who want an easy to use, scalable and feature rich vector search for AI applications. Aerospike is for environments where vector search supplements robust real-time transactional systems. Choose between the two based on your data types, scalability, integration and operational complexity. In the end it depends on whether you want a mature vector search tool or a database with emerging vector capabilities.
Read this to get an overview of Zilliz Cloud and Aerospike 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
- Aerospike: 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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