Qdrant vs Aerospike Choosing the Right Vector Database for Your AI Apps
What is a Vector Database?
Before we compare Qdrant 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.
Qdrant 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.
Qdrant: Overview and Core Technology
Qdrant is a vector database built specifically for similarity search and machine learning applications. It's designed from the ground up to handle vector data efficiently, making it a top choice for developers working on AI-driven projects. Qdrant excels in performance optimization and can work with high-dimensional vector data, which is crucial for many modern machine learning models.
One of Qdrant's key strengths is its flexible data modeling. It allows you to store and index not just vectors, but also payload data associated with each vector. This means you can run complex queries that combine vector similarity with filtering based on metadata, enabling more powerful and nuanced search capabilities. Qdrant ensures data consistency with ACID-compliant transactions, even during concurrent operations.
Qdrant's vector search capabilities are a core part of its architecture. It uses a custom version of the HNSW (Hierarchical Navigable Small World) algorithm for indexing, known for its efficiency in high-dimensional spaces. This allows for fast approximate nearest neighbor search, which is essential for many AI applications. For scenarios where precision trumps speed, Qdrant also supports exact search methods.
What sets Qdrant apart is its query language and API design. It offers a rich set of filtering and query options that work seamlessly with vector search, allowing for complex, multi-stage queries. This makes it particularly good for applications that need to perform semantic search alongside traditional filtering. Qdrant also includes features like automatic sharding and replication to help you scale as your data and query load grow. It supports a variety of data types and query conditions, including string matching, numerical ranges, and geo-locations. Qdrant's scalar, product, and binary quantization features can significantly reduce memory usage and boost search performance, especially for high-dimensional vectors.
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
Search Methodology
Qdrant: Built for vector similarity search, Qdrant uses an optimized version of Hierarchical Navigable Small World (HNSW) algorithm. This allows for efficient approximate nearest neighbor (ANN) search for high-dimensional vector data. It also supports exact search for applications where precision over speed matters.
Aerospike: Aerospike’s Vector Search (AVS) uses HNSW for ANN search. But it’s still in Preview and query ecosystem is evolving. AVS uses concurrent, distributed indexing across nodes and leverages advanced CPU for scalability.
Key Takeaway: Both use HNSW for vector search, but Qdrant has a more mature and specialized implementation, while Aerospike’s is newer and in early access.
Data Handling
Qdrant: Stores vectors and associated payload data. You can run combined vector similarity and metadata-based filtering queries, such as searching for a vector and filtering by numerical, textual or geo-locational conditions. Qdrant is ACID compliant and provides reliable concurrent operations.
Aerospike: As a NoSQL database, Aerospike handles structured and semi-structured data well. AVS allows to associate vectors with records and each record can have multiple vectors. But Aerospike’s metadata filtering for vector records is less mature than Qdrant.
Key Takeaway: Qdrant is designed to combine vector and payload data queries seamlessly. Aerospike is catching up but not as feature rich for metadata filtering yet.
Scalability and Performance
Qdrant: Supports automatic sharding and replication for large datasets. Performance optimization includes quantization techniques like scalar and binary quantization to reduce memory usage while maintaining search speed.
Aerospike: Designed for high-performance real-time applications, Aerospike scales horizontally. AVS’s indexing uses concurrent processing across nodes and advanced CPU for scalability. But ingestion performance depends on memory and storage configuration.
Key Takeaway: Aerospike’s infrastructure is great for horizontal scaling but Qdrant’s optimizations for vector search makes it better for applications that prioritize search performance and efficiency.
Flexibility and Customization
Qdrant: Has a robust query language and API to run multi-stage queries combining vector similarity with advanced filtering. Supports flexible data types and queries, very customizable for various AI-driven use cases.
Aerospike: Aerospike supports complex record structures but vector search is less flexible at this stage, focused on foundation functionality.
Key Takeaway: Qdrant has more options for AI/ML workflows.
Integration and Ecosystem
Qdrant: Integrates with machine learning frameworks, perfect for AI projects. API is designed for developer usability.
Aerospike: Already in high-performance applications, Aerospike’s ecosystem supports integrations with other tools. But AVS specific integrations are limited in Preview.
Key Takeaway: Qdrant is for developers embedding AI/ML into their workflow, Aerospike is for teams already using its core database features.
Usability
Qdrant: Has clear documentation and developer friendly interface. Focus on vector databases reduces the learning curve for teams focused on similarity search.
Aerospike: Steeper learning curve, especially for new users, since it’s a NoSQL database and AVS is in Preview.
Key Takeaway: Qdrant is easier to use for vector database tasks, Aerospike might take more time to set up and explore.
Pricing
Qdrant: Open-source, managed service available. Costs are based on storage, compute resources and optional managed features.
Aerospike: Known for cost-effective performance in high-throughput scenarios but AVS might add overhead depending on ingestion and query. Managed service costs will vary based on deployment complexity.
Key Takeaway: Qdrant has a more transparent cost model for vector specific use cases, Aerospike might be more cost effective for broader use cases.
Security
Qdrant: Has security features like encryption and access control in its managed offerings. Open-source deployments will require additional configuration.
Aerospike: Built for enterprise applications, Aerospike has robust authentication, encryption and access control,and will extend to AVS as it matures.
Key Takeaway: Aerospike’s security is enterprise-ready, but Qdrant covers essential security needs for most AI/ML applications.
When to use Qdrant
Qdrant is for projects where vector similarity search is a key requirement, especially for AI and machine learning applications. It’s great for high dimensional vector embedding data and combining vector search with metadata filtering. If you’re building a semantic search engine, recommendation system or other AI tools that require precise and fine grained querying then Qdrant’s features and APIs are a natural fit. It’s also good for companies that expect rapid data growth in their AI workflows.
When to use Aerospike
Aerospike is for high throughput, real-time applications where vector search is an add-on rather than a core feature. It’s great for teams already using Aerospike’s core NoSQL capabilities and want to add similarity search to their database. For example, use cases like real-time fraud detection, personalization or session management where structured or semi-structured data is key, then Aerospike is a good fit. The enterprise grade scalability and security features make it suitable for large companies with complex distributed systems.
Summary
Qdrant and Aerospike are different despite having similar vector search functionality. Qdrant is great for AI workloads and scenarios where vector search is core, it’s flexible, easy to use and has rich integrations. Aerospike is great for high throughput environments so it’s a good choice for real-time, enterprise grade applications where vector search is an add-on. The choice depends on your project’s use case, data and scalability requirements and how these technologies fit into your long term plans.
Read this to get an overview of Qdrant 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?
- Qdrant: 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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