Qdrant vs Vald Choosing the Right Vector Database for Your AI Apps
What is a Vector Database?
Before we compare Qdrant and Vald, 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 and Vald are purpose-built vector databases. This post compares their vector search capabilities.
Qdrant: Overview and Core Technology
Qdrant is a vector database for similarity search and machine learning. Built from the ground up for vector data, it’s the go to choice for AI developers. Qdrant optimizes performance and can handle high dimensional vector data which is key for many modern ML models.
One of the key strengths of Qdrant is its flexible data modeling. You can 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 on metadata, so you can have more powerful and nuanced search. Qdrant ensures data consistency with ACID compliant transactions even during concurrent operations.
Qdrant’s vector search is at the heart of the platform. It uses a custom version of the HNSW (Hierarchical Navigable Small World) algorithm for indexing which is efficient in high dimensional spaces. The Distance Matrix API allows to calculate efficiently pairwise distances between vectors, so it’s great for tasks like clustering and dimensionality reduction - even with thousands of vectors. For scenarios where precision matters more than speed, Qdrant also supports exact search and provides visual tools to explore vector relationships through the Graph UI.
What’s special about Qdrant is its query and optimization features. Its query language works seamlessly with vector search and supports complex operations including a powerful Facet API to aggregate and count unique values in the data. Memory optimization features like on-disk text and geo indexing allow to handle large scale deployments while keeping performance through intelligent caching. Qdrant has automatic sharding and replication for scalability and supports various data types and query conditions from string matching to numerical ranges and geo-locations. The scalar, product and binary quantization features can reduce memory usage and speed up search, especially for high dimensional vectors.
You can configure the trade off between search precision and performance with both approximate and exact matching depending on your use case. The architecture is designed for real world scenarios where vector search needs to be combined with filtering and aggregation, so it’s great for building practical AI applications.
Vald: Overview and Core Technology
Vald is a powerful tool for searching through huge amounts of vector data really fast. It's built to handle billions of vectors and can easily grow as your needs get bigger. The cool thing about Vald is that it uses a super quick algorithm called NGT to find similar vectors.
One of Vald's best features is how it handles indexing. Usually, when you're building an index, everything has to stop. But Vald is smart - it spreads the index across different machines, so searches can keep happening even while the index is being updated. Plus, Vald automatically backs up your index data, so you don't have to worry about losing everything if something goes wrong.
Vald is great at fitting into different setups. You can customize how data goes in and out, making it work well with gRPC. It's also built to run smoothly in the cloud, so you can easily add more computing power or memory when you need it. Vald spreads your data across multiple machines, which helps it handle huge amounts of information.
Another neat trick Vald has is index replication. It stores copies of each index on different machines. This means if one machine has a problem, your searches can still work fine. Vald automatically balances these copies, so you don't have to worry about it. All of this makes Vald a solid choice for developers who need to search through tons of vector data quickly and reliably.
Key Differences
Search Methodology: Core Algorithms and Features
Qdrant uses custom HNSW (Hierarchical Navigable Small World) algorithm. This is super fast in high dimensional spaces, perfect for semantic search and recommendation systems. Qdrant’s exact search is a bonus for high precision scenarios. And tools like Distance Matrix API allow to do clustering and dimensionality reduction, so it’s great for advanced analytics.
Vald uses NGT (Neighborhood Graph and Tree), fast algorithm for billions of vectors. Continuous indexing is its main feature, so searches can run while the system updates its indexes. This is super useful for dynamic datasets where new vectors are being added all the time.
Both are fast, but Qdrant’s flexibility in combining vector similarity with metadata filtering might be more appealing to users who need more nuanced search.
Data: Flexibility and Payload
Qdrant is flexible with data. You can attach payload to vectors and do complex queries that combine similarity with metadata filtering—geolocation data, numerical ranges or text. It supports multiple data types and advanced filtering conditions which are required for applications that need fine grained control over search results.
Vald is all about scalability and simplicity of data distribution. While it doesn’t have Qdrant’s level of flexibility for structured and semi-structured data, it has robust mechanisms to handle big unstructured datasets.
Scalability and Performance: Large Workloads
Both are designed to scale but the approach is different:
Qdrant uses automatic sharding and replication to ensure performance is balanced as data grows. Memory optimization features like on-disk indexing and caching allows to handle big deployments without sacrificing speed.
Vald is cloud-native and excels in horizontal scaling. It distributes data across multiple machines and replicates indexes to ensure fault tolerance and high availability. The system also balances loads dynamically, so it’s great for distributed environments.
If you have really big datasets Vald’s cloud compatibility might be an advantage. But Qdrant’s memory optimizations and query flexibility is better suited for use cases that need both scalability and complex querying.
Flexibility and customization: Your way
Qdrant has a powerful query language that is integrated with vector search. With Facet API for aggregation and ability to trade off between precision and performance, Qdrant has many customization options.
Vald is all about simplicity of data input and output, especially with its gRPC integrations. Less focused on query customization but can adapt to different setups and manage index replication automatically, so development workflow is simplified.
Integration and Ecosystem
Qdrant integrates with machine learning pipelines and is being adopted in AI driven applications. Its APIs are modern and designed for modern development stacks.
Vald is cloud-native and integrates with Kubernetes. Its focus on distributed systems is perfect for organizations already using cloud infrastructure and microservices architecture.
Usability: Learning Curve and Maintenance
Qdrant has good documentation and community support and focuses on making complex features available to developers. Setup is straightforward even for those new vector databases.
Vald has a similar focus on usability but benefits from its cloud-first design which makes deployment in distributed setup simpler. Its self-managing replication and balancing features reduces the need for manual intervention, which is good for teams with limited operational resources.
Cost
Qdrant: Has a self-hosted version which can help control costs if you have an on-premise infrastructure. But large scale deployments will require additional resources for sharding and replication.
Vald: Costs will depend on your cloud provider and the size of your deployment. Automatic data distribution will reduce operational overhead.
Security
Both have basic security features: authentication and encryption. But Qdrant’s ACID transactions ensures data consistency even during concurrent operations which might be a deal breaker for applications with high data integrity requirements.
When to Choose Qdrant
Qdrant is perfect for applications that need vector search with metadata filtering and complex queries. HNSW algorithm, advanced query language and support for structured, semi-structured and unstructured data makes it great for AI driven use cases like recommendation systems, semantic search and multimodal search. Developers working with hybrid search scenarios - where precision, filtering and aggregation matters - will love ACID compliance, on-disk indexing and Facet API. Also, for teams that care about data consistency and fine grained control over search results.
When to Choose Vald
Vald is better for large scale, cloud native deployments where speed, scalability and distributed performance matters. NGT based indexing, horizontal scaling and ability to handle billions of vectors makes it great for organizations with huge datasets in dynamic environments. Applications with real-time indexing needs like live recommendation engines or IoT data processing will love Vald’s ability to update indexes in real-time without interrupting searches. Built-in fault tolerance and Kubernetes support makes it perfect for teams with a microservices approach.
Summary
Qdrant and Vald are two different beasts. Qdrant is great for hybrid search with advanced querying and flexible data, Vald is great for cloud native, large scale distributed with high availability and speed. The choice depends on your use cases, data types and performance requirements. Evaluate your technical needs carefully to choose the right tool for your needs.
Read this to get an overview of Qdrant and Vald 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
- Vald: 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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