Apache Cassandra vs Weaviate: Choosing the Right Vector Database for Your AI Apps
Introduction
As artificial intelligence continues to redefine this data-driven world, the need for robust vector databases that can handle complex data structures like vector embeddings is becoming increasingly evident. This blog will introduce and compare two notable databases: Apache Cassandra and Deep Lake. Each offers distinctive approaches to handling vector embeddings essential for AI applications.
What is a Vector Database?
Before we compare Apache Cassandra vs Weaviate, 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 text's semantic meaning, images' visual features, or product attributes using machine learning models. By enabling efficient similarity searches, vector databases play a pivotal role in AI applications, allowing for more advanced data analysis and retrieval.
Vector databases have been adopted in many use cases, including 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 like Apache Cassandra
Understanding Apache Cassandra
Apache Cassandra is an open-source, distributed NoSQL database system designed to handle massive amounts of data across many servers with no single point of failure. It was originally developed to efficiently handle large amounts of structured and semi-structured data across many nodes. Cassandra is known for its high scalability, fault tolerance, and ability to operate in distributed environments with minimal downtime or performance degradation.
With the release of Cassandra 5.0, Apache Cassandra is evolving beyond its core functionality as a NoSQL database to support vector embeddings and vector search. Cassandra's vector search functionality is built on its existing architecture. It allows users to store vector embeddings alongside other data and perform similarity searches. This integration enables Cassandra to support AI-driven applications while maintaining its strengths in handling large-scale, distributed data.
A key component of Cassandra's vector search is Storage-Attached Indexes (SAI). SAI is a highly scalable and globally distributed index that adds column-level indexes to any vector data type column. It provides unparalleled I/O throughput for databases using Vector Search and other search indexing. SAI offers extensive indexing functionality, capable of indexing both queries and content (including large inputs like documents, words, and images) to capture semantics.
Vector Search is the first instance of validating SAI's extensibility, leveraging its new modularity. This Vector Search and SAI combination enhances Cassandra's capabilities in handling AI and machine learning workloads, making it a strong contender in the vector database space.
Weaviate: Overview and Core Technology
Weaviate is an open-source vector database designed to simplify AI application development. It offers built-in vector and hybrid search capabilities, easy integration with machine learning models, and a focus on data privacy. These features aim to help developers of various skill levels create, iterate, and scale AI applications more efficiently.
One of Weaviate's strengths is its fast and accurate similarity search. It uses HNSW (Hierarchical Navigable Small World) indexing to enable vector search on large datasets. Weaviate also supports combining vector searches with traditional filters, allowing for powerful hybrid queries that leverage both semantic similarity and specific data attributes.
Key features of Weaviate include:
- PQ compression for efficient storage and retrieval
- Hybrid search with an alpha parameter for tuning between BM25 and vector search
- Built-in plugins for embeddings and reranking, which ease development
Weaviate is an entry point for developers to try out vector search. It offers a developer-friendly approach with a simple setup and well-documented APIs. Deep integration with the GenAI ecosystem makes it suitable for small projects or proof-of-concept work. The target audience for Weaviate are software engineers building AI applications, data engineers working with large datasets and data scientists deploying machine learning models. Weaviate simplifies semantic search, recommendation systems, content classification and other AI features.
Weaviate is designed to scale horizontally so it can handle large datasets and high query loads by distributing data across multiple nodes in a cluster. It supports multi-modal data, works with various data types (text, images, audio, video) depending on the vectorization modules used. Weaviate provides both RESTful and GraphQL APIs for flexibility in how developers interact with the database.
However, for large-scale production environments, there are several considerations to keep in mind:
- Limited enterprise-grade security features
- Potential scalability challenges with multi-billion vector datasets
- Manual management required for newly released tiered storage options
- Horizontal scale-up requires assistance from Weaviate engineers and cannot be done automatically
This last point is particularly noteworthy, as it means organizations need to plan ahead and allocate time for scaling operations, ensuring they don't approach their system limits without proper preparation.
Key Differences
Choosing between Apache Cassandra and Weaviate for vector search depends on your needs. Here’s a breakdown of the differences:
Search Methodology
Apache Cassandra: Cassandra’s vector search is built on top of its existing architecture and uses Storage-Attached Indexes (SAI). This means column-level indexing for vector data and similarity search directly in the database. Good for users who want to manage vector embeddings alongside structured and semi-structured data and do hybrid queries.
Weaviate: Weaviate uses the HNSW (Hierarchical Navigable Small World) algorithm for vector similarity search. This is optimized for fast and accurate search on large datasets. Hybrid search combines semantic similarity with traditional filters and gives fine grained results.
Takeaway: Choose Cassandra if you need to have vector and traditional database features combined. Choose Weaviate if you prioritize advanced similarity search with hybrid options.
Data
Apache Cassandra: Designed for massive amounts of structured and semi-structured data, Cassandra treats vector embeddings as an extension of its robust distributed system. It’s fault tolerant and highly available.
Weaviate: Supports multi-modal data (text, images, audio, video) and is good for applications that need flexibility in handling unstructured data types. Modular vectorization plugins make it easy to integrate with various data sources.
Takeaway: Cassandra is good for structured data and hybrid use cases, Weaviate is good for unstructured, multi-modal data in AI driven applications.
Scalability and Performance
Apache Cassandra: Linear scalability, can scale horizontally across many nodes without performance impact. Distributed architecture is good for large scale production environments.
Weaviate: Weaviate supports horizontal scaling but handling multi-billion vector datasets often requires manual management and planning. This scaling process may require Weaviate engineers to assist.
Takeaway: For seamless scaling in large production systems Cassandra has a clear win. Weaviate is better for smaller projects or projects that can afford manual scaling interventions.
Flexibility and Customization
Apache Cassandra: Flexible schema design, good for many workloads. But vector search is relatively new and not as customizable as specialized vector databases.
Weaviate: Developer friendly APIs (RESTful and GraphQL), easy to customize queries. Built-in plugins for embeddings and reranking for AI use cases.
Takeaway: Weaviate has more out of the box customization for AI and machine learning use cases, Cassandra is better for general data management.
Integration and Ecosystem
Apache Cassandra: Well established in the NoSQL space, integrates with many tools and frameworks for data engineering, analytics and vector search.
Weaviate: Part of the GenAI ecosystem, Weaviate has seamless connections to ML frameworks making it easy to build AI applications.
Takeaway: For traditional enterprise ecosystems Cassandra has more integrations. Weaviate is better for AI first development workflows.
Usability
Apache Cassandra: Powerful but has a steeper learning curve for new users, especially those not familiar with distributed systems. Documentation is comprehensive but technical.
Weaviate: Weaviate is designed to be easy to use, simple setup, intuitive APIs, well organized documentation.
Takeaway: New to vector search Weaviate is more accessible, experienced with distributed databases might prefer Cassandra.
Cost
Apache Cassandra: Requires significant resources for self-hosted environments. Managed services like AstraDB can reduce operational overhead but may increase costs.
Weaviate: Lower entry cost for small projects but higher operational cost as datasets grow, especially if manual scaling is needed.
Takeaway: For cost predictability and large scale deployments Cassandra is better. Weaviate is good for small scale or experimental projects.
Security
Apache Cassandra: Enterprise grade security: encryption, RBAC, authentication
Weaviate: Lags behind in enterprise grade security, not suitable for highly regulated environments.
When to Choose Apache Cassandra
Apache Cassandra is for large scale distributed data workloads that require high availability and fault tolerance. With recent vector search capabilities powered by Storage-Attached Indexes (SAI) it’s perfect for applications that combine vector embeddings with structured or semi-structured data. If you need to do hybrid queries, low latency across a distributed system or production environments with strict security and scalability requirements, Cassandra has a proven solution. Linear scalability and robust architecture for enterprise grade systems with billions of data points.
When to Choose Weaviate
Weaviate is for developers building AI-first applications that rely heavily on semantic search, recommendation systems or multi-modal data. Native HNSW indexing for fast and accurate similarity search, hybrid search and built-in embedding plugins to integrate with machine learning workflows. Weaviate’s developer friendly APIs and modular design makes prototyping and deployment for small to medium sized datasets easy. Perfect for teams focused on AI innovation where ease of use, flexible queries and integration with GenAI tools are more important than large scale distribution.
Conclusion
Apache Cassandra is good at scale, security and handling diverse workloads so it’s a great choice for enterprise scale applications. Weaviate is good at simplicity, AI application development and semantic search so it’s great for small to mid sized projects. Ultimately it’s up to your use cases, data types and performance requirements. Choose Cassandra if you need a robust distributed system for hybrid workloads or Weaviate if you need AI driven features and ease of use for unstructured or multi-modal data.
Read this to get an overview of Apache Cassandra and Weaviate 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.
- Introduction
- What is a Vector Database?
- Understanding Apache Cassandra
- Weaviate: 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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