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Apache Cassandra vs MyScale: Choosing the Right Vector Database for Your AI Applications

Apache Cassandra vs MyScale: Choosing the Right Vector Database for Your AI Applications

Sep 06, 20249 min read

Introduction

As AI and machine learning continue to grow, managing and searching large datasets efficiently has become a critical requirement. With AI-driven applications such as natural language processing (NLP) and image search becoming mainstream, vector search and vector databases have emerged as a key technology.

This article compares two popular databases: Apache Cassandra and MyScale. Both offer vector search capabilities but have different strengths. The goal is to help you decide which is better suited for your specific needs.

What is a Vector Database?

Before comparing Apache Cassandra and MyScale, it’s important to understand vector databases and their role in AI-driven applications.

A vector database is built to store and retrieve high-dimensional vector embeddings—numeric representations of unstructured data such as text, images, or videos. These vectors are often generated using deep learning models such as OpenAI's text-embedding-3-large to capture the semantic meaning of complex data.

Instead of searching for exact matches, vector databases enable similarity vector searches, helping systems like recommendation engines suggest products or content similar to what a user has shown interest in. They use algorithms like cosine similarity or Euclidean distance to measure how close two vectors are in a high-dimensional space, making them essential for AI tasks like product recommendations, natural language processing (NLP), anomaly detection, and image analysis.

Vector databases 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.

Both Apache Cassandra and MyScale are traditional databases that have evolved to include vector search capabilities as an add-on.

Overview of Apache Cassandra

Apache Cassandra is an open-source, NoSQL, and distributed database originally designed to handle large amounts of structured data across many servers. It offers strong horizontal scalability and is fault-tolerant by design. This makes it a favorite for enterprises needing to manage large datasets across distributed systems, ensuring high availability and resilience.

Cassandra's core features include its ability to automatically replicate data across multiple data centers, making it reliable even in server failures. It’s also known for its write-optimized architecture, allowing for high-speed data ingestion, which is useful in environments with constant data updates.

Although Cassandra is traditionally more focused on structured data, it has been adapted to support semi-structured and unstructured data types. Vector search functionality is not part of its original design but can be added through extensions or third-party tools like DataStax. This requires extra effort to set up the environment for vector embeddings and similarity searches.

Overview of MyScale

MyScale is a newer database solution built on the open-source ClickHouse database and designed for AI and machine learning workloads. It can handle both structured and vector data and support real-time analytics and machine learning workloads. It focuses heavily on time-series data, vector search, and full-text search, making it ideal for applications that require real-time processing and AI-driven insights.

With native SQL support, MyScale simplifies complex AI-driven queries by integrating vector search, full-text search, and traditional SQL queries in a unified system. This reduces the need for multiple tools and ensures scalability for AI applications.

Key Differences Between Apache Cassandra and MyScale

While both technologies can perform vector search, they approach it from very different angles. Let’s break down the main differences.

Search Methodology

Apache Cassandra relies on third-party solutions or custom-built extensions for its vector search capabilities. These solutions integrate vector processing tools but don’t come with the database itself.

On the other hand, MyScale has built-in support for vector search, including various distance metrics like cosine similarity and Euclidean distance. This native integration makes MyScale more straightforward to use for AI-heavy workloads that need vector search capabilities from the start.

Data Handling

Cassandra was built with structured and semi-structured data in mind, though it can handle unstructured data with some adjustments. Its data model is flexible but requires careful planning, especially when working with unstructured data or vector embeddings.

In contrast, MyScale is designed to handle structured and unstructured data seamlessly, making it more flexible in AI-driven scenarios. Its focus on vector and time-series data makes it easier to implement real-time analytics and AI workloads without extensive customization.

Scalability and Performance

One of Cassandra’s key strengths is its ability to scale horizontally. It can handle massive datasets by distributing data across multiple nodes, and its performance improves as you add more nodes. This scalability is ideal for write-heavy environments like logging systems or social media platforms.

MyScale, while also scalable, is optimized for low-latency, real-time processing. It performs particularly well in read-heavy environments that require quick searches, such as recommendation engines or anomaly detection systems. While Cassandra excels in large-scale distributed systems, MyScale offers better performance for real-time AI-driven workloads.

Flexibility and Customization

Cassandra provides a highly flexible data model, but its vector search capabilities often require custom implementations or external tools. This can be an advantage for developers who want full control over data processing, but it also adds complexity.

In contrast, MyScale offers more built-in flexibility for AI and vector search tasks. You can easily customize the search algorithms, distance metrics, and data models to fit your specific needs without needing extensive third-party integrations.

Integration and Ecosystem

Cassandra integrates well with many tools, including Apache Spark and Hadoop, making it suitable for big data applications. However, its vector search capabilities are not natively integrated, so you’ll likely need additional tools for AI-driven workloads.

MyScale, on the other hand, is built for seamless integration with AI and machine learning frameworks. It supports modern data pipelines and AI tools out of the box, making it easier to develop and deploy AI-driven applications without needing multiple systems to work together.

Ease of Use

Cassandra has a steeper learning curve, especially when it comes to implementing vector search capabilities. Its distributed architecture requires significant setup and management, and handling large datasets with complex queries may involve more manual tuning.

MyScale, by contrast, is designed to be user-friendly. Its native vector search functionality makes it simpler to get started, and its focus on AI-driven use cases reduces the complexity typically associated with setting up machine learning pipelines.

Cost Considerations

Both Cassandra and MyScale offer open-source versions, but their cost profiles can differ significantly based on your use case. Cassandra is known for its ability to run on commodity hardware, which can keep infrastructure costs low, especially when scaling out horizontally. However, adding vector search capabilities through third-party tools or plugins could increase costs. Managed Cassandra services, like those offered by DataSta*, can also add operational expenses.

MyScale, while open-source, also offers managed services for enterprises that need high availability and performance. Real-time workloads with heavy vector search demands may lead to higher operational costs, particularly if low-latency performance is a priority.

Security Features

Cassandra offers comprehensive security features, including encryption, role-based access control, and auditing. These are crucial for enterprises dealing with sensitive data.

MyScale also provides robust security features, focusing on encryption and access control to protect AI-driven searches and vector data. The choice between the two may depend on your specific security needs, but both platforms offer strong foundational features to keep your data secure.

When to Choose Apache Cassandra

Apache Cassandra shines in environments where scalability and high availability are critical. Cassandra is an excellent choice if your application involves managing massive datasets across multiple data centers and you need fault tolerance. It’s ideal for industries like telecommunications or financial services, where data needs to be consistently available even in the event of node failures.

However, if vector search is not a core requirement for your application but more of an add-on feature, Cassandra’s robust distributed architecture might be the better fit. For example, if you’re building a large-scale system to manage customer data and only plan to integrate AI-driven recommendations later, Cassandra offers the reliability and scalability you need.

When to Choose MyScale

MyScale is the better choice for AI-driven applications that rely heavily on vector search and real-time data processing. If your application needs to process large amounts of vector data quickly—whether it’s for product recommendations, NLP-based search engines, or image recognition—MyScale’s native support for vector search will simplify development. Its built-in tools and algorithms are designed for modern AI workloads, making it an ideal solution for teams focused on building intelligent, real-time systems.

Applications like e-commerce platforms or streaming services, which need to deliver personalized content instantly, will benefit from MyScale’s low-latency performance and easy integration with AI frameworks.

When to Choose a Specialized Vector Database?

While both Apache Cassandra and MyScale offer vector search capabilities, they are not optimized for large-scale, high-performance vector search tasks.

If your application relies on fast, accurate similarity searches over millions or billions of high-dimensional vectors, such as image recognition, e-commerce recommendations, or NLP tasks, specialized vector databases like Milvus and Zilliz Cloud (the managed Milvus) are better suited. These databases are built to handle vector data at scale, using advanced Approximate Nearest Neighbor (ANN) algorithms (e.g., HNSW, IVF ) and offering advanced features like hybrid search (including hybrid sparse and dense search, multimodal search, vector search with metadata filtering, and hybrid dense and full-text search), real-time ingestion, and distributed scalability for high-performance in dynamic environments.

On the other hand, general-purpose systems like Apache Cassandra and TiDB are suitable when vector search is not the primary focus, and you’re handling structured or semi-structured data with smaller vector datasets or moderate performance requirements. If you already use these systems and want to avoid the overhead of introducing new infrastructure, vector search plugins can extend their capabilities and provide a cost-effective solution for simpler, lower-scale vector search tasks.

Using Open-source VectorDBBench to Evaluate and Compare Vector Databases on Your Own

VectorDBBench is an open-source benchmarking tool designed for users who require high-performance data storage and retrieval systems, particularly vector databases. This tool allows users to test and compare the performance of different vector database systems such as Milvus and Zilliz Cloud (the managed Milvus) using their own datasets, and determine the most suitable one for their use cases. Using VectorDBBench, users can make informed decisions based on the actual vector database performance rather than relying on marketing claims or anecdotal evidence.

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 Williams
Chloe Williams is a technical writer at Zilliz.

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