Chroma vs Aerospike: Choosing the Right Vector Database for Your Needs
As AI and data-driven technologies advance, selecting an appropriate vector database for your application is becoming increasingly important. Chroma and Aerospike are two options in this space. This article compares these technologies to help you make an informed decision for your project.
What is a Vector Database?
Before we compare Chroma 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 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 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.
Meta Description: Chroma is a vector database and Aerospike is a distributed, scalable NoSQL database. Both have vector search capabilities as an add-on. This post compares their vector search capabilities.
What is Chroma? An Overview
Chroma is an open-source, AI-native vector database that simplifies the process of building AI applications. It acts as a bridge between large language models (LLMs) and the data they require to function effectively. Chroma's main objective is to make knowledge, facts, and skills easily accessible to LLMs, thereby streamlining the development of AI-powered applications. At its core, Chroma provides tools for managing vector data, allowing developers to store embeddings (vector representations of data) along with their associated metadata. This capability is crucial for many AI applications, as it enables efficient similarity searches and data retrieval based on vector relationships.
One of Chroma's key strengths is its focus on simplicity and developer productivity. The team behind Chroma has prioritized creating an intuitive interface that allows developers to quickly integrate vector search capabilities into their applications. This emphasis on ease of use doesn't come at the cost of performance. Chroma is designed to be fast and efficient, making it suitable for a wide range of applications. It operates as a server and offers first-party client SDKs for both Python and JavaScript/TypeScript, providing flexibility for developers to work in their preferred programming environment.
Chroma's functionality revolves around the concept of collections, which are groups of related embeddings. When adding documents to a Chroma collection, the system can automatically tokenize and embed them using a specified embedding function, or a default one if not provided. This process transforms raw data into vector representations that can be efficiently searched. Along with the embeddings, Chroma allows storage of metadata for each document, which can include additional information useful for filtering or organizing data. Chroma provides flexible querying options, allowing searches for similar documents using either vector embeddings or text queries, returning the closest matches based on vector similarity.
Chroma stands out in several ways. Its API is designed to be intuitive and easy to use, reducing the learning curve for developers new to vector databases. It supports various types of data and can work with different embedding models, allowing users to choose the best approach for their specific use case. Chroma is built to integrate seamlessly with other AI tools and frameworks, making it a good fit for complex AI pipelines. Additionally, Chroma's open-source nature (licensed under Apache 2.0) provides transparency and the potential for community-driven improvements and customizations. The Chroma team is actively working on enhancements, including plans for a managed service (Hosted Chroma) and various tooling improvements, indicating a commitment to ongoing development and support.
What is Aerospike? An Overview
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 Difffrences
When building AI applications that need vector search capabilities, you'll likely consider Chroma and Aerospike as potential options. This comparison will help you understand their key differences and choose the right tool for your needs.
Search Methodology
Chroma offers flexible search options with support for multiple embedding models and search methods. You can search using either vector embeddings or text queries, making it adaptable for different use cases.
Aerospike Vector Search (AVS) uses the Hierarchical Navigable Small World (HNSW) index exclusively. While HNSW is a proven approach for vector search, having only one indexing option might limit some specialized use cases.
Data Handling
Chroma organizes data into collections, handling both embeddings and their associated metadata. It can automatically process and embed documents, making it simpler to work with raw data. Each document can include additional metadata for filtering and organization.
Aerospike requires at least one vector per record in the specified vector field of an index. It supports multiple vectors and indexes per record, offering flexibility in how you search your data. Records are written directly to the Aerospike Database (ASDB) and are immediately visible.
Scalability and Performance
Chroma prioritizes developer productivity and offers good performance for many use cases. However, the documentation doesn't detail specific scalability features.
Aerospike shines in scalability with its distributed indexing system. The indexing process runs concurrently across all AVS nodes, using all available CPU cores in the cluster. It uses vector extensions (AVX) for parallel processing and includes smart caching through "pre-hydration" of the index cache.
Flexibility and Customization
Chroma provides an open-source codebase (Apache 2.0 license) that developers can modify and extend. It works with various embedding models and data types, offering flexibility in implementation.
Aerospike's vector search capability is more structured but allows customization through multiple vectors and indexes per record. The system is part of Aerospike's broader NoSQL database offering.
Integration and Ecosystem
Chroma offers first-party client SDKs for Python and JavaScript/TypeScript, making it accessible for most modern development stacks. It's designed to work well with other AI tools and frameworks.
Aerospike integrates with its existing NoSQL database ecosystem, which might be valuable if you're already using Aerospike for other data storage needs.
Ease of Use
Chroma emphasizes simplicity with an intuitive API design. Its focus on developer productivity means less time spent on setup and configuration. The system handles many complex operations automatically, like document tokenization and embedding.
Aerospike's vector search requires more technical understanding, particularly around index configuration and optimization. However, it provides detailed control over the indexing process.
Cost Considerations
Chroma is open-source and free to use. They plan to offer a managed service (Hosted Chroma) in the future, but pricing isn't yet available.
Aerospike Vector Search is in Preview and requires early access approval. Costs would likely align with Aerospike's enterprise pricing model.
When to Choose Each Technology
When to Choose Chroma
Chroma works best for teams building new AI applications that need a straightforward vector search solution, particularly when working with language models and document embeddings. It's the right choice when you want minimal setup time, need automatic handling of embedding generation, and prefer a simple API that lets you focus on building features rather than managing infrastructure. Small to medium-sized teams, startups, and projects that need quick iteration will find Chroma's developer-friendly approach particularly valuable.
When to Choose Aerospike
Aerospike Vector Search is ideal for enterprise environments that need high-performance vector search integrated with their existing NoSQL infrastructure. It's the better choice when you need guaranteed scalability, have strict performance requirements, manage large distributed datasets, and want precise control over the indexing process. Organizations already using Aerospike's NoSQL database or those requiring immediate consistency and enterprise-grade features will benefit most from AVS.
Conclusion
Your choice between Chroma and Aerospike ultimately depends on your specific needs: Chroma offers simplicity, quick setup, and strong AI integration features, while Aerospike provides enterprise-grade scalability and precise control over indexing. Consider your team's technical expertise, existing infrastructure, performance requirements, and growth projections when making your decision. For newer AI projects prioritizing development speed, Chroma is often the better choice. For enterprise applications requiring scalability and precise control, especially those already using Aerospike, AVS is likely the better fit.
While this article provides an overview of Chroma and Aerospike, it's key to evaluate these databases based on your specific use case. One tool that can assist in this process is VectorDBBench, an open-source benchmarking tool designed for comparing vector database performance. Ultimately, thorough benchmarking with specific datasets and query patterns will be essential in making an informed decision between these two powerful, yet distinct, 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 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 WilliamsChloe Williams is a technical writer at Zilliz.
- What is a Vector Database?
- What is Chroma**? An Overview**
- What is Aerospike? An Overview
- Key Difffrences
- Search Methodology
- Data Handling
- Scalability and Performance
- Flexibility and Customization
- Integration and Ecosystem
- Ease of Use
- Cost Considerations
- When to Choose Each Technology
- Conclusion
- Using Open-source VectorDBBench to Evaluate and Compare Vector Databases on Your Own
- Further Resources about VectorDB, GenAI, and ML
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