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Annoy vs Voyager: Choosing the Right Vector Search Tool for GenAI

Annoy vs Voyager: Choosing the Right Vector Search Tool for GenAI

Oct 07, 202411 min read

As AI-driven applications continue to grow, the need for fast and scalable vector search tools has become essential. Vector search is a key element in recommendation systems, image retrieval, natural language processing (NLP), and other fields where finding similarities between high-dimensional data is critical. Among the many tools available for vector search, Annoy and Voyager are two widely used options, each offering distinct advantages.

In this article, we’ll compare Annoy and Voyager, focusing on their features, search methodologies, scalability, and use cases to help you decide which one is better suited for your needs.

What is Vector Search?

Before diving into the specifics of Annoy and Voyager, it's essential to understand vector search. Simply put, Vector search, or vector similarity search, finds the closest vectors (data points) in a high-dimensional space to a given query vector. These vectors are often generated by machine learning models to capture the essence of the unstructured data (e.g., the meaning of a sentence or the features of an image).

Unlike traditional databases, where searches are based on exact matches or filtering, vector search focuses on similarity. The goal is to find vectors that are "close" to each other based on a distance metric (such as Euclidean distance or cosine similarity). For instance, vectors can represent words or sentences in natural language processing (NLP), and vector search helps find the most semantically similar words or texts. In recommendation systems, vector search identifies items closest to a user's preferences. Vector searches also play a crucial part in retrieval augmented generation (RAG), a technique that augments the output of large language models (LLMs) by providing them with extra contextual information.

There are many solutions available on the market for performing vector searches, including:

Vector search libraries such as Annoy and Voyager.
Purpose-built vector databases such as Milvus, Zilliz Cloud (fully managed Milvus)
Lightweight vector databases such as Chroma and Milvus Lite.
Traditional databases with vector search add-ons

What is Annoy? An Overview

Annoy (Approximate Nearest Neighbors Oh Yeah) is an open-source library developed by Spotify that is designed for efficient approximate nearest-neighbor (ANN) search in high-dimensional spaces. Its primary function is to quickly find items that are similar to a given query item, based on vector embeddings. Annoy is particularly useful when working with large datasets where exact matches aren't as important as quickly finding "close enough" results. Based on user preferences, it is often used to build recommendation engines that suggest similar items (like songs, products, or videos).

Core Features and Strengths of Annoy

Approximate Nearest-Neighbor Search: Annoy is known for its speed in performing approximate nearest-neighbor (ANN) searches, which provide results that are “close enough” without requiring exact matches. This is particularly useful for applications dealing with massive datasets where an exact search might be too slow or resource-heavy.
Tree-Based Indexing: Annoy uses random projection trees to index data, which speeds up search queries by organizing the data into more manageable subsets.
Disk-Based Storage: One of the most valuable features of Annoy is that it stores its indexes on disk. This means that large datasets that don't fit into memory can still be indexed and searched efficiently. Annoy’s disk-based storage also allows for sharing indexes across different processes, helping to reduce memory usage.
Memory Efficiency: Annoy is optimized to work efficiently with memory. It allows you to build the index in memory and store it on disk, making it possible to handle large datasets even if you don't have enough RAM. This feature is particularly useful if your system's memory is a constraint.
Immutable Indexes: Once an index is built in Annoy, it cannot be modified. If the dataset changes, you’ll need to rebuild the entire index. This makes it a good choice for static datasets, where data doesn't change frequently.
Batch Queries: You can run multiple queries in parallel, which helps to optimize the search process further, especially for high-throughput applications.
Language Support: Annoy is primarily used in Python but is written in C++ for performance reasons.

Annoy’s strength lies in its simplicity and ability to handle high-dimensional vector searches with speed and efficiency. However, it sacrifices some level of accuracy to achieve these performance gains.

What is Voyager? An Overview

Voyager is Spotify’s newest vector search library, designed to replace Annoy. Built on top of hnswlib, Voyager is optimized for modern nearest-neighbor search use cases that demand higher speed and accuracy, better memory efficiency, and broader flexibility. It also offers strong production-ready features that make it more suitable for enterprise-level deployments.

Core Features and Strengths of Voyager

Speed and Accuracy: Voyager offers more than 10 times the speed of Annoy while maintaining the same recall rate. It also delivers up to 50% more accuracy for the same level of speed, providing more precise results without compromising performance.
Memory Efficiency: Voyager is highly memory-efficient, using up to 4 times less memory than Annoy, thanks to its use of E4M3 8-bit floating point representation. This makes it ideal for memory-constrained environments.
Multithreading and Scalability: Voyager supports multithreaded index creation and querying, making it highly scalable. Whether you are building a small app or a large enterprise solution, Voyager can handle the workload efficiently.
Language Support: Unlike many nearest-neighbor search tools that only support Python, Voyager provides identical interfaces for both Python and Java, making it more versatile for different development environments.
Fault-Tolerant and Production-Ready: Voyager includes fault-tolerant index files with corruption detection, ensuring that the system can handle large-scale deployments without the risk of data corruption.
Google Cloud Integration: Voyager offers built-in support for stream-based I/O from Google Cloud Services, allowing you to stream indices directly from the cloud, which can simplify the management of large datasets.

Voyager is designed with production use in mind, offering speed, accuracy, and memory efficiency while providing strong language support and compatibility with cloud-based infrastructure.

Key Differences Between Annoy and Voyager

Search Methodology

Annoy uses random projection trees for its approximate nearest-neighbor search, prioritizing speed at the expense of some accuracy. This method works well when the search dataset is large, and you don’t need perfect results. In contrast, Voyager is based on hnswlib, which employs the Hierarchical Navigable Small World (HNSW) algorithm. This method provides better accuracy and speed, outperforming Annoy in most use cases, especially when precision is important.

Data Handling

Annoy is optimized for unstructured data and high-dimensional vector searches. Its tree-based approach handles large volumes of data efficiently, but it isn’t very flexible when it comes to structured or semi-structured data. Voyager, on the other hand, is more flexible. While both handle vector data, Voyager’s design, particularly its multithreading and Google Cloud integration, makes it better suited for more complex, large-scale data environments where multiple data types are involved.

Scalability and Performance

Both tools scale well, but Voyager offers more scalability options with its support for multithreaded index creation and querying. Voyager's fault-tolerant index files and cloud compatibility also make it easier to scale across distributed systems or cloud environments. Annoy is simpler to deploy and can handle large datasets efficiently, but it is not as robust in enterprise-level scalability or cloud-native architectures.

Flexibility and Customization

Annoy offers basic customization, but its primary focus is on approximate nearest-neighbor search, limiting its flexibility in adapting to different data types or search methodologies. Voyager, by contrast, is built with customization in mind. Users can fine-tune performance based on their specific needs, balancing between speed, accuracy, latency, and cost. This makes Voyager a better fit for applications that require more tailored solutions.

Integration and Ecosystem

Annoy is a standalone library, with limited support for integrating into larger ecosystems. It works well with Python-based projects but lacks the broader integration capabilities needed for enterprise environments. Voyager shines here, offering seamless integration with cloud-based services like Google Cloud and full support for Java and Python. This makes it easier to incorporate Voyager into larger data pipelines, machine learning workflows, and enterprise systems.

Ease of Use

Annoy’s simplicity is one of its greatest strengths. It is easy to set up and use, especially if you’re working in a Python environment. However, it is relatively limited in scope. Voyager, on the other hand, offers more features and flexibility but comes with a slightly higher learning curve due to its additional capabilities and customization options. The fact that Voyager is production-ready and includes extensive documentation for both Python and Java does help to ease the process of integrating it into more complex systems.

Cost Considerations

Annoy is completely open-source and does not carry any licensing costs. However, users still need to account for infrastructure and scaling costs when deploying it in large-scale environments. Voyager, being newer and more advanced, can incur additional costs, especially if you use managed services like Google Cloud for hosting its indexes. That said, Voyager’s memory efficiency and ability to handle large datasets at scale can result in cost savings over time, especially for enterprise-level applications.

Security Features

Annoy does not come with built-in security features. Users would need to implement encryption, authentication, and access control separately if needed. Voyager, being designed for production environments, includes better support for fault-tolerant index files and corruption detection, but security features like encryption and access control would still need to be implemented outside of the tool itself.

When to Choose Annoy

Annoy is an excellent choice if:

You are working on a smaller-scale project where approximate nearest-neighbor search is sufficient.
Your application doesn’t need to handle structured or semi-structured data.
You prioritize speed over accuracy, and the precision of search results is less critical.
You are constrained by memory resources but need to handle large datasets efficiently.
Your team prefers a lightweight, easy-to-use tool with minimal setup.

When to Choose Voyager

Voyager is the better choice if:

You require a high level of accuracy and need precise results for your nearest-neighbor searches.
Your application handles structured, semi-structured, and unstructured data, and you need more flexibility.
You are working in a large-scale, cloud-based environment and require seamless integration with cloud services like Google Cloud.
Your project needs to balance speed, accuracy, and cost, with the option to customize the search algorithms and data handling.
You need a production-ready solution with strong support for both Python and Java and robust fault-tolerance features.

Comparing Vector Search Libraries and Purpose-built Vector Databases

Both vector search libraries like Annoy and Voyager and purpose-built vector databases like Milvus aim to solve the similarity search problem for high-dimensional vector data, but they serve different roles.

Vector search libraries focus solely on the task of efficient nearest neighbor search. They offer lightweight, fast solutions for finding vectors similar to a query vector. They are often used in smaller, single-node environments or for applications with static or moderately sized datasets. However, they generally lack features for managing dynamic data, providing persistence, or scaling across distributed systems. Developers using these libraries typically need to manually handle data management, updates, and scaling.

On the other hand, purpose-built vector databases like Milvus and Zilliz Cloud (the managed Milvus) are comprehensive systems designed for large-scale vector data management. These databases go beyond simple vector search, offering features like persistent storage, real-time updates, distributed architecture, and advanced querying capabilities. They support dynamic datasets and can easily handle real-time applications where data is frequently updated. Additionally, vector databases often include integrated support for combining vector searches with traditional filtering and metadata queries, making them ideal for production environments requiring scalability, high availability, and more complex search functionalities.

Check out the latest new features and enhancements of Zilliz Cloud: Zilliz Cloud Update: Migration Services, Fivetran Connectors, Multi-replicas, and More

When to Choose Each Vector Search Solution

Choose Vector Search Libraries if:
- You have a small to medium-sized, relatively static dataset.
- You prefer full control over indexing and search algorithms.
- You're embedding search in an existing system and can manage the infrastructure.
Choose Purpose-Built Vector Databases if:
- You need to scale to billions of vectors across distributed systems.
- Your dataset changes frequently, requiring real-time updates.
- You prefer managed solutions that handle storage, scaling, and query optimizations for you.

In summary, vector search libraries are best suited for simpler, smaller-scale use cases where speed and memory efficiency are priorities, but operational complexity is minimal. Purpose-built vector databases, by contrast, are designed for large-scale, production-grade systems that demand dynamic data handling, scalability, and ease of use, often providing significant operational benefits for developers managing complex applications.

Evaluating and Comparing Any Vector Search Solutions

OK, now we've learned the difference between different vector search solutions. The following questions are: how do you ensure your search algorithm returns accurate results and does so at lightning speed? How do you evaluate the effectiveness of different ANN algorithms, especially at scale?

To answer these questions, we need a benchmarking tool. Many such tools are available, and two emerge as the most efficient: ANN benchmarks and VectorDBBench.

ANN benchmarks

ANN Benchmarks (Approximate Nearest Neighbor Benchmarks) is an open-source project designed to evaluate and compare the performance of various approximate nearest neighbor (ANN) algorithms. It provides a standardized framework for benchmarking different algorithms on tasks such as high-dimensional vector search, allowing developers and researchers to measure metrics like search speed, accuracy, and memory usage across various datasets. By using ANN-Benchmarks, you can assess the trade-offs between speed and precision for algorithms like those found in libraries such as Faiss, Annoy, HNSWlib, and others, making it a valuable tool for understanding which algorithms perform best for specific applications.

ANN Benchmarks GitHub repository: https://github.com/erikbern/ann-benchmarks

ANN Benchmarks Website: https://ann-benchmarks.com/

VectorDBBench: an open source benchmarking tool

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. VectorDBBench is written in Python and licensed under the MIT open-source license, meaning anyone can freely use, modify, and distribute it.

VectorDBBench GitHub repository: https://github.com/zilliztech/VectorDBBench
Take a quick look at the performance of mainstream vector databases on the VectorDBBench Leaderboard.
Techniques & Insights on VectorDB Evaluation:
- Benchmark Vector Database Performance: Techniques & Insights
- Compare any vector database to an alternative

Further Resources about VectorDB, GenAI, and ML

Chloe Williams
Chloe Williams is a technical writer at Zilliz.

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