Vector embedding is one of the most useful concepts in machine learning, especially when it comes to domains like recommendation systems or search algorithms.
A vector embedding is a dense numerical representation of real-world concepts like text, images, or audio as vectors in a vector space. It’s easy to perceive how close vectors are to each other when you equip the vector space with a metric. This also allows you to easily group similar points together.
In a machine learning model that deals with vector embeddings, you need to not only store the vectors efficiently but also search and perform mathematical operations on them. However, the most commonly used relational databases are not suitable for dealing with vector embeddings because of the fundamental difference between relational data and vector data. Vector databases are made specifically for working with vector embeddings.
Imagine you're building a recommendation system for an e-commerce site. With a traditional SQL database, you can store each item and its information (eg, color, model, price, etc.). If a user shows interest in an item, you can now search for items that are of the same color or same price as that item, and recommend them to the user. However, a similar item may not necessarily have the same color or price. Because SQL databases have no concept of similarity, your selections can be inaccurate.
If you represent the items as vectors, you can leverage a vector database like Embeddinghub to easily filter similar items to the ones the user has previously shown interest in. For example, you can perform a nearest neighbor lookup to find the most relevant item, or you can calculate the average vector difference between a black item and a red item and use that difference to recommend the closest color for items that don’t have a red version.
In this article, you’ll learn about the best vector database options, their features, and how they compare.
Tools for this Roundup
There are several tools that can effectively allow your machine learning teams to set up a vector database for their projects. The tools for this specific roundup have been chosen based on usability, flexibility, scalability, cost, and organizational features.
Usability
A tool can be high-performing yet turn out to be unprofitable if it takes considerable time and effort to teach your team how to set it up and use it effectively. Complex structures and processes are challenging for current employees and pose a threat to projects when new members are added.
Since vector databases are critical to the success of machine learning projects, they need to be easy to set up, easy to learn, and use. Features like SDK support for multiple languages, data cluster management, deployment capabilities, and administrative add-ons should be considered when reviewing vector database options.
Flexibility
The ability to blend with the needs and ecosystem of the organization should be one of your top criteria. If it’s not, the projects have to work around the tool’s features, which can restrict innovation, functionalities, and the speed of production.
A vector database should not only efficiently store and retrieve vectors, they should also support common vector operations such as approximate nearest neighbor operations, space partitioning, sub-indices, and averaging.
Another important aspect is the algorithm used to calculate similarity and perform vector search. A vector database should not only use a high-performance algorithm, but also ideally offer custom algorithms, if the need arises.
Scalability
Vector databases are often integrated into a company’s existing infrastructure with scale in mind. The data handled by vector databases is of considerable volume and requires effective techniques such as hashing and sharding to enable expansion at scale. It’s essential to pay attention to the tool’s storage and transaction mechanisms to estimate the potential scalability.
Cost
The costs incurred during or projected for purchase, set up, and maintenance should be considered when choosing a vector database. If it's a managed offering, the pricing structure plays a huge role in determining the cost of the tool. For a self-hosted vector database, it's the cost of the infrastructure that is most important.
If the infrastructure of a vector database is sturdy, yet complex, it might incur high costs over time due to the training involved when the project changes hands. Integrations can also be time-consuming and more difficult to fix and maintain or fault management could be troublesome leading to increased downtime. Features like auto-backup disaster recovery can be huge cost-saving mechanisms in this regard.
Organizational Features
Vector database tools have certain core features that every individual tool specializes in. Alongside those features, it’s beneficial to opt for tools that can offer support to manage peripheral requirements like administrative tasks, security, or project management.
For example, a tool that ensures the instant availability of vectors, cluster insights, real-time alerts, or infrastructure orchestration, will facilitate and speed up the operations cycle leading to faster production with the least amount of manual administration.
These additional features can help save you money and eliminate the need for more third-party tools in the ecosystem. It also increases team efficiency by removing hurdles like tool finalization, set up processes, and maintenance.
Vector Database Options
In addition to the features mentioned above, there are primary strengths that are unique to each tool that should be taken into consideration. Following are overviews of five tool options that can get you started with vector databases.
Embeddinghub
Embeddinghub is an open-source solution designed to store machine learning embeddings with high durability and easy access. It allows intelligent analysis, like approximate nearest neighbor operations, and regular analysis, like partitioning and averaging. It uses HNSW algorithm for indexing the embeddings using HNSWLib, offering a high performance approximate nearest neighbor lookup.
The tool offers high-speed processing through local caching during training. It’s ideal for scale and can index billions of vectors on its storage layer.
Embeddinghub is not just effective for high-speed and high-volume analysis but is also a great administrative asset. With capabilities like access control, versioning, rollbacks, and performance monitoring, the documentation of this tool is extremely thorough and makes the swift, six-step initiation process easy.
Being an open-source platform, Embeddinghub is free to use and can be downloaded through a pip installation. The only costs incurred are from the adjacent tools in the data ecosystem.
Milvus
Milvus is a cloud-native vector database solution that can manage unstructured data. It supports automated horizontal scaling and uses acceleration methods to enable high-speed retrieving of vector data. Milvus comes with the added advantages of being user-friendly and cost-efficient, and boasts an impressive clientele with customers like Moj and Dailyhunt.
Milvus supports multiple approximate nearest neighbor algorithm based indices like IVF_FLAT, Annoy, HNSW, RNSG, etc.
It’s easy to get acquainted with Milvus through its refined and visually appealing guides which are being constantly improved due to its large open-source community.
Milvus is free to use and the only cost incurred is restricted to peripheral resources.
Pinecone
As a fully managed vector database, Pinecone specializes in enabling semantic search capabilities to production applications. It offers features like filtering, vector search libraries, and distributed infrastructure for the key benefit of reliability and speed. Other features include deduplication, record matching, recommendations, ranking, detection, and classification.
Pinecone supports exact KNN with FAISS. It's ANN capabilities are powered by a proprietary algorithm.
Pinecone has a fast setup process that requires just a few lines of code and claims that you can add it to production applications in less time than other models. Along with vector data efficiency, Pinecone takes care of sidelines like security through AWS and GCP environments, isolated containers, and encryptions. Pinecone’s guide offers a clean outline of its setup process.
With three tiers of pricing, Pinecone has something to offer everyone. The free version can get you started but if you’re looking for additional support, scaling, and optimization, you can upgrade to the standard version for seven cents an hour. The most expensive tier referred to as the enterprise version has custom pricing and additional features that can be added like dedicated environment support and multiple availability zones.
Weaviate
Weaviate by SeMI Technologies uses machine learning models to create and store vectors. It can support various types of data and offers assistance for some important use cases, like combined vector and scalar search, question-answer extraction, classification, and model customization. The tool can also conduct structured filtering on vectors and is accessible through a host of language clients.
Weaviate uses a custom HNSW algorithm that supports full CRUD. It can also support multiple ANN algorithms as long as they support full CRUD.
Weaviate has optimized storage which saves space for processing queries, which results in high-speed searchability. Other benefits include high scalability, cost-effectiveness, and thorough guides for quick setups.
The tool supports custom pricing based on user-specific requirements and a quote can be created by contacting their team directly.
To better understand the requirement fit, explore Weaviate’s use cases.
Vald
Vald is a highly scalable distributed vector search engine. Vald uses a distributed index graph to support asynchronous indexing. It stores each index in multiple agents which enables index replicas and ensures high availability.
Vald offers SDKs for multiple languages including Golang, java, NodeJS and Python. It uses the vector search engine NGT, which is very fast and guarantees high performance.
Vald is also open-source and free to use. It can be deployed on a Kubernetes cluster and the only cost incurred is that of the infrastructure.
Conclusion
Vector databases are becoming more and more common for machine learning projects, inciting interest among the giants in the tech world. It’s a go-to option for teams managing high-speed vector embedding at scale, and a definite opportunity that small- to medium-scale enterprises should explore.
Compared to traditional SQL databases, vector databases are much more suited to handle vector embeddings. They can leverage the vector representation of the data to perform a similarity search and can be used in recommendation systems, search engines, NLP, and computer vision projects.
With more flexibility than SQL databases, vector databases can ensure that you’re offering relevant products to your users without overly taxing your data team. Hopefully, the tools covered in this roundup have given you a good feel for what’s on the market that can help you hit those goals.