In this blog post, we will explore the top 10 vector databases used for building LLM applications. Here’s a primer on how to build your own LLM applications.
Now, let’s jump directly into it.
Which is the best Vector Database? Comparing the top 10 candidates.
1. Pinecone
Pinecone is a state-of-the-art vector database that is revolutionizing digital data interaction. Just picture being able to set up an account and generate your first index in under 30 seconds. The ultra-fast vector searches supported by Pinecone’s vector database are essential for many applications, including search engines, recommendation engines, and detection tools. You can quickly expand your index or build new ones to accommodate billions of embeddings, making sure that your data requirements are consistently satisfied effectively. Looking more closely at its capabilities, Pinecone gives results that are extremely relevant by integrating vector search with metadata filters. To ensure you always have access to the most recent findings, the Pinecone index is updated in real-time in addition to this.
It strikes the ideal mix between vector search and keyword boosting to satisfy a variety of needs. Pinecone performs much quicker than conventional techniques, with query latency of 5–10 ms and update latency of 9 ms. Additionally, it’s always growing—Pinecone now has more than 100 billion vectors in total.
Pinecone’s interoperability with well-known cloud providers, data sources, models, frameworks, and other components makes it a flexible and essential component of the AI stack that developers choose. This is just one more desirable feature of Pinecone. Pinecone delivers on security and dependability without fail. Your data is safe since it complies with SOC 2 and HIPAA regulations.
Cloud-native and capable of supporting large-scale mission-critical applications, it provides complete administration in the cloud of your choosing. Pinecone’s accessibility and convenience are further enhanced by its availability via markets like as AWS, Azure, and GCP.
Pinecone has its limitations, much like any other device, though. It can accommodate, for example, a maximum of 20,000 vector dimensions and a maximum of 2MB for upsert requests, with a suggested cap of 100 vectors per request. After upserting, vectors might not be immediately accessible to queries. Pinecone allows sparse vector values with sizes up to 1000 non-zero values.
The number of results to return, top_k, has a maximum value of 10,000; however, for searches with specific parameters, this value lowers to 1,000. Additionally, there may only be 1,000 vectors in a get or delete request. Pinecone’s metadata functionality is also limited; it can only handle 40 KB of metadata per vector and cannot handle large cardinality or null values.
2. Milvus
Milvus is an innovative vector database that is open-source and cloud-native, which greatly improves AI applications and embedding similarity search. Since its launch in 2019, Milvus has developed into a crucial instrument in the field of artificial intelligence, democratizing vector database technology access and streamlining the search for unstructured data in a variety of deployment scenarios. Its source code, which is accessible on GitHub under an Apache 2.0 license, has attracted a lot of interest and contributions from the community, which is indicative of its strong and expanding appeal.
The simplicity of usage of Milvus is one of its best qualities. In less than a minute, you can use it to build a large-scale similarity search service. This ease of use is further boosted by straightforward SDKs accessible in a variety of languages. Milvus is renowned for its speed as well, offering ten times faster retrieval times because to sophisticated indexing algorithms and efficient technology.
Milvus has been extensively tested for availability and scalability across several corporate applications, demonstrating its dependability and robustness. It is a good fit for handling massive amounts of vector data because of its distributed and high-throughput characteristics. Because computation and storage are kept separate in the database’s cloud-native design, scalability up and down is supported, guaranteeing reliable performance even as your data expands.
With support for many data types, attribute filtering, UDF, adjustable consistency levels, time travel, and more, Milvus is a feature-rich tool. Its design is made up of two layers: one for storage and the other for computation. All of its parts are stateless for maximum flexibility and elasticity. This architecture consists of worker nodes that carry out orders, a coordinator service that acts as the system’s brain, an access layer made up of stateless proxies, and a storage layer that maintains data.
Milvus is a popular tool for developing similarity-search-based apps. It works especially well in recommender systems, question-answering systems, semantic text search, AI advertising, and similarity searches for images, videos, and audio.
Like any technology, Milvus is not without its limits. These include limitations on the names of resources, the quantity of resources in a collection, string lengths, vector dimensions, and the amount of input and output allowed each RPC. It’s crucial to remember that querying data with duplicate keys may result in unexpected behaviors because Milvus does not provide update operations and does not check for duplicate entity primary keys. Furthermore, because to gRPC limitations, the maximum amount of data that may be inserted during an insert operation is 1,024 MB.
3. Chroma
Chroma is a cutting-edge AI-native open-source embedding database designed exclusively for creating AI applications with embeddings, providing a unique approach to data management and utilization for AI-driven activities. The effective handling of embeddings, which is essential to contemporary AI applications, is the center of its functionality. With Chroma, you can quickly set up and begin working on AI projects directly on your computer because to its user-friendly interface. An even more accessible hosted version is promised for a future release, increasing its usefulness and scope.
With Chroma, getting started is simple. It can be used in a variety of programming contexts and is flexible because it supports both Python and JavaScript. The installation procedure is straightforward and requires only the normal package installation instructions for the chosen language. After installation, setting up the Chroma client and starting to create and manage your embedding, document, and metadata collections is simple.
Being able to manage the complete text document workflow is one of Chroma’s primary features. Chroma automates these procedures, saving time and money, from tokenization and embedding to indexing. In Chroma, you can add documents to your collection, and the collection’s embedding feature will take care of the rest. When managing massive amounts of data effectively, this capability is quite helpful.
Additionally, Chroma provides data management freedom. Instead of going through the embedding procedure, Chroma lets you import pre-generated embeddings straight into your collection. When pre-computed embeddings are available or chosen, this capability supports a broad variety of use cases.
Chroma offers similarly flexible querying of collections. A series of texts or embeddings can be used to execute searches, and Chroma will provide the most pertinent results depending on how similar the query inputs are to each other. This is an important feature for applications where it’s necessary to locate comparable things in a big dataset, such as content discovery and recommender systems.
As with many technologies, Chroma has drawbacks in addition to its numerous benefits. To start, each document you upload has to have its own unique ID; if you add the same ID again, the first value will be stored. This might be problematic in instances where document tracking or versioning is required. Chroma also requires careful data preparation since it depends on embedding dimensions; if the provided embeddings do not match the collection’s dimensions, it will raise an exception.
4. Weaviate
Weaviate is a fascinating open-source vector database that is changing the way we store and retrieve data. Weaviate’s basic architecture is to use vectors to store data items and their semantic attributes, allowing for a unique mix of vector search and structured filtering. This capability provides a useful tool for software developers, data engineers, and data scientists to install and manage machine learning models fast and effectively.
Weaviate’s modular configuration is one of its most notable characteristics. It includes a number of pre-installed modules for applications such as NLP, semantic search, automated categorization, and picture similarity search. These functionalities are simply incorporated into current architectures and provide complete CRUD capability, similar to other open-source databases. Weaviate also grows with workload needs and works well in Kubernetes environments because it is distributed and cloud native.
Weaviate has various special features for data administration and investigation. In order to provide more precise search results and suggestions, it makes use of a vector space model to comprehend the semantic links between various things. This feature is very helpful for organizing and interpreting unstructured data. Weaviate’s capacity to handle massive amounts of data without experiencing performance issues is another one of its strong points: scalability. Additionally, its adaptability makes it possible to integrate it with a variety of data sources, increasing its usefulness in a range of processes.
Because of this deep integration within Weaviate’s architecture, extensive data analysis can be done without the need of external tools. Another plus is that it can see and understand data as linked entities, which is essential for finding hidden patterns and insights. Support for graph data models is one of its strongest points. Weaviate does, however, have certain restrictions. The resources that the Weaviate cluster has access to determine how many classes, connections, and attributes may be created in Weaviate. The vector dimensions have a ceiling of 65,536. This is the sole noteworthy constraint. Despite this, Weaviate’s advantages exceed its drawbacks, making it a game-changing tool in database technology and search engines.
5. Azure AI Search
Azure AI Search, as a vector database store, provides a substantial advance in information retrieval and search technology. With this powerful platform, you can use AI to create complex search experiences and generative AI apps. Whether you’re building mobile apps, business solutions, or SaaS products, Azure AI Search can help.
Azure AI Search includes vector search, which uses numeric representations of material to perform search scenarios. This implies that, rather than using standard keyword-based search, Azure AI Search uses machine learning models to comprehend the meaning and context of words and phrases. Even if the precise phrases are not contained in the page, this method produces more relevant search results.
Support for hybrid environments is one of Azure AI Search’s main advantages. Vector searches can be combined with filters and other query types in a single search request. Vector data can be indexed as fields in documents alongside alphanumeric content. Numerous use cases are made possible by this adaptability, including multi-modal searches across several data types, text-based vector searches, multilingual searches, and filtered vector searches.
The simplicity of creation and deployment of Azure AI Search is another excellent feature. With the use of user-friendly RESTful APIs and SDKs, Azure storage solutions integration, and data ingestion and search index building, it streamlines these complicated processes. As a result, less operational overhead is often involved in maintaining search solutions.
It is important to acknowledge certain constraints and factors. For example, Azure AI Search’s integrated vectorization capability, which complements indexer-based indexing capabilities with text-to-vector embedding and data chunking, is still in public preview and may not be appropriate for all production scenarios. Large datasets and heavy traffic loads may be handled with great benefit by Azure AI Search, but managing such large-scale deployments may call for careful planning and optimization.
6. Deep Lake
Deep Lake emerges as a game-changing technology in the fast-paced world of data management, particularly for deep learning applications. The platform is adaptable to many data sources such as PDFs, vectors, audio, videos, and more, since it combines the features of data lakes with the sophisticated requirements of vector databases. This mixed method offers a significant improvement in handling and using data for AI by enabling effective AI data storage and optimization for Large Language Models (LLMs).
One of Deep Lake’s most notable features is its speed and efficiency while handling datasets on the petabyte scale. This is especially useful for generative AI applications that require quick data access and processing. Users have the ability to store views, query large datasets quickly, and stream data to machine learning (ML) frameworks for training. This capability cuts down on the time it takes to ship AI solutions, which is both a practical benefit and a technical advancement.
Another special feature provided by Deep Lake is the ability to view dataset evolution right from a browser. This helps with improved data management and comprehension in addition to improving user experience. Furthermore, it has dataset version management capabilities, which make it comparable to a “Git for data” and facilitate efficient and easy modifications of dataset items between versions by users.
Like any technology, Deep Lake is not without its restrictions. It could be too much for smaller, simpler data requirements, even if it works well for handling big, complicated information. There is a learning curve associated with its features because of their complexity, particularly for individuals who are unfamiliar with such sophisticated data management systems. In addition, installation and operating costs may need to be taken into account based on the particular use case.
7. Qdrant
Qdrant describes itself as a large-scale, high-performance vector database that will transform the way artificial intelligence (AI) applications organize and search through data. Fundamentally, Qdrant is a vector similarity search engine that can effectively handle high-dimensional vectors. These vectors can represent a wide range of data items, such as songs in a music recommendation system or photos in a recognition system. As each vector element is associated with a distinct feature or property, Qdrant is the perfect platform for applications that need to do precise and quick similarity searches.
Support for several distance metrics, such as Euclidean Distance, Cosine Similarity, and Dot Product, which are essential for carrying out similarity and semantic searches, is one of Qdrant’s key features.
It also has payload support, so you can store related data in addition to the vectors. This gives you greater freedom and control over how you handle your data by enabling filtering results based on payload values.
Asit was created in Rust, Qdrant is renowned for its resource efficiency and for providing scalable, cloud-native solutions. With the appropriate number of computer resources, it can handle data of any magnitude thanks to its distributed processing capabilities.
All technologies, though, have their limitations. Since Qdrant’s primary purpose is vector search, full-text support is only implemented to the degree that it doesn’t interfere with the ability to do vector searches. Its usefulness in situations where full-text search is a main need may be limited by this concentration. Furthermore, while Qdrant allows for the creation of many collections, each collection necessitates the use of extra resources. As a result, generating several little collections might result in high resource use, which may not be appropriate in all circumstances.
8. Elasticsearch
Elasticsearch is a reliable and adaptable analytics and search engine that is essential to the modern, data-driven world. It is the distributed brains of the Elastic Stack, connecting with Kibana for interactive visualization and Logstash and Beats for data aggregation with ease. It is quite amazing how well it can give search and analytics in almost real-time for a variety of data kinds, including numerical, geographical, and structured or unstructured text. Large volumes of data can be effectively stored and indexed using Elasticsearch, guaranteeing quick searches and the capacity to compile data for the identification of trends and patterns. Because of its distributed architecture, it can scale easily to handle increasing data and query loads.
One of Elasticsearch’s best features is its query DSL, which makes it easy and precise to create sophisticated searches. The platform’s application scope is further expanded by the usage of JSON objects, which improves compatibility with several programming languages. Its adaptability covers a wide range of use cases, including handling logs, analytics, and security event data in addition to supporting search functions in websites and applications. Elasticsearch’s versatility is demonstrated by its machine learning skills and use as a vector database or storage engine for business activities. It is a useful tool in the GIS field for organizing and interpreting geographical data, and it is especially useful for processing and storing genetic data in bioinformatics research.
Elasticsearch does have several drawbacks, though. Its absence of standard database functionality, such as constraints, joins, relations, and transactional behavior—which might be essential for some applications—and its schema-less design are two major disadvantages. In situations when instantaneous data consistency is essential, its dependence on eventual consistency rather than stringent consistency, as seen in standard RDBMS, may provide difficulties. Operational expenses are increased by the frequent need for new, paid plugins for security capabilities, especially access rights management. Although Elasticsearch offers simple scalability, this entails managing the underlying infrastructure well in order to accommodate the additional demand and guarantee excellent performance.
9. FAISS
FAISS (Facebook AI Similarity Search), a high-performance library created by Facebook’s AI team, is optimized for dense vector similarity search and grouping. It is especially made to provide scalable and more effective similarity search functionalities, hence overcoming the drawbacks of conventional query search engines that are tuned for hash-based searches. FAISS excels at managing multimedia document embeddings, enabling quick and accurate searches across enormous datasets.
FAISS’s broad range of indexing techniques, such as the brute-force search-based IndexFlatL2 and other types like the inverted file (IVF) index, HNSW, product quantization (PQ), locality-sensitive hashing (LSH), and scalar quantizers, is one of its main advantages. These techniques are designed to make dense vector clustering and similarity searches more effective.
In addition, FAISS optimizes memory-speed-accuracy trade-offs and search efficiency by incorporating sophisticated techniques like quantization and partitioning. Smaller subsets of the feature space are created via partitioning, and vectors are compressed into a more manageable form for easy handling and retrieval by quantization.
FAISS’s GPU implementation, which can operate up to 5–10 times quicker than its CPU version, is another important benefit. FAISS is therefore particularly well-suited for applications needing k-means and small k-selection algorithms, as well as those requiring fast, accurate, and approximate closest neighbor search. The library’s CUDA support for GPUs further improves speed, which makes it a great option for managing big datasets. FAISS does have several restrictions, though. For example, relying solely on the IndexFlatL2 index, which compares each query vector to every other vector in the index to do exhaustive searches, can become computationally costly and inefficient, particularly when dealing with huge datasets. While FAISS provides methods for optimizing searches, such as index splitting, they may necessitate extra processes and considerations.
10 OpenSearch
OpenSearch is a community-driven open-source search and analytics engine, arose in 2021 as a fork from Elasticsearch and Kibana, with Amazon Web Services pushing its development. The necessity for an open source substitute was created in reaction to Elastic’s transition to a dual license structure. OpenSearch remains committed to being free and open, allowing for the creation of more extensive goods and services without being restricted by proprietary licensing under the Apache 2.0 License.
Full-text querying, autocomplete, scroll search, and customized scoring and ranking are just a few of the many capabilities that the engine provides to improve user experience. Beyond simple search functions, it incorporates familiar SQL query syntax, complex data querying via asynchronous and pipeline processing languages, and application analytics for system health monitoring.
Notably, OpenSearch has an ML Commons library containing machine learning algorithms for data trend forecasts, as well as a Data Prepper for data collecting and processing. Its usefulness is further enhanced with dashboard notebooks, index management, index transformations, and a performance analyzer with a root cause analysis framework.
OpenSearch’s adaptability is demonstrated by the variety of use cases it can be used to, including observability of cloud infrastructure and application search. With its strong anomaly detection and trace analytics features, it provides complete monitoring solutions and enables the ingestion, visualization, and analysis of applications across several clouds. Its k-NN search power is especially impressive for machine learning applications, such fraud detection and product suggestions. Furthermore, data safety and compliance are guaranteed by its sophisticated security features, which include encryption and role-based access control.
OpenSearch has significant drawbacks in spite of these benefits. Being a relatively young player, it’s changing quickly, which might present a problem for companies looking for consistency in their analytics and search capabilities. Users switching to OpenSearch may need to adjust to changes and perhaps have incompatibilities with current systems and plugins due to the divergence from Elasticsearch. Additionally, OpenSearch management and optimization for particular requirements need for a certain degree of competence, which might provide a challenge for teams without specialized technical skills.
Conclusion
Vector databases are important in the context of LLM applications. They function as a kind of semantic vault by storing embeddings that represent complex objects (words, sentences, and pictures) as dense numerical vectors. In LLM contexts, these embeddings improve the AI’s comprehension and reactions. With the addition of transparency and multi-source synthesis for complicated tasks, Retrieval-Augmented Generation (RAG) guarantees that replies are based on trustworthy, external sources, substantially enhancing AI’s capabilities. Different demands are met by different vector storage. Pinecone and other specialized vector databases handle dense vectors, which are perfect for dynamic data applications. Existing databases that enable vector indexing (such as Elasticsearch, Redis, and PostgreSQL with PGVector) and integrations like FAISS into DBMSs demonstrate how conventional database robustness can coexist with contemporary vector data processing requirements.
Specific application needs determine which vector database is best. Vector databases improve comprehension and response accuracy in LLM applications such as chatbots. Vector search is superior than keyword matching for e-commerce platforms when it comes to product discovery. SQL code generation technologies improve their efficiency by storing and retrieving embeddings in vector databases.