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Introduction
Vector database (Vector DB) has emerged as a powerful tool in recent years alongside the rapid growth of AI and machine learning technologies. It is essentially a data management system to store, search, and retrieve high-dimensional data generated by AI models, often in the form of texts, images, audio, or unstructured embeddings.
The popularity of AI, especially in the fields of computer vision and Natural Language Processing, has undeniably made vector DBs indispensable in managing the overwhelming volume of unstructured data produced by these AI systems. These systems, in turn, enable AI models to rapidly search for similar content for purposes such as recommendation engines, translation, and image recognition.
In this article, we cover the most recent professional vector DBs and examine their attributes, capacities, and how they are changing the business landscape with AI and big data. Whether you’re a tech expert who’s already working with vector databases, a business owner looking to stay competitive, or someone curious about how this technology can transform your work, we hope this list can help you identify the most current and well-suited vector DB that meets your unique requirements.
Milvus is an open-source vector database designed for handling massive-scale vector data. This vector database has excellent performance, with GPU acceleration, distributed querying, and efficient indexing. It is highly configurable and supports a range of indexing methods such as IVF, HNSW, and PQ, allowing users to balance accuracy and speed according to their needs. The database offers excellent scalability with efficient index storage and shard management, ensuring smooth growth as data volumes increase. With native support for multiple languages (Python, Java, Go, etc.) and integration with data pipelines like Kafka, Milvus also ensures seamless usability.
As an open-source solution, Milvus is cost-effective for on-premise use, though large-scale deployments may require substantial resources. For its flexibility in supporting real-time updates, hybrid search, and rich metadata handling, we recommend Milvus to enterprise-grade businesses looking to store and analyze large-scale data for insights across industries such as marketing, logistics, and customer analytics.
While Milvus provides powerful capabilities, managing and deploying it at scale can present challenges, especially for organizations without dedicated resources for database management. Shakudo can streamline the integration of Milvus into your existing infrastructure by offering a fully managed, cloud-based platform that simplifies deployment and scaling. With Shakudo, you can leverage Milvus’ high performance and advanced vector search capabilities without the operational complexity of managing the database yourself. Find Out How
Chroma is a vector DB designed specifically to query high-dimensional vector embeddings. Chroma has an intuitive API that simplifies integration into applications, making it accessible for developers and researchers without requiring extensive database management expertise.
Chroma delivers high accuracy with impressive recall rates, supporting embedding-based search and advanced ANN methods. While it offers compact storage, its storage efficiency is less robust for massive datasets compared to dedicated vector databases like Milvus. However, as an open-source option, it offers minimal deployment costs unless scaled heavily. This is a database we’d recommend for early-stage businesses with small-to-medium workloads, particularly for startups who are looking to experiment and prototype with AI models.
As businesses begin to scale their AI initiatives, they may find the need for more robust infrastructure and enhanced security. Shakudo helps secure companies’ integration of vector databases by providing built-in encryption and access control, ensuring data is protected both at rest and in transit. The platform ensures scalability without compromising security, with disaster recovery protocols and seamless integration with existing security tools.
Pinecone offers exceptional query speed and low-latency search, particularly well-suited for enterprise-grade workloads. It is tuned for high accuracy, with configurable trade-offs between recall and performance to meet specific needs. Storage efficiency is optimized through vector compression and scaling support, ensuring effective use of resources. The solution provides strong metadata support, making it ideal for enterprise and production-ready applications. It also offers a managed service with robust APIs, supporting most programming languages via SDKs.
While the managed service can come with higher costs, they are predictable, making it an excellent choice for businesses that need scalability without the concerns of managing infrastructure.
Qdrant is another open-source database with excellent performance. It boasts high recall rates using advanced ANN methods and customizable distance metrics. Storage efficiency is enhanced with compact design and support for hybrid search, combining vectors and filters. The system is highly flexible, offering dynamic updates, metadata search, and hybrid queries, which makes it versatile for various use cases.
Qdrant is highly compatible with Python and JavaScript, along with a simple API, making integration easy and straightforward. As an open-source solution, it is cost-effective for self-hosting. We recommend this tool to businesses or developers seeking an efficient, flexible vector database for AI and ML applications that require high performance, scalability, and ease of integration.
Weaviate leverages hybrid search and a distributed architecture for optimal efficiency. This tool focuses on high recall rates and supports various distance metrics and vector models for accurate results. Storage efficiency is enhanced through vector compression and modularity, making it both compact and scalable. The system also provides strong support for metadata, hybrid search, and real-time updates, offering great flexibility for diverse use cases.
With a user-friendly, API-first design, it seamlessly integrates with external machine learning models. As an open-source solution, it is cost-effective, making it a top choice for companies looking for large-scale or enterprise-grade deployments.
The database excels in integration within the MongoDB ecosystem, making it an excellent choice for general-purpose use cases. It offers seamless usability and works well for light vector workloads combined with traditional database needs. MongoDB Atlas provides managed services, though these can become costly for large datasets.
However, the database has some limitations. It performs decently for smaller datasets but is not optimized for high-scale vector workloads. In terms of accuracy, it lags behind dedicated VectorDBs, particularly in the flexibility of its approximate nearest neighbor (ANN) algorithms. Its storage efficiency is hindered by the use of MongoDB's general-purpose storage, which may not be ideal for dense vector indexing. Additionally, it offers limited vector-specific features and lacks advanced vector-native tooling. For businesses that are already integrated into the MongoDB ecosystem and looking to leverage light vector search alongside conventional database functionality, we recommend this tool for its seamless usability and cost-effective deployment.
Vespa mainly excels in accuracy for hybrid use cases, effectively combining structured data, text, and vector search to meet complex requirements. The system ensures storage efficiency with optimized indexing for both structured and unstructured data. It is highly flexible, supporting custom ranking algorithms and mixed workloads, though it requires more setup effort for advanced customization.
As an open-source solution, it is cost-effective for self-hosting, but can become resource-intensive for large clusters, making it more suitable for businesses that need robust performance and are prepared for the associated infrastructure costs. The system also requires more setup effort, making it less beginner-friendly.
Deep Lake specializes in handling unstructured and multimodal data, making it ideal for AI/ML applications. Its performance is tailored to unstructured data such as images and videos, delivering decent vector operations but with a primary focus on multimodal datasets. The system offers high recall, particularly when integrated deeply with multimodal data. Storage efficiency is optimized for large, unstructured datasets rather than just vectors, making it well-suited for complex AI/ML workflows. It integrates tightly with PyTorch and TensorFlow, supporting seamless AI pipeline integration.
As an open-source solution, it is affordable for self-hosting but may require additional tooling for large-scale operations. This is a database we’d recommend to companies who focus on multimodal AI/ML workflows, particularly those working with large, unstructured datasets such as images, videos, and audio.
Last on our list is Pgvector. This database came out in 2021 as an extension for PostgreSQL to enable native support for vector search. This extension allows PostgreSQL users to perform operations like similarity search on high-dimensional vectors, making it easier to integrate vector-based queries within a relational database environment.
Pgvector relies on basic vector search methods and lacks the advanced indexing options found in dedicated vector databases. It performs adequately for smaller datasets but is not optimized for high-speed or concurrent vector queries. Storage efficiency is also limited with its general-purpose database architecture. We recommend using this database if you’re working mostly with small datasets with mixed workloads such as relational and vector data, where high performance and scalability are not primary concerns.
About Shakudo
At Shakudo, we specialize in simplifying the integration of vector databases into enterprise infrastructures. Shakudo empowers teams to focus on innovation rather than infrastructure management, offering robust tools and support for handling large-scale vector data efficiently. Our platform accelerates the deployment of advanced vector search capabilities, enabling companies to unlock deeper insights and drive smarter decision-making across industries.
Vector database (Vector DB) has emerged as a powerful tool in recent years alongside the rapid growth of AI and machine learning technologies. It is essentially a data management system to store, search, and retrieve high-dimensional data generated by AI models, often in the form of texts, images, audio, or unstructured embeddings.
The popularity of AI, especially in the fields of computer vision and Natural Language Processing, has undeniably made vector DBs indispensable in managing the overwhelming volume of unstructured data produced by these AI systems. These systems, in turn, enable AI models to rapidly search for similar content for purposes such as recommendation engines, translation, and image recognition.
In this article, we cover the most recent professional vector DBs and examine their attributes, capacities, and how they are changing the business landscape with AI and big data. Whether you’re a tech expert who’s already working with vector databases, a business owner looking to stay competitive, or someone curious about how this technology can transform your work, we hope this list can help you identify the most current and well-suited vector DB that meets your unique requirements.
Milvus is an open-source vector database designed for handling massive-scale vector data. This vector database has excellent performance, with GPU acceleration, distributed querying, and efficient indexing. It is highly configurable and supports a range of indexing methods such as IVF, HNSW, and PQ, allowing users to balance accuracy and speed according to their needs. The database offers excellent scalability with efficient index storage and shard management, ensuring smooth growth as data volumes increase. With native support for multiple languages (Python, Java, Go, etc.) and integration with data pipelines like Kafka, Milvus also ensures seamless usability.
As an open-source solution, Milvus is cost-effective for on-premise use, though large-scale deployments may require substantial resources. For its flexibility in supporting real-time updates, hybrid search, and rich metadata handling, we recommend Milvus to enterprise-grade businesses looking to store and analyze large-scale data for insights across industries such as marketing, logistics, and customer analytics.
While Milvus provides powerful capabilities, managing and deploying it at scale can present challenges, especially for organizations without dedicated resources for database management. Shakudo can streamline the integration of Milvus into your existing infrastructure by offering a fully managed, cloud-based platform that simplifies deployment and scaling. With Shakudo, you can leverage Milvus’ high performance and advanced vector search capabilities without the operational complexity of managing the database yourself. Find Out How
Chroma is a vector DB designed specifically to query high-dimensional vector embeddings. Chroma has an intuitive API that simplifies integration into applications, making it accessible for developers and researchers without requiring extensive database management expertise.
Chroma delivers high accuracy with impressive recall rates, supporting embedding-based search and advanced ANN methods. While it offers compact storage, its storage efficiency is less robust for massive datasets compared to dedicated vector databases like Milvus. However, as an open-source option, it offers minimal deployment costs unless scaled heavily. This is a database we’d recommend for early-stage businesses with small-to-medium workloads, particularly for startups who are looking to experiment and prototype with AI models.
As businesses begin to scale their AI initiatives, they may find the need for more robust infrastructure and enhanced security. Shakudo helps secure companies’ integration of vector databases by providing built-in encryption and access control, ensuring data is protected both at rest and in transit. The platform ensures scalability without compromising security, with disaster recovery protocols and seamless integration with existing security tools.
Pinecone offers exceptional query speed and low-latency search, particularly well-suited for enterprise-grade workloads. It is tuned for high accuracy, with configurable trade-offs between recall and performance to meet specific needs. Storage efficiency is optimized through vector compression and scaling support, ensuring effective use of resources. The solution provides strong metadata support, making it ideal for enterprise and production-ready applications. It also offers a managed service with robust APIs, supporting most programming languages via SDKs.
While the managed service can come with higher costs, they are predictable, making it an excellent choice for businesses that need scalability without the concerns of managing infrastructure.
Qdrant is another open-source database with excellent performance. It boasts high recall rates using advanced ANN methods and customizable distance metrics. Storage efficiency is enhanced with compact design and support for hybrid search, combining vectors and filters. The system is highly flexible, offering dynamic updates, metadata search, and hybrid queries, which makes it versatile for various use cases.
Qdrant is highly compatible with Python and JavaScript, along with a simple API, making integration easy and straightforward. As an open-source solution, it is cost-effective for self-hosting. We recommend this tool to businesses or developers seeking an efficient, flexible vector database for AI and ML applications that require high performance, scalability, and ease of integration.
Weaviate leverages hybrid search and a distributed architecture for optimal efficiency. This tool focuses on high recall rates and supports various distance metrics and vector models for accurate results. Storage efficiency is enhanced through vector compression and modularity, making it both compact and scalable. The system also provides strong support for metadata, hybrid search, and real-time updates, offering great flexibility for diverse use cases.
With a user-friendly, API-first design, it seamlessly integrates with external machine learning models. As an open-source solution, it is cost-effective, making it a top choice for companies looking for large-scale or enterprise-grade deployments.
The database excels in integration within the MongoDB ecosystem, making it an excellent choice for general-purpose use cases. It offers seamless usability and works well for light vector workloads combined with traditional database needs. MongoDB Atlas provides managed services, though these can become costly for large datasets.
However, the database has some limitations. It performs decently for smaller datasets but is not optimized for high-scale vector workloads. In terms of accuracy, it lags behind dedicated VectorDBs, particularly in the flexibility of its approximate nearest neighbor (ANN) algorithms. Its storage efficiency is hindered by the use of MongoDB's general-purpose storage, which may not be ideal for dense vector indexing. Additionally, it offers limited vector-specific features and lacks advanced vector-native tooling. For businesses that are already integrated into the MongoDB ecosystem and looking to leverage light vector search alongside conventional database functionality, we recommend this tool for its seamless usability and cost-effective deployment.
Vespa mainly excels in accuracy for hybrid use cases, effectively combining structured data, text, and vector search to meet complex requirements. The system ensures storage efficiency with optimized indexing for both structured and unstructured data. It is highly flexible, supporting custom ranking algorithms and mixed workloads, though it requires more setup effort for advanced customization.
As an open-source solution, it is cost-effective for self-hosting, but can become resource-intensive for large clusters, making it more suitable for businesses that need robust performance and are prepared for the associated infrastructure costs. The system also requires more setup effort, making it less beginner-friendly.
Deep Lake specializes in handling unstructured and multimodal data, making it ideal for AI/ML applications. Its performance is tailored to unstructured data such as images and videos, delivering decent vector operations but with a primary focus on multimodal datasets. The system offers high recall, particularly when integrated deeply with multimodal data. Storage efficiency is optimized for large, unstructured datasets rather than just vectors, making it well-suited for complex AI/ML workflows. It integrates tightly with PyTorch and TensorFlow, supporting seamless AI pipeline integration.
As an open-source solution, it is affordable for self-hosting but may require additional tooling for large-scale operations. This is a database we’d recommend to companies who focus on multimodal AI/ML workflows, particularly those working with large, unstructured datasets such as images, videos, and audio.
Last on our list is Pgvector. This database came out in 2021 as an extension for PostgreSQL to enable native support for vector search. This extension allows PostgreSQL users to perform operations like similarity search on high-dimensional vectors, making it easier to integrate vector-based queries within a relational database environment.
Pgvector relies on basic vector search methods and lacks the advanced indexing options found in dedicated vector databases. It performs adequately for smaller datasets but is not optimized for high-speed or concurrent vector queries. Storage efficiency is also limited with its general-purpose database architecture. We recommend using this database if you’re working mostly with small datasets with mixed workloads such as relational and vector data, where high performance and scalability are not primary concerns.
About Shakudo
At Shakudo, we specialize in simplifying the integration of vector databases into enterprise infrastructures. Shakudo empowers teams to focus on innovation rather than infrastructure management, offering robust tools and support for handling large-scale vector data efficiently. Our platform accelerates the deployment of advanced vector search capabilities, enabling companies to unlock deeper insights and drive smarter decision-making across industries.
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Top 9 Vector Databases as of January 2025
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Vector database (Vector DB) has emerged as a powerful tool in recent years alongside the rapid growth of AI and machine learning technologies. It is essentially a data management system to store, search, and retrieve high-dimensional data generated by AI models, often in the form of texts, images, audio, or unstructured embeddings.
The popularity of AI, especially in the fields of computer vision and Natural Language Processing, has undeniably made vector DBs indispensable in managing the overwhelming volume of unstructured data produced by these AI systems. These systems, in turn, enable AI models to rapidly search for similar content for purposes such as recommendation engines, translation, and image recognition.
In this article, we cover the most recent professional vector DBs and examine their attributes, capacities, and how they are changing the business landscape with AI and big data. Whether you’re a tech expert who’s already working with vector databases, a business owner looking to stay competitive, or someone curious about how this technology can transform your work, we hope this list can help you identify the most current and well-suited vector DB that meets your unique requirements.
Milvus is an open-source vector database designed for handling massive-scale vector data. This vector database has excellent performance, with GPU acceleration, distributed querying, and efficient indexing. It is highly configurable and supports a range of indexing methods such as IVF, HNSW, and PQ, allowing users to balance accuracy and speed according to their needs. The database offers excellent scalability with efficient index storage and shard management, ensuring smooth growth as data volumes increase. With native support for multiple languages (Python, Java, Go, etc.) and integration with data pipelines like Kafka, Milvus also ensures seamless usability.
As an open-source solution, Milvus is cost-effective for on-premise use, though large-scale deployments may require substantial resources. For its flexibility in supporting real-time updates, hybrid search, and rich metadata handling, we recommend Milvus to enterprise-grade businesses looking to store and analyze large-scale data for insights across industries such as marketing, logistics, and customer analytics.
While Milvus provides powerful capabilities, managing and deploying it at scale can present challenges, especially for organizations without dedicated resources for database management. Shakudo can streamline the integration of Milvus into your existing infrastructure by offering a fully managed, cloud-based platform that simplifies deployment and scaling. With Shakudo, you can leverage Milvus’ high performance and advanced vector search capabilities without the operational complexity of managing the database yourself. Find Out How
Chroma is a vector DB designed specifically to query high-dimensional vector embeddings. Chroma has an intuitive API that simplifies integration into applications, making it accessible for developers and researchers without requiring extensive database management expertise.
Chroma delivers high accuracy with impressive recall rates, supporting embedding-based search and advanced ANN methods. While it offers compact storage, its storage efficiency is less robust for massive datasets compared to dedicated vector databases like Milvus. However, as an open-source option, it offers minimal deployment costs unless scaled heavily. This is a database we’d recommend for early-stage businesses with small-to-medium workloads, particularly for startups who are looking to experiment and prototype with AI models.
As businesses begin to scale their AI initiatives, they may find the need for more robust infrastructure and enhanced security. Shakudo helps secure companies’ integration of vector databases by providing built-in encryption and access control, ensuring data is protected both at rest and in transit. The platform ensures scalability without compromising security, with disaster recovery protocols and seamless integration with existing security tools.
Pinecone offers exceptional query speed and low-latency search, particularly well-suited for enterprise-grade workloads. It is tuned for high accuracy, with configurable trade-offs between recall and performance to meet specific needs. Storage efficiency is optimized through vector compression and scaling support, ensuring effective use of resources. The solution provides strong metadata support, making it ideal for enterprise and production-ready applications. It also offers a managed service with robust APIs, supporting most programming languages via SDKs.
While the managed service can come with higher costs, they are predictable, making it an excellent choice for businesses that need scalability without the concerns of managing infrastructure.
Qdrant is another open-source database with excellent performance. It boasts high recall rates using advanced ANN methods and customizable distance metrics. Storage efficiency is enhanced with compact design and support for hybrid search, combining vectors and filters. The system is highly flexible, offering dynamic updates, metadata search, and hybrid queries, which makes it versatile for various use cases.
Qdrant is highly compatible with Python and JavaScript, along with a simple API, making integration easy and straightforward. As an open-source solution, it is cost-effective for self-hosting. We recommend this tool to businesses or developers seeking an efficient, flexible vector database for AI and ML applications that require high performance, scalability, and ease of integration.
Weaviate leverages hybrid search and a distributed architecture for optimal efficiency. This tool focuses on high recall rates and supports various distance metrics and vector models for accurate results. Storage efficiency is enhanced through vector compression and modularity, making it both compact and scalable. The system also provides strong support for metadata, hybrid search, and real-time updates, offering great flexibility for diverse use cases.
With a user-friendly, API-first design, it seamlessly integrates with external machine learning models. As an open-source solution, it is cost-effective, making it a top choice for companies looking for large-scale or enterprise-grade deployments.
The database excels in integration within the MongoDB ecosystem, making it an excellent choice for general-purpose use cases. It offers seamless usability and works well for light vector workloads combined with traditional database needs. MongoDB Atlas provides managed services, though these can become costly for large datasets.
However, the database has some limitations. It performs decently for smaller datasets but is not optimized for high-scale vector workloads. In terms of accuracy, it lags behind dedicated VectorDBs, particularly in the flexibility of its approximate nearest neighbor (ANN) algorithms. Its storage efficiency is hindered by the use of MongoDB's general-purpose storage, which may not be ideal for dense vector indexing. Additionally, it offers limited vector-specific features and lacks advanced vector-native tooling. For businesses that are already integrated into the MongoDB ecosystem and looking to leverage light vector search alongside conventional database functionality, we recommend this tool for its seamless usability and cost-effective deployment.
Vespa mainly excels in accuracy for hybrid use cases, effectively combining structured data, text, and vector search to meet complex requirements. The system ensures storage efficiency with optimized indexing for both structured and unstructured data. It is highly flexible, supporting custom ranking algorithms and mixed workloads, though it requires more setup effort for advanced customization.
As an open-source solution, it is cost-effective for self-hosting, but can become resource-intensive for large clusters, making it more suitable for businesses that need robust performance and are prepared for the associated infrastructure costs. The system also requires more setup effort, making it less beginner-friendly.
Deep Lake specializes in handling unstructured and multimodal data, making it ideal for AI/ML applications. Its performance is tailored to unstructured data such as images and videos, delivering decent vector operations but with a primary focus on multimodal datasets. The system offers high recall, particularly when integrated deeply with multimodal data. Storage efficiency is optimized for large, unstructured datasets rather than just vectors, making it well-suited for complex AI/ML workflows. It integrates tightly with PyTorch and TensorFlow, supporting seamless AI pipeline integration.
As an open-source solution, it is affordable for self-hosting but may require additional tooling for large-scale operations. This is a database we’d recommend to companies who focus on multimodal AI/ML workflows, particularly those working with large, unstructured datasets such as images, videos, and audio.
Last on our list is Pgvector. This database came out in 2021 as an extension for PostgreSQL to enable native support for vector search. This extension allows PostgreSQL users to perform operations like similarity search on high-dimensional vectors, making it easier to integrate vector-based queries within a relational database environment.
Pgvector relies on basic vector search methods and lacks the advanced indexing options found in dedicated vector databases. It performs adequately for smaller datasets but is not optimized for high-speed or concurrent vector queries. Storage efficiency is also limited with its general-purpose database architecture. We recommend using this database if you’re working mostly with small datasets with mixed workloads such as relational and vector data, where high performance and scalability are not primary concerns.
About Shakudo
At Shakudo, we specialize in simplifying the integration of vector databases into enterprise infrastructures. Shakudo empowers teams to focus on innovation rather than infrastructure management, offering robust tools and support for handling large-scale vector data efficiently. Our platform accelerates the deployment of advanced vector search capabilities, enabling companies to unlock deeper insights and drive smarter decision-making across industries.
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