Alternatives to Milvus
Compare Milvus alternatives for your business or organization using the curated list below. SourceForge ranks the best alternatives to Milvus in 2026. Compare features, ratings, user reviews, pricing, and more from Milvus competitors and alternatives in order to make an informed decision for your business.
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Pinecone
Pinecone
The AI Knowledge Platform. The Pinecone Database, Inference, and Assistant make building high-performance vector search apps easy. Developer-friendly, fully managed, and easily scalable without infrastructure hassles. Once you have vector embeddings, manage and search through them in Pinecone to power semantic search, recommenders, and other applications that rely on relevant information retrieval. Ultra-low query latency, even with billions of items. Give users a great experience. Live index updates when you add, edit, or delete data. Your data is ready right away. Combine vector search with metadata filters for more relevant and faster results. Launch, use, and scale your vector search service with our easy API, without worrying about infrastructure or algorithms. We'll keep it running smoothly and securely. -
2
Zilliz Cloud
Zilliz
Zilliz Cloud is a fully managed vector database based on the popular open-source Milvus. Zilliz Cloud helps to unlock high-performance similarity searches with no previous experience or extra effort needed for infrastructure management. It is ultra-fast and enables 10x faster vector retrieval, a feat unparalleled by any other vector database management system. Zilliz includes support for multiple vector search indexes, built-in filtering, and complete data encryption in transit, a requirement for enterprise-grade applications. Zilliz is a cost-effective way to build similarity search, recommender systems, and anomaly detection into applications to keep that competitive edge.Starting Price: $0 -
3
Qdrant
Qdrant
Qdrant is a vector similarity engine & vector database. It deploys as an API service providing search for the nearest high-dimensional vectors. With Qdrant, embeddings or neural network encoders can be turned into full-fledged applications for matching, searching, recommending, and much more! Provides the OpenAPI v3 specification to generate a client library in almost any programming language. Alternatively utilise ready-made client for Python or other programming languages with additional functionality. Implement a unique custom modification of the HNSW algorithm for Approximate Nearest Neighbor Search. Search with a State-of-the-Art speed and apply search filters without compromising on results. Support additional payload associated with vectors. Not only stores payload but also allows filter results based on payload values. -
4
Azure AI Search
Microsoft
Deliver high-quality responses with a vector database built for advanced retrieval augmented generation (RAG) and modern search. Focus on exponential growth with an enterprise-ready vector database that comes with security, compliance, and responsible AI practices built in. Build better applications with sophisticated retrieval strategies backed by decades of research and customer validation. Quickly deploy your generative AI app with seamless platform and data integrations for data sources, AI models, and frameworks. Automatically upload data from a wide range of supported Azure and third-party sources. Streamline vector data processing with built-in extraction, chunking, enrichment, and vectorization, all in one flow. Support for multivector, hybrid, multilingual, and metadata filtering. Move beyond vector-only search with keyword match scoring, reranking, geospatial search, and autocomplete.Starting Price: $0.11 per hour -
5
Supabase
Supabase
Create a backend in less than 2 minutes. Start your project with a Postgres database, authentication, instant APIs, real-time subscriptions and storage. Build faster and focus on your products. Every project is a full Postgres database, the world's most trusted relational database. Add user sign-ups and logins, securing your data with Row Level Security. Store, organize and serve large files. Any media, including videos and images. Write custom code and cron jobs without deploying or scaling servers. There are many example apps and starter projects to get going. We introspect your database to provide APIs instantly. Stop building repetitive CRUD endpoints and focus on your product. Type definitions built directly from your database schema. Use Supabase in the browser without a build process. Develop locally and push to production when you're ready. Manage Supabase projects from your local machine.Starting Price: $25 per month -
6
Weaviate
Weaviate
Weaviate is an open-source vector database. It allows you to store data objects and vector embeddings from your favorite ML-models, and scale seamlessly into billions of data objects. Whether you bring your own vectors or use one of the vectorization modules, you can index billions of data objects to search through. Combine multiple search techniques, such as keyword-based and vector search, to provide state-of-the-art search experiences. Improve your search results by piping them through LLM models like GPT-3 to create next-gen search experiences. Beyond search, Weaviate's next-gen vector database can power a wide range of innovative apps. Perform lightning-fast pure vector similarity search over raw vectors or data objects, even with filters. Combine keyword-based search with vector search techniques for state-of-the-art results. Use any generative model in combination with your data, for example to do Q&A over your dataset.Starting Price: Free -
7
Vald
Vald
Vald is a highly scalable distributed fast approximate nearest neighbor dense vector search engine. Vald is designed and implemented based on the Cloud-Native architecture. It uses the fastest ANN Algorithm NGT to search neighbors. Vald has automatic vector indexing and index backup, and horizontal scaling which made for searching from billions of feature vector data. Vald is easy to use, feature-rich and highly customizable as you needed. Usually the graph requires locking during indexing, which cause stop-the-world. But Vald uses distributed index graph so it continues to work during indexing. Vald implements its own highly customizable Ingress/Egress filter. Which can be configured to fit the gRPC interface. Horizontal scalable on memory and cpu for your demand. Vald supports to auto backup feature using Object Storage or Persistent Volume which enables disaster recovery.Starting Price: Free -
8
Vespa
Vespa.ai
Vespa is forBig Data + AI, online. At any scale, with unbeatable performance. To build production-worthy online applications that combine data and AI, you need more than point solutions: You need a platform that integrates data and compute to achieve true scalability and availability - and which does this without limiting your freedom to innovate. Only Vespa does this. Vespa is a fully featured search engine and vector database. It supports vector search (ANN), lexical search, and search in structured data, all in the same query. Users can easily build recommendation applications on Vespa. Integrated machine-learned model inference allows you to apply AI to make sense of your data in real-time. Together with Vespa's proven scaling and high availability, this empowers you to create production-ready search applications at any scale and with any combination of features.Starting Price: Free -
9
Chroma
Chroma
Chroma is an AI-native open-source embedding database. Chroma has all the tools you need to use embeddings. Chroma is building the database that learns. Pick up an issue, create a PR, or participate in our Discord and let the community know what features you would like.Starting Price: Free -
10
Embeddinghub
Featureform
Operationalize your embeddings with one simple tool. Experience a comprehensive database designed to provide embedding functionality that, until now, required multiple platforms. Elevate your machine learning quickly and painlessly through Embeddinghub. Embeddings are dense, numerical representations of real-world objects and relationships, expressed as vectors. They are often created by first defining a supervised machine learning problem, known as a "surrogate problem." Embeddings intend to capture the semantics of the inputs they were derived from, subsequently getting shared and reused for improved learning across machine learning models. Embeddinghub lets you achieve this in a streamlined, intuitive way.Starting Price: Free -
11
Faiss
Meta
Faiss is a library for efficient similarity search and clustering of dense vectors. It contains algorithms that search in sets of vectors of any size, up to ones that possibly do not fit in RAM. It also contains supporting code for evaluation and parameter tuning. Faiss is written in C++ with complete wrappers for Python. Some of the most useful algorithms are implemented on the GPU. It is developed by Facebook AI Research.Starting Price: Free -
12
LanceDB
LanceDB
LanceDB is a developer-friendly, open source database for AI. From hyperscalable vector search and advanced retrieval for RAG to streaming training data and interactive exploration of large-scale AI datasets, LanceDB is the best foundation for your AI application. Installs in seconds and fits seamlessly into your existing data and AI toolchain. An embedded database (think SQLite or DuckDB) with native object storage integration, LanceDB can be deployed anywhere and easily scales to zero when not in use. From rapid prototyping to hyper-scale production, LanceDB delivers blazing-fast performance for search, analytics, and training for multimodal AI data. Leading AI companies have indexed billions of vectors and petabytes of text, images, and videos, at a fraction of the cost of other vector databases. More than just embedding. Filter, select, and stream training data directly from object storage to keep GPU utilization high.Starting Price: $16.03 per month -
13
Amazon S3 Vectors
Amazon
Amazon S3 Vectors is the first cloud object store with native support for storing and querying vector embeddings at scale, delivering purpose-built, cost-optimized vector storage for semantic search, AI agents, retrieval-augmented generation, and similarity-search applications. It introduces a new “vector bucket” type in S3, where users can organize vectors into “vector indexes,” store high-dimensional embeddings (representing text, images, audio, or other unstructured data), and run similarity queries via dedicated APIs, all without provisioning infrastructure. Each vector may carry metadata (e.g., tags, timestamps, categories), enabling filtered queries by attributes. S3 Vectors offers massive scale; now generally available, it supports up to 2 billion vectors per index and up to 10,000 vector indexes per bucket, with elastic, durable storage and server-side encryption (SSE-S3 or optionally KMS). -
14
Vectorize
Vectorize
Vectorize is a platform designed to transform unstructured data into optimized vector search indexes, facilitating retrieval-augmented generation pipelines. It enables users to import documents or connect to external knowledge management systems, allowing Vectorize to extract natural language suitable for LLMs. The platform evaluates multiple chunking and embedding strategies in parallel, providing recommendations or allowing users to choose their preferred methods. Once a vector configuration is selected, Vectorize deploys it into a real-time vector pipeline that automatically updates with any data changes, ensuring accurate search results. The platform offers connectors to various knowledge repositories, collaboration platforms, and CRMs, enabling seamless integration of data into generative AI applications. Additionally, Vectorize supports the creation and updating of vector indexes in preferred vector databases.Starting Price: $0.57 per hour -
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Cloudflare Vectorize
Cloudflare
Begin building for free in minutes. Vectorize enables fast & cost-effective vector storage to power your search & AI Retrieval Augmented Generation (RAG) applications. Avoid tool sprawl & reduce total cost of ownership, Vectorize seamlessly integrates with Cloudflare’s AI developer platform and AI gateway for centralized development, monitoring & control of AI applications on a global scale. Vectorize is a globally distributed vector database that enables you to build full-stack, AI-powered applications with Cloudflare Workers AI. Vectorize makes querying embeddings, representations of values or objects like text, images, and audio that are designed to be consumed by machine learning models and semantic search algorithms, faster, easier, and more affordable. Search, similarity, recommendation, classification & anomaly detection based on your own data. Improved results & faster search. String, number & boolean types are supported. -
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MyScale
MyScale
MyScale is an innovative AI database that seamlessly integrates vector search with SQL analytics, delivering a comprehensive, fully managed, and high-performance solution. Key Features: - Superior Data Capacity and Performance: Each MyScale pod supports 5 million 768-dimensional data points with exceptional accuracy, enabling over 150 queries per second (QPS). - Rapid Data Ingestion: Import up to 5 million data points in under 30 minutes, reducing waiting time and enabling faster utilization of your vector data. - Flexible Indexing: MyScale allows you to create multiple tables with unique vector indexes, efficiently managing diverse vector data within a single cluster. - Effortless Data Import and Backup: Seamlessly import/export data from/to S3 or other compatible storage systems, ensuring smooth data management and backup processes. With MyScale, unleash the power of advanced AI database capabilities for efficient and effective data analysis. -
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txtai
NeuML
txtai is an all-in-one open source embeddings database designed for semantic search, large language model orchestration, and language model workflows. It unifies vector indexes (both sparse and dense), graph networks, and relational databases, providing a robust foundation for vector search and serving as a powerful knowledge source for LLM applications. With txtai, users can build autonomous agents, implement retrieval augmented generation processes, and develop multi-modal workflows. Key features include vector search with SQL support, object storage integration, topic modeling, graph analysis, and multimodal indexing capabilities. It supports the creation of embeddings for various data types, including text, documents, audio, images, and video. Additionally, txtai offers pipelines powered by language models that handle tasks such as LLM prompting, question-answering, labeling, transcription, translation, and summarization.Starting Price: Free -
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Couchbase
Couchbase
Unlike other NoSQL databases, Couchbase provides an enterprise-class, multicloud to edge database that offers the robust capabilities required for business-critical applications on a highly scalable and available platform. As a distributed cloud-native database, Couchbase runs in modern dynamic environments and on any cloud, either customer-managed or fully managed as-a-service. Couchbase is built on open standards, combining the best of NoSQL with the power and familiarity of SQL, to simplify the transition from mainframe and relational databases. Couchbase Server is a multipurpose, distributed database that fuses the strengths of relational databases such as SQL and ACID transactions with JSON’s versatility, with a foundation that is extremely fast and scalable. It’s used across industries for things like user profiles, dynamic product catalogs, GenAI apps, vector search, high-speed caching, and much more. -
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CrateDB
CrateDB
The enterprise database for time series, documents, and vectors. Store any type of data and combine the simplicity of SQL with the scalability of NoSQL. CrateDB is an open source distributed database running queries in milliseconds, whatever the complexity, volume and velocity of data. -
20
Marqo
Marqo
Marqo is more than a vector database, it's an end-to-end vector search engine. Vector generation, storage, and retrieval are handled out of the box through a single API. No need to bring your own embeddings. Accelerate your development cycle with Marqo. Index documents and begin searching in just a few lines of code. Create multimodal indexes and search combinations of images and text with ease. Choose from a range of open source models or bring your own. Build interesting and complex queries with ease. With Marqo you can compose queries with multiple weighted components. With Marqo, input pre-processing, machine learning inference, and storage are all included out of the box. Run Marqo in a Docker image on your laptop or scale it up to dozens of GPU inference nodes in the cloud. Marqo can be scaled to provide low-latency searches against multi-terabyte indexes. Marqo helps you configure deep-learning models like CLIP to pull semantic meaning from images.Starting Price: $86.58 per month -
21
SuperDuperDB
SuperDuperDB
Build and manage AI applications easily without needing to move your data to complex pipelines and specialized vector databases. Integrate AI and vector search directly with your database including real-time inference and model training. A single scalable deployment of all your AI models and APIs which is automatically kept up-to-date as new data is processed immediately. No need to introduce an additional database and duplicate your data to use vector search and build on top of it. SuperDuperDB enables vector search in your existing database. Integrate and combine models from Sklearn, PyTorch, and HuggingFace with AI APIs such as OpenAI to build even the most complex AI applications and workflows. Deploy all your AI models to automatically compute outputs (inference) in your datastore in a single environment with simple Python commands. -
22
VectorDB
VectorDB
VectorDB is a lightweight Python package for storing and retrieving text using chunking, embedding, and vector search techniques. It provides an easy-to-use interface for saving, searching, and managing textual data with associated metadata and is designed for use cases where low latency is essential. Vector search and embeddings are essential when working with large language models because they enable efficient and accurate retrieval of relevant information from massive datasets. By converting text into high-dimensional vectors, these techniques allow for quick comparisons and searches, even when dealing with millions of documents. This makes it possible to find the most relevant results in a fraction of the time it would take using traditional text-based search methods. Additionally, embeddings capture the semantic meaning of the text, which helps improve the quality of the search results and enables more advanced natural language processing tasks.Starting Price: Free -
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BilberryDB
BilberryDB
BilberryDB is an enterprise-grade vector-database platform designed for building AI applications that handle multimodal data, including images, video, audio, 3D models, tabular data, and text, across one unified system. It supports lightning-fast similarity search and retrieval via embeddings, allows few-shot or no-code workflows to create powerful search/classification capabilities without large labelled datasets, and offers a developer SDK (such as TypeScript) as well as a visual builder for non-technical users. The platform emphasises sub-second query performance at scale, seamless ingestion of diverse data types, and rapid deployment of vector-search-enabled apps (“Deploy as an App”) so organisations can build AI-driven search, recommendation, classification, or content-discovery systems without building infrastructure from scratch.Starting Price: Free -
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ZeusDB
ZeusDB
ZeusDB is a next-generation, high-performance data platform designed to handle the demands of modern analytics, machine learning, real-time insights, and hybrid data workloads. It supports vector, structured, and time-series data in one unified engine, allowing recommendation systems, semantic search, retrieval-augmented generation pipelines, live dashboards, and ML model serving to operate from a single store. The platform delivers ultra-low latency querying and real-time analytics, eliminating the need for separate databases or caching layers. Developers and data engineers can extend functionality with Rust or Python logic, deploy on-premises, hybrid, or cloud, and operate under GitOps/CI-CD patterns with observability built in. With built-in vector indexing (e.g., HNSW), metadata filtering, and powerful query semantics, ZeusDB enables similarity search, hybrid retrieval, filtering, and rapid application iteration. -
25
KDB.AI
KX Systems
KDB.AI is a powerful knowledge-based vector database and search engine that allows developers to build scalable, reliable and real-time applications by providing advanced search, recommendation and personalization for AI applications. Vector databases are a new wave of data management designed for generative AI, IoT and time-series applications. Here's why they matter, what makes them different, how they work, the new use cases they're designed for, and how to get started. -
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Oracle Autonomous Database
Oracle
Oracle Autonomous Database is a fully automated cloud database that uses machine learning to automate database tuning, security, backups, updates, and other routine management tasks traditionally performed by DBAs. It supports a wide range of data types and models, including SQL, JSON documents, graph, geospatial, text, and vectors, enabling developers to build applications for any workload without integrating multiple specialty databases. Built-in AI and machine learning capabilities allow for natural language queries, automated data insights, and the development of AI-powered applications. It offers self-service tools for data loading, transformation, analysis, and governance, reducing the need for IT intervention. It provides flexible deployment options, including serverless and dedicated infrastructure on Oracle Cloud Infrastructure (OCI), as well as on-premises with Exadata Cloud@Customer.Starting Price: $123.86 per month -
27
Flowise
Flowise AI
Flowise is an open-source, low-code platform that enables developers to create customized Large Language Model (LLM) applications through a user-friendly drag-and-drop interface. It supports integration with various LLMs, including LangChain and LlamaIndex, and offers over 100 integrations to facilitate the development of AI agents and orchestration flows. Flowise provides APIs, SDKs, and embedded widgets for seamless incorporation into existing systems, and is platform-agnostic, allowing deployment in air-gapped environments with local LLMs and vector databases.Starting Price: Free -
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TopK
TopK
TopK is a serverless, cloud-native, document database built for powering search applications. It features native support for both vector search (vectors are simply another data type) and keyword search (BM25-style) in a single, unified system. With its powerful query expression language, TopK enables you to build reliable search applications (semantic search, RAG, multi-modal, you name it) without juggling multiple databases or services. Our unified retrieval engine will evolve to support document transformation (automatically generate embeddings), query understanding (parse metadata filters from user query), and adaptive ranking (provide more relevant results by sending “relevance feedback” back to TopK) under one unified roof. -
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pgvector
pgvector
Open-source vector similarity search for Postgres. Supports exact and approximate nearest neighbor search for L2 distance, inner product, and cosine distance.Starting Price: Free -
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ApertureDB
ApertureDB
Build your competitive edge with the power of vector search. Streamline your AI/ML pipeline workflows, reduce infrastructure costs, and stay ahead of the curve with up to 10x faster time-to-market. Break free of data silos with ApertureDB's unified multimodal data management, freeing your AI teams to innovate. Set up and scale complex multimodal data infrastructure for billions of objects across your entire enterprise in days, not months. Unifying multimodal data, advanced vector search, and innovative knowledge graph with a powerful query engine to build AI applications faster at enterprise scale. ApertureDB can enhance the productivity of your AI/ML teams and accelerate returns from AI investment with all your data. Try it for free or schedule a demo to see it in action. Find relevant images based on labels, geolocation, and regions of interest. Prepare large-scale multi-modal medical scans for ML and clinical studies.Starting Price: $0.33 per hour -
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Astra DB
DataStax
Astra DB from DataStax is vector database for developers that need to get accurate Generative AI applications into production, quickly and efficiently. Built on Apache Cassandra, Astra DB is the only vector database that can make vector updates immediately available to applications and scale to the largest real-time data and streaming workloads, securely on any cloud. Astra DB offers unprecedented serverless, pay as you go pricing and the flexibility of multi-cloud and open-source. You can store up to 80GB and/or perform 20 million operations per month. Securely connect to VPC peering and private links. Manage your encryption keys with your own key management and SAML SSO secure account accessibility. You can deploy on AWS, GCP, or Azure while still maintaining open-source Cassandra compatibility. -
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LlamaIndex
LlamaIndex
LlamaIndex is a “data framework” to help you build LLM apps. Connect semi-structured data from API's like Slack, Salesforce, Notion, etc. LlamaIndex is a simple, flexible data framework for connecting custom data sources to large language models. LlamaIndex provides the key tools to augment your LLM applications with data. Connect your existing data sources and data formats (API's, PDF's, documents, SQL, etc.) to use with a large language model application. Store and index your data for different use cases. Integrate with downstream vector store and database providers. LlamaIndex provides a query interface that accepts any input prompt over your data and returns a knowledge-augmented response. Connect unstructured sources such as documents, raw text files, PDF's, videos, images, etc. Easily integrate structured data sources from Excel, SQL, etc. Provides ways to structure your data (indices, graphs) so that this data can be easily used with LLMs. -
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Superlinked
Superlinked
Combine semantic relevance and user feedback to reliably retrieve the optimal document chunks in your retrieval augmented generation system. Combine semantic relevance and document freshness in your search system, because more recent results tend to be more accurate. Build a real-time personalized ecommerce product feed with user vectors constructed from SKU embeddings the user interacted with. Discover behavioral clusters of your customers using a vector index in your data warehouse. Describe and load your data, use spaces to construct your indices and run queries - all in-memory within a Python notebook. -
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Azure Managed Redis
Microsoft
Azure Managed Redis features the latest Redis innovations, industry-leading availability, and a cost-effective Total Cost of Ownership (TCO) designed for the hyperscale cloud. Azure Managed Redis delivers these capabilities on a trusted cloud platform, empowering businesses to scale and optimize their generative AI applications seamlessly. Azure Managed Redis brings the latest Redis innovations to support high-performance, scalable AI applications. With features like in-memory data storage, vector similarity search, and real-time processing, it enables developers to handle large datasets efficiently, accelerate machine learning, and build faster AI solutions. Its interoperability with Azure OpenAI Service enables AI workloads to be faster, scalable, and ready for mission-critical use cases, making it an ideal choice for building modern, intelligent applications. -
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Nomic Atlas
Nomic AI
Atlas integrates into your workflow by organizing text and embedding datasets into interactive maps for exploration in a web browser. You shouldn’t have to scroll through Excel files, log Dataframes and page through lists to understand your data. Atlas automatically reads, organizes and summarizes your collections of documents surfacing trends and patterns. Atlas’ pre-organized data interface allows you to quickly surface pathologies and dirty data that can jeopardize your AI projects. Label and tag your data while you clean it with immediate sync to your Jupyter Notebook. Vector databases enable powerful applications such as recommendation systems but are notoriously hard to interpret. Atlas stores, visualizes and lets you search through all of your vectors in the same API.Starting Price: $50 per month -
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Turso
Turso
Turso is a globally distributed, SQLite-compatible database service designed to provide low-latency data access across various platforms, including online, offline, and on-device environments. Built atop libSQL, an open-source fork of SQLite, Turso enables developers to deploy databases close to their users, enhancing application performance. It supports seamless integration with multiple frameworks, languages, and infrastructure providers, facilitating efficient data management for applications such as personalized large language models and AI agents. Turso offers features like unlimited databases, instant rollback with branching, and native vector search at scale, allowing for efficient parallel vector searches across users, instances, or contexts using SQL database integration. The platform emphasizes security with encryption at rest and in transit and provides an API-first approach for programmatic database management.Starting Price: $8.25 per month -
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Deep Lake
activeloop
Generative AI may be new, but we've been building for this day for the past 5 years. Deep Lake thus combines the power of both data lakes and vector databases to build and fine-tune enterprise-grade, LLM-based solutions, and iteratively improve them over time. Vector search does not resolve retrieval. To solve it, you need a serverless query for multi-modal data, including embeddings or metadata. Filter, search, & more from the cloud or your laptop. Visualize and understand your data, as well as the embeddings. Track & compare versions over time to improve your data & your model. Competitive businesses are not built on OpenAI APIs. Fine-tune your LLMs on your data. Efficiently stream data from remote storage to the GPUs as models are trained. Deep Lake datasets are visualized right in your browser or Jupyter Notebook. Instantly retrieve different versions of your data, materialize new datasets via queries on the fly, and stream them to PyTorch or TensorFlow.Starting Price: $995 per month -
38
SciPhi
SciPhi
Intuitively build your RAG system with fewer abstractions compared to solutions like LangChain. Choose from a wide range of hosted and remote providers for vector databases, datasets, Large Language Models (LLMs), application integrations, and more. Use SciPhi to version control your system with Git and deploy from anywhere. The platform provided by SciPhi is used internally to manage and deploy a semantic search engine with over 1 billion embedded passages. The team at SciPhi will assist in embedding and indexing your initial dataset in a vector database. The vector database is then integrated into your SciPhi workspace, along with your selected LLM provider.Starting Price: $249 per month -
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Baidu Natural Language Processing, based on Baidu’s immense data accumulation, is devoted to developing cutting-edge natural language processing and knowledge graph technologies. Natural Language Processing has open several core abilities and solutions, including more than ten kinds of abilities such as sentiment analysis, address recognition, and customer comments analysis. Based on word segmentation, part-of-speech tagging, and named entity recognition technology, lexical analysis allows you to locate basic language elements, get rid of ambiguity, and support accurate understanding. Based on deep neural networks and massive high-quality data on the internet, semantic similarity is possible to calculate the similarity of two words through vectorization of words, meeting the business scenario requirements for high precision. Word vector representation can calculate texts through the vectorization of words and it can help you quickly complete semantic mining.
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40
Metal
Metal
Metal is your production-ready, fully-managed, ML retrieval platform. Use Metal to find meaning in your unstructured data with embeddings. Metal is a managed service that allows you to build AI products without the hassle of managing infrastructure. Integrations with OpenAI, CLIP, and more. Easily process & chunk your documents. Take advantage of our system in production. Easily plug into the MetalRetriever. Simple /search endpoint for running ANN queries. Get started with a free account. Metal API Keys to use our API & SDKs. With your API Key, you can use authenticate by populating the headers. Learn how to use our Typescript SDK to implement Metal into your application. Although we love TypeScript, you can of course utilize this library in JavaScript. Mechanism to fine-tune your spp programmatically. Indexed vector database of your embeddings. Resources that represent your specific ML use-case.Starting Price: $25 per month -
41
Oracle Real Application Clusters (RAC) is a unique, scale-everything, highly available database architecture that transparently scales both reads and writes for all workloads, including OLTP, analytics, AI vectors, SaaS, JSON, batch, text, graph, IoT, and in-memory. It effortlessly scales complex applications such as SAP, Oracle Fusion Applications, and Salesforce workloads. Oracle RAC delivers the lowest latency and highest throughput for all data needs through its unique fused cache across servers, ensuring ultrafast local data access. Parallelized workloads across all CPUs guarantee maximum throughput, and the integration of Oracle’s storage design enables seamless online storage expansion. Unlike other databases that depend on public cloud infrastructures, sharding, or read replicas for scalability, Oracle RAC guarantees the lowest latency and highest throughput out of the box.
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42
Mixedbread
Mixedbread
Mixedbread is a fully-managed AI search engine that allows users to build production-ready AI search and Retrieval-Augmented Generation (RAG) applications. It offers a complete AI search stack, including vector stores, embedding and reranking models, and document parsing. Users can transform raw data into intelligent search experiences that power AI agents, chatbots, and knowledge systems without the complexity. It integrates with tools like Google Drive, SharePoint, Notion, and Slack. Its vector stores enable users to build production search engines in minutes, supporting over 100 languages. Mixedbread's embedding and reranking models have achieved over 50 million downloads and outperform OpenAI in semantic search and RAG tasks while remaining open-source and cost-effective. The document parser extracts text, tables, and layouts from PDFs, images, and complex documents, providing clean, AI-ready content without manual preprocessing. -
43
Apache Doris
The Apache Software Foundation
Apache Doris is a modern data warehouse for real-time analytics. It delivers lightning-fast analytics on real-time data at scale. Push-based micro-batch and pull-based streaming data ingestion within a second. Storage engine with real-time upsert, append and pre-aggregation. Optimize for high-concurrency and high-throughput queries with columnar storage engine, MPP architecture, cost based query optimizer, vectorized execution engine. Federated querying of data lakes such as Hive, Iceberg and Hudi, and databases such as MySQL and PostgreSQL. Compound data types such as Array, Map and JSON. Variant data type to support auto data type inference of JSON data. NGram bloomfilter and inverted index for text searches. Distributed design for linear scalability. Workload isolation and tiered storage for efficient resource management. Supports shared-nothing clusters as well as separation of storage and compute.Starting Price: Free -
44
ParadeDB
ParadeDB
ParadeDB brings column-oriented storage and vectorized query execution to Postgres tables. Users can choose between row and column-oriented storage at table creation time. Column-oriented tables are stored as Parquet files and are managed by Delta Lake. Search by keyword with BM25 scoring, configurable tokenizers, and multi-language support. Search by semantic meaning with support for sparse and dense vectors. Surface results with higher accuracy by combining the strengths of full text and similarity search. ParadeDB is ACID-compliant with concurrency controls across all transactions. ParadeDB integrates with the Postgres ecosystem, including clients, extensions, and libraries. -
45
Databend
Databend
Databend is a modern, cloud-native data warehouse built to deliver high-performance, cost-efficient analytics for large-scale data processing. It is designed with an elastic architecture that scales dynamically to meet the demands of different workloads, ensuring efficient resource utilization and lower operational costs. Written in Rust, Databend offers exceptional performance through features like vectorized query execution and columnar storage, which optimize data retrieval and processing speeds. Its cloud-first design enables seamless integration with cloud platforms, and it emphasizes reliability, data consistency, and fault tolerance. Databend is an open source solution, making it a flexible and accessible choice for data teams looking to handle big data analytics in the cloud.Starting Price: Free -
46
Rebuff AI
Rebuff AI
Store embeddings of previous attacks in a vector database to recognize and prevent similar attacks in the future. Use a dedicated LLM to analyze incoming prompts and identify potential attacks. Add canary tokens to prompts to detect leakages, allowing the framework to store embeddings about the incoming prompt in the vector database and prevent future attacks. Filter out potentially malicious input before it reaches the LLM. -
47
Kinetica
Kinetica
A scalable cloud database for real-time analysis on large and streaming datasets. Kinetica is designed to harness modern vectorized processors to be orders of magnitude faster and more efficient for real-time spatial and temporal workloads. Track and gain intelligence from billions of moving objects in real-time. Vectorization unlocks new levels of performance for analytics on spatial and time series data at scale. Ingest and query at the same time to act on real-time events. Kinetica's lockless architecture and distributed ingestion ensures data is available to query as soon as it lands. Vectorized processing enables you to do more with less. More power allows for simpler data structures, which lead to lower storage costs, more flexibility and less time engineering your data. Vectorized processing opens the door to amazingly fast analytics and detailed visualization of moving objects at scale. -
48
ConfidentialMind
ConfidentialMind
We've done the work of bundling and pre-configuring all the components you need for building solutions and integrating LLMs directly into your business processes. With ConfidentialMind you can jump right into action. Deploys an endpoint for the most powerful open source LLMs like Llama-2, turning it into an internal LLM API. Imagine ChatGPT in your very own cloud. This is the most secure solution possible. Connects the rest of the stack with the APIs of the largest hosted LLM providers like Azure OpenAI, AWS Bedrock, or IBM. ConfidentialMind deploys a playground UI based on Streamlit with a selection of LLM-powered productivity tools for your company such as writing assistants and document analysts. Includes a vector database, critical components for the most common LLM applications for shifting through massive knowledge bases with thousands of documents efficiently. Allows you to control the access to the solutions your team builds and what data the LLMs have access to. -
49
Cloaked AI
IronCore Labs
Cloaked AI protects sensitive AI data by encrypting it, but keeping it usable. Vector embeddings in vector databases can be encrypted without losing functionality such that only someone with the proper key can search the vectors. It prevents inversion attacks and other AI attacks on RAG systems, facial recognition systems, and more.Starting Price: $599/month -
50
FalkorDB
FalkorDB
FalkorDB is an ultra-fast, multi-tenant graph database optimized for GraphRAG, delivering accurate, relevant AI/ML results with reduced hallucinations and enhanced performance. It leverages sparse matrix representations and linear algebra to efficiently handle complex, interconnected data in real-time, resulting in fewer hallucinations and more accurate responses from large language models. FalkorDB supports the OpenCypher query language with proprietary enhancements, enabling expressive and efficient querying of graph data. It offers built-in vector indexing and full-text search capabilities, allowing for complex searches and similarity matching within the same database environment. FalkorDB's architecture includes multi-graph support, enabling multiple isolated graphs within a single instance, ensuring security and performance across tenants. It also provides high availability with live replication, ensuring data is always accessible.
