Alternatives to Vald

Ambassador Edge Stack, a Kubernetes-native API Gateway, provides simplicity, security, and scalability for some of the largest Kubernetes infrastructures in the world. Ambassador Edge Stack makes it easy to secure microservices with a complete set of security functionality including automatic TLS, authentication and rate limiting. WAF integration is also available. Fine-grained access control is also possible. The API Gateway is a Kubernetes-based ingress controller that supports a wide range of protocols, including gRPC, gRPC Web, TLS termination, and traffic management controls to ensure resource availability.

Qdrant is a vector database and similarity engine. It is an API service that allows you to search for the closest high-dimensional vectors. Qdrant allows embeddings and neural network encoders to be transformed into full-fledged apps for matching, searching, recommending, etc. This specification provides the OpenAPI version 3 specification to create a client library for almost any programming language. You can also use a ready-made client for Python, or other programming languages that has additional functionality. For Approximate Nearest Neighbor Search, you can make a custom modification to the HNSW algorithm. Search at a State of the Art speed and use search filters to maximize results. Additional payload can be associated with vectors. Allows you to store payload and filter results based upon payload values.

Artificial intelligence long-term memory The Pinecone vector database makes building high-performance vector search apps easy. Fully managed and developer-friendly, the database is easily scalable without any infrastructure problems. Once you have vector embeddings created, you can search and manage them in Pinecone to power semantic searches, recommenders, or other applications that rely upon relevant information retrieval. Even with billions of items, ultra-low query latency Provide a great user experience. You can add, edit, and delete data via live index updates. Your data is available immediately. For more relevant and quicker results, combine vector search with metadata filters. Our API makes it easy to launch, use, scale, and scale your vector searching service without worrying about infrastructure. It will run smoothly and securely.

One tool allows you to operationalize your embeddings. A comprehensive database that provides embedding functionality previously unavailable on multiple platforms is now available to you. Embeddinghub makes it easy to accelerate your machine learning. Embeddings are dense numerical representations of real world objects and relationships. They can be expressed as vectors. They are often created by first defining an unsupervised machine learning problem, also known as a "surrogate issue". Embeddings are intended to capture the semantics from the inputs they were derived. They can then be shared and reused for better learning across machine learning models. This is possible with Embeddinghub in an intuitive and streamlined way.

Searching and analyzing structured data is easy; however, over 80% of generated data is unstructured, requiring a different approach. Machine learning converts unstructured data into high-dimensional vectors of numerical values, which makes it possible to find patterns or relationships within that data type. Unfortunately, traditional databases were never meant to store vectors or embeddings and can not meet unstructured data's scalability and performance requirements. Zilliz Cloud is a cloud-native vector database that stores, indexes, and searches for billions of embedding vectors to power enterprise-grade similarity search, recommender systems, anomaly detection, and more. Zilliz Cloud, built on the popular open-source vector database Milvus, allows for easy integration with vectorizers from OpenAI, Cohere, HuggingFace, and other popular models. Purpose-built to solve the challenge of managing billions of embeddings, Zilliz Cloud makes it easy to build applications for scale.

Vespa is forBig Data + AI, online. At any scale, with unbeatable performance. 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. Integrated machine-learned model inference allows you to apply AI to make sense of your data in real-time. Users build recommendation applications on Vespa, typically combining fast vector search and filtering with evaluation of machine-learned models over the items. 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. 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.

Chroma is an AI-native, open-source embedding system. Chroma provides all the tools needed to embeddings. Chroma is creating the database that learns. You can pick up an issue, create PRs, or join our Discord to let the community know your ideas.

Postgres: Open-source vector similarity search Supports exact and approximate closest neighbor search for L2 distances, inner product and cosine distances.

A vector database designed for scalable similarity searches. Open-source, highly scalable and lightning fast. Massive embedding vectors created by deep neural networks or other machine learning (ML), can be stored, indexed, and managed. Milvus vector database makes it easy to create large-scale similarity search services in under a minute. For a variety languages, there are simple and intuitive SDKs. Milvus is highly efficient on hardware and offers advanced indexing algorithms that provide a 10x speed boost in retrieval speed. Milvus vector database is used in a variety a use cases by more than a thousand enterprises. Milvus is extremely resilient and reliable due to its isolation of individual components. Milvus' distributed and high-throughput nature makes it an ideal choice for large-scale vector data. Milvus vector database uses a systemic approach for cloud-nativity that separates compute and storage.

Faiss is a library that allows for efficient similarity searches and clustering dense vectors. It has algorithms that can search for vectors of any size. It also includes supporting code for parameter tuning and evaluation. Faiss is written entirely in C++ and includes wrappers for Python. The GPU is home to some of the most powerful algorithms. It was developed by Facebook AI Research.

MyScale is a cutting-edge AI database that combines vector search with SQL analytics, offering a seamless, fully managed, and high-performance solution. Key features of MyScale include: - Enhanced data capacity and performance: Each standard MyScale pod supports 5 million 768-dimensional data points with exceptional accuracy, delivering over 150 QPS. - Swift data ingestion: Ingest up to 5 million data points in under 30 minutes, minimizing wait times and enabling faster serving of your vector data. - Flexible index support: MyScale allows you to create multiple tables, each with its own unique vector indexes, empowering you to efficiently manage heterogeneous vector data within a single MyScale cluster. - Seamless data import and backup: Effortlessly import and export data from and to S3 or other compatible storage systems, ensuring smooth data management and backup processes. With MyScale, you can harness the power of advanced AI database capabilities for efficient and effective data analysis.

Metal is a fully-managed, production-ready ML retrieval platform. Metal embeddings can help you find meaning in unstructured data. Metal is a managed services that allows you build AI products without having to worry about managing infrastructure. Integrations with OpenAI and CLIP. Easy processing & chunking of your documents. Profit from our system in production. MetalRetriever is easily pluggable. Simple /search endpoint to run ANN queries. Get started for free. Metal API Keys are required to use our API and SDKs. Authenticate by populating headers with your API Key. Learn how to integrate Metal into your application using our Typescript SDK. You can use this library in JavaScript as well, even though we love TypeScript. Fine-tune spp programmatically. Indexed vector data of your embeddings. Resources that are specific to your ML use case.

Create and manage AI applications without the need to move data to complex vector databases and pipelines. Integrate AI, vector search and real-time inference directly with your database. Python is all you need. All your AI models can be deployed in a single, scalable deployment. The AI models and APIs are automatically updated as new data is processed. You don't need to duplicate your data or create an additional database to use vector searching and build on it. SuperDuperDB allows vector search within your existing database. Integrate and combine models such as those from Sklearn PyTorch HuggingFace, with AI APIs like OpenAI, to build even the most complicated AI applications and workflows. With simple Python commands, deploy all your AI models in one environment to automatically compute outputs in your datastore (inference).

Create a natural language interface to your data. NLP is the heart of modern enterprise data processing. We provide developers the tools they need to quickly and efficiently build NLP systems that are ready for production. Our open-source framework allows for API-driven, scalable NLP application architectures. We believe in sharing. Our software is open-source. We value our community and make modern NLP accessible, practical, scalable, and easy to use. Natural language processing (NLP), a branch in AI, allows machines to interpret and process human language. Companies can use human language to interact and communicate with data and computers by implementing NLP. NLP is used in areas such as semantic search, question answering (QA), conversational A (chatbots), text summarization and question generation. It also includes text mining, machine translation, speech recognition, and text mining.

Vectara offers LLM-powered search as-a-service. The platform offers a complete ML search process, from extraction and indexing to retrieval and re-ranking as well as calibration. API-addressable for every element of the platform. Developers can embed the most advanced NLP model for site and app search in minutes. Vectara automatically extracts text form PDF and Office to JSON HTML XML CommonMark, and many other formats. Use cutting-edge zero-shot models that use deep neural networks to understand language to encode at scale. Segment data into any number indexes that store vector encodings optimized to low latency and high recall. Use cutting-edge, zero shot neural network models to recall candidate results from millions upon millions of documents. Cross-attentional neural networks can increase the precision of retrieved answers. They can merge and reorder results. Focus on the likelihood that the retrieved answer is a probable answer to your query.

Marqo is a complete vector search engine. It's more than just a database. A single API handles vector generation, storage and retrieval. No need to embed your own embeddings. Marqo can accelerate your development cycle. In just a few lines, you can index documents and start searching. Create multimodal indexes, and search images and text combinations with ease. You can choose from a variety of open-source models or create your own. Create complex and interesting queries with ease. Marqo allows you to compose queries that include multiple weighted components. Marqo includes input pre-processing and machine learning inference as well as storage. Marqo can be run as a Docker on your laptop, or scaled up to dozens GPU inference nodes. Marqo is scalable to provide low latency searches on multi-terabyte indices. Marqo allows you to configure deep-learning models such as CLIP for semantic meaning extraction from images.

Weaviate is an open source vector database. It allows you to store vector embeddings and data objects from your favorite ML models, and scale seamlessly into billions upon billions of data objects. You can index billions upon billions of data objects, whether you use the vectorization module or your own vectors. Combining multiple search methods, such as vector search and keyword-based search, can create state-of-the art search experiences. To improve your search results, pipe them through LLM models such as GPT-3 to create next generation search experiences. Weaviate's next generation vector database can be used to power many innovative apps. You can perform a lightning-fast, pure vector similarity search on raw vectors and data objects. Combining keyword-based and vector search techniques will yield state-of the-art results. You can combine any generative model with your data to do Q&A, for example, over your dataset.

KDB.AI, a powerful knowledge based vector database, is a powerful search engine and knowledge-based vector data base that allows developers to create scalable, reliable, and real-time AI applications. It provides advanced search, recommendation, and personalization. Vector databases are the next generation of data management, designed for applications such as generative AI, IoT or time series. Here's what makes them unique, how they work and the new applications they're designed to serve.

Use user feedback and semantic relevance to reliably retrieve optimal document chunks for your retrieval-augmented generation system. In your search system, combine semantic relevance with document freshness because recent results are more accurate. Create a personalized ecommerce feed in real-time using user vectors based on the SKU embeddings that were viewed by the user. A vector index in your warehouse can be used to discover behavioral clusters among your customers. Use spaces to build your indices, and run queries all within a Python Notebook.

A user-friendly interface will help you improve your embedding metadata, and embedding tokens. Apply advanced NLP cleaning techniques such as TF-IDF to normalize and enrich your embedded tokens. This will improve efficiency and accuracy for your LLM applications. Optimize relevance of content returned from vector databases by intelligently splitting and merging content based on structure, adding void or invisible tokens to make chunks more semantically coherent. Embedditor can be installed locally on your PC, in your enterprise cloud or on premises. Embedditor's advanced cleansing techniques can help you save up to 40% in embedding costs and vector storage by filtering out non-relevant tokens such as stop-words and punctuation.

The enterprise database for time series, documents, and vectors. Store any type data and combine the simplicity and scalability NoSQL with SQL. CrateDB is a distributed database that runs queries in milliseconds regardless of the complexity, volume, and velocity.

Zeta Alpha is the best Neural Discovery Platform to AI and beyond. You and your team can use state-of the-art NeuralSearch to improve the way you and others discover, organize, and share knowledge. Modern AI can help you make better decisions, avoid reinventing your wheel, and make it easier to stay in the know. The most up-to-date neural discovery across all relevant AI research sources and engineering information sources. With a seamless combination search, organization, recommendation, you can ensure that nothing is left behind. You can improve decision-making and reduce risks by having a single view of all relevant information, both internal and external. Get a clear view of what your team is reading or working on.

Zevi is a site-search engine that uses natural language processing (NLP), and machine learning (ML), to better understand users' search intent. Zevi uses its ML models to produce the most relevant search results instead of relying on keywords. They have been trained using vast amounts of multilingual data. Zevi is able to deliver highly relevant results regardless of search query, providing users with an intuitive search experience that minimizes cognitive load. Zevi also allows website owners to create personalized search results, promote specific search results based upon different criteria, and use search data to inform business decisions.

We've been working on Generative AI for 5 years. Deep Lake combines the power and flexibility of vector databases and data lakes to create enterprise-grade LLM-based solutions and refine them over time. Vector search does NOT resolve retrieval. You need a serverless search for multi-modal data including embeddings and metadata to solve this problem. You can filter, search, and more using the cloud, or your laptop. Visualize your data and embeddings to better understand them. Track and compare versions to improve your data and your model. OpenAI APIs are not the foundation of competitive businesses. Your data can be used to fine-tune LLMs. As models are being trained, data can be efficiently streamed from remote storage to GPUs. Deep Lake datasets can be visualized in your browser or Jupyter Notebook. Instantly retrieve different versions and materialize new datasets on the fly via queries. Stream them to PyTorch, TensorFlow, or Jupyter Notebook.

Atlas integrates with your workflow by organizing text, embedding datasets and creating interactive maps that can be explored in a web browser. To understand your data, you don't need to scroll through Excel files or log Dataframes. Atlas automatically analyzes, organizes, and summarizes your documents, surfacing patterns and trends. Atlas' pre-organized data interface makes it easy to quickly identify and remove any data that could be harmful to your AI projects. You can label and tag your data, while cleaning it up with instant sync to your Jupyter notebook. Although vector databases are powerful, they can be difficult to interpret. Atlas stores, visualizes, and allows you to search through all your vectors within the same API.

Jina Search makes it easy to search for any topic in seconds. It's faster and more accurate than traditional search engines. Our AI search captures all information stored in images, text, and provides you with the most comprehensive results. Jina Search unlocks the power of search to revolutionize how you find what your looking for. Classical Search was unable to retrieve relevant results because not all items in the dataset had the right label. Jina Search does not rely on tags and was able to find better items. Make the most of state-of-the art ML models that can work with multiple data types, including images and text. All customizations are maintained by Elasticsearch. Jina Search will automatically recognize each image in your database and store it accordingly.

Semantee, a managed database that is easy to configure and optimized for semantic searches, is hassle-free. It is available as a set REST APIs that can be easily integrated into any application in minutes. It offers multilingual semantic searching for applications of any size, both on-premise and in the cloud. The product is significantly cheaper and more transparent than most providers, and is optimized for large-scale applications. Semantee also offers an abstraction layer over an e-shop's product catalog, enabling the store to utilize semantic search instantly without having to re-configure its database.

Businesses and developers can now create cutting-edge neural searches, generative AI and multimodal services using state of the art LMOps, LLOps, and cloud-native technology. Multimodal data is everywhere. From tweets to short videos on TikTok to audio snippets, Zoom meeting records, PDFs containing figures, 3D meshes and photos in games, there's no shortage of it. It is powerful and rich, but it often hides behind incompatible data formats and modalities. High-level AI applications require that one solve search first and create second. Neural Search uses AI for finding what you need. A description of a sunrise may match a photograph, or a photo showing a rose can match the lyrics to a song. Generative AI/Creative AI use AI to create what you need. It can create images from a description or write poems from a photograph.

You can access countless corporate documents and systems, regardless of platform, and keep track millions of data. A combination of state-of-the art neural search techniques, enterprise search functionality, and many standard connectors create a new search experience. INTERGATOR Cloud can also be hosted by a German hoster. This allows you to comply with all requirements of German and European law, especially data protection. We adapt to your needs. INTERGATOR Cloud is easily scaleable to meet your search needs. You can search your company data anywhere in the world, and access information without complicated VPN solutions. Natural Language Processing (NLP), and neural networks are used to train models that can extract the most important information from documents and data, and then consider the entire information stock. This comprehensive solution provides you with the best information and knowledge management.

A true second brain for knowledge-based work. Orchard is an AI assistant that can converse with you and understand complex requests. Orchard Classic is still the best AI editor for text editing. Ask questions about your documents from wherever they are located. Neural search across all your documents + synthesis using AI = The best way to learn from your work. A text editor that can finish your sentences and suggest related ideas, based on your institutional knowledge. AI text editing is now contextually aware. Orchard should be your personal analyst, able to understand you and your work. Orchard will determine if and how it can use the information it has about you each time you submit a request. It's almost as if ChatGPT had cited sources that included resources relevant to your work. Orchard is able to break down complex tasks much more accurately than ChatGPT. Orchard creates a search engine that can find all your data. Orchard is being integrated with businesses.

Sinequa is an intelligent enterprise search that connects workers in the digital workplace to the information, expertise, and insights they require to do their jobs. It can handle large and complex data volumes and ensure compliance in even the most challenging environments. Employees can access relevant information and insights to increase innovation and customer responsiveness. Intelligent search empowers people to do their jobs more effectively, which results in significant cost savings. Employees can get insights from their work context to help them comply with regulations quickly and reduce financial and reputational risk. Sinequa’s Neural Search provides the most sophisticated engine for discovering enterprise information assets available on the market today. By combining state-of-the-art deep learning language models with the best NLP and statistical techniques, employees and customers spend less time searching for information and more time developing insights to drive decisions and solutions.

With just a few lines, you can integrate natural language understanding and generation into the product. The Cohere API allows you to access models that can read billions upon billions of pages and learn the meaning, sentiment, intent, and intent of every word we use. You can use the Cohere API for human-like text. Simply fill in a prompt or complete blanks. You can create code, write copy, summarize text, and much more. Calculate the likelihood of text, and retrieve representations from your model. You can filter text using the likelihood API based on selected criteria or categories. You can create your own downstream models for a variety of domain-specific natural languages tasks by using representations. The Cohere API is able to compute the similarity of pieces of text and make categorical predictions based on the likelihood of different text options. The model can see ideas through multiple lenses so it can identify abstract similarities between concepts as distinct from DNA and computers.

The complete platform for all aspects of research. {Instantly retrieve and wrangle the insights you need, no matter your source of unstructured data.|No matter what source of unstructured data, you can instantly retrieve and extract the insights that you need.} Find answers from millions of public sources like SEC Filings, Earnings Calls and expert network transcripts. Or leverage your firm's expertise. Hebbia can instantly connect to any source of unstructured information in your company, and can ingest any file type or API. You can work faster by using tools for diligence and research processes, regardless of the task. With a single click, you can spread financials, find public comps or structure unstructured information. Hebbia is trusted with the most sensitive data by some of the world's most powerful financial institutions and governments. Security is our core. Hebbia is the only encrypted search engine available on the market.

In less than 2 minutes, you can create a backend. Get a Postgres database, authentication and instant APIs to start your project. Real-time subscriptions are also available. You can build faster and concentrate on your products. Every project is a Postgres database, the most trusted relational database in the world. You can add user sign-ups or logins to secure your data with Row Level Security. Large files can be stored, organized and served. Any media, including images and videos. Without the need to deploy or scale servers, you can write custom code and cron jobs. There are many starter projects and example apps to help you get started. We will instantly inspect your database and provide APIs. Stop creating repetitive CRUD endpoints. Instead, focus on your product. Type definitions directly from your database schema. Supabase can be used in the browser without a build. You can develop locally and push to production as soon as you are ready. You can manage Supabase projects on your local machine.

Astra DB from DataStax is a real-time vector database as a service for developers that need to get accurate Generative AI applications into production, fast. Astra DB gives you a set of elegant APIs supporting multiple languages and standards, powerful data pipelines and complete ecosystem integrations. Astra DB enables you to quickly build Gen AI applications on your real-time data for more accurate AI that you can deploy in production. 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. SAML SSO secure account accessibility. You can deploy on Amazon, Google Cloud, or Microsoft Azure while still compatible with open-source Apache Cassandra.

Voldemort does not have a relational database. It doesn't attempt to satisfy arbitrary relationships while also satisfying ACID properties. It is not an object database that attempts transparently to map object reference graphs. Nor does it introduce a new abstraction such as document-orientation. It is essentially a large, distributed, persistent, fault-tolerant, hash table. This will allow applications to use O/R maps like active-record and hibernate, which will provide horizontal scaling and greater availability, but with a great loss in convenience. A system may consist of many functionally partitioned APIs or services that can manage storage resources across multiple data centres using storage systems that may be themselves horizontally partitioned. This is useful for large applications that are subject to internet-type scalability. Because all data is not in one database, it is impossible to make arbitrary in-database connections for applications in this space.

dbForge Index Manager for SQL Server is a user-friendly tool designed to help database specialists detect and resolve index fragmentation issues. It gathers index fragmentation statistics, displays detailed information in a visual interface, identifies indexes in need of maintenance, and provides recommendations for addressing these issues. Key Features: - Detailed information about all database indexes - Customizable thresholds for rebuilding and reorganizing indexes - Automatic resolving index fragmentation issues - Generation of scripts for index rebuilding and reorganization with options to save and reuse them - Exporting index analysis results as detailed reports - Scanning multiple databases for fragmented indexes - Efficient index analysis with sorting and searching through - Task automation for regular index analysis and defragmentation via the command-line interface dbForge Index Manager integrates seamlessly into Microsoft SQL Server Management Studio (SSMS), allowing users to quickly master its functionality and incorporate it into their workflows.

NGINX Service Mesh is always free and can scale from open-source projects to a fully supported enterprise-grade solution. NGINX Service Mesh gives you control over Kubernetes. It features a single configuration that provides a unified data plan for ingress and exit management. NGINX Service Mesh's real star is its fully integrated, high-performance Data Plan. Our data plane leverages the power of NGINX Plus in order to operate highly available, scalable containerized environments. It offers enterprise traffic management, performance and scalability that no other sidecars could offer. It offers seamless and transparent load balancing and reverse proxy, traffic routing and identity as well as encryption features that are required for high-quality service mesh deployments. It can be paired with NGINX Plus-based NGINX Ingress Controller to create a unified data plan that can be managed from one configuration.

*GraphDB allows the creation of large knowledge graphs by linking diverse data and indexing it for semantic search. * GraphDB is a robust and efficient graph database that supports RDF and SPARQL. The GraphDB database supports a highly accessible replication cluster. This has been demonstrated in a variety of enterprise use cases that required resilience for data loading and query answering. Visit the GraphDB product page for a quick overview and a link to download the latest releases. GraphDB uses RDF4J to store and query data. It also supports a wide range of query languages (e.g. SPARQL and SeRQL), and RDF syntaxes such as RDF/XML and Turtle.

This advanced DBF data management tool is for all database users. It has many features that are not available in high-end software. DBF Manager's built-in dbf editor provides secure and safe access to the internals. It has many sophisticated features, including extensive support for data structure modification. Each data type is matched with the sophisticated data editing, conversion, and search-replace features. DBF Manager offers a wide range of tools to help you manage your database indexes. The index manager, which is easy to use, can perform index rebuilds and re-indexing from an open dbf files. It also allows for a variety of file types to be opened, modified and viewed. The extensive index file manager feature set includes a print dbf format feature.

The graph database is designed for graphs up to super large scale with very low latency. We continue to work with the community to promote, popularize, and prepare the graph database. Nebula Graph allows only authenticated access through role-based access control. Nebula Graph can support multiple storage engines and the query language is extensible to support new algorithms. Nebula Graph offers low latency read/write while maintaining high throughput to simplify complex data sets. Nebula Graph's distributed, shared-nothing architecture allows for linear scaling. Nebula Graph's SQL query language is similar to SQL and can be used to address complex business requirements. Nebula Graph's horizontal scalability, snapshot feature and high availability guarantee that there will be no downtime. Nebula Graph has been used in production environments by large Internet companies such as JD, Meituan and Xiaohongshu.

HugeGraph is a high-speed, highly-scalable graph database. HugeGraph's excellent OLTP capability allows for the storage and querying of billions of edges and vertices. Gremlin, a powerful graph traversal and query language, can handle complex graph queries in compliance with Apache TinkerPop 3. It supports Gremlin and is compliant to Apache TinkerPop 3. Schema Metadata Management includes VertexLabel EdgeLabel PropertyKey and IndexLabel. Multi-type Indexes that support complex combination queries, range query, and exact query. Plug-in Backend Store Driver Framework. Supports RocksDB, Cassandra and ScyllaDB. It is easy to add another backend store driver if necessary. Integration with Hadoop/Spark. HugeGraph is built on the TinkerPop framework. We refer to the storage structure and schema definition of DataStax.

PostgresML is an entire platform that comes as a PostgreSQL Extension. Build simpler, faster and more scalable model right inside your database. Explore the SDK, and test open-source models in our hosted databases. Automate the entire workflow, from embedding creation to indexing and Querying for the easiest (and fastest) knowledge based chatbot implementation. Use multiple types of machine learning and natural language processing models, such as vector search or personalization with embeddings, to improve search results. Time series forecasting can help you gain key business insights. SQL and dozens regression algorithms allow you to build statistical and predictive models. ML at database layer can detect fraud and return results faster. PostgresML abstracts data management overheads from the ML/AI cycle by allowing users to run ML/LLM on a Postgres Database.

Use the power of video searches. Multimodal, contextual understanding for video. Our AI extracts key video features such as object, action, text on screen and people. It converts all that information into vectors. Vectors allow for fast and scalable search. AI provides context-specific insights and search, replacing ineffective keyword tagging. Search your video, visuals and conversations, logos and text. End-to end infrastructure to make your videos searchable. Start building in just a few API requests. Twelve Labs' AI models outperform the best commercial and open-source models. It is simple and intuitive for developers to integrate Twelve Labs AI video understanding. It's just a two-step process (index/search). You can fine-tune your model based on our AI for video understanding.

Apache Doris is an advanced data warehouse for real time analytics. It delivers lightning fast analytics on real-time, large-scale data. Ingestion of micro-batch data and streaming data within a second. Storage engine with upserts, appends and pre-aggregations in real-time. Optimize for high-concurrency, high-throughput queries using columnar storage engine, cost-based query optimizer, and vectorized execution engine. Federated querying for data lakes like Hive, Iceberg, and Hudi and databases like MySQL and PostgreSQL. Compound data types, such as Arrays, Maps and JSON. Variant data types to support auto datatype inference for JSON data. NGram bloomfilter for text search. Distributed design for linear scaling. Workload isolation, tiered storage and efficient resource management. Supports shared-nothing as well as the separation of storage from compute.

Solr is highly reliable, scalable, and fault-tolerant. It provides distributed indexing, replication, load-balanced querying and automated failover and recovery. Solr powers many of the largest internet sites around the world's search and navigation functions. Solr allows powerful matching capabilities, including phrases, wildcards and joins, grouping, and much more across any type of data. Solr has been proven on large scales all over the globe. Solr uses the same tools that you use to make application development easy. Solr ships with an intuitive, responsive administrative interface that makes it easy to manage your Solr instances. Want to know more about your instances? Solr publishes loads metric data via JMX. Solr is built on Apache Zookeeper, which has been proven to scale well. Solr is a complete solution for replication, distribution, rebalancing, and fault tolerance.

LibreNMS is a network monitoring tool that offers a variety of features and supports multiple devices. Automatically discover all of your network using CDP/FDP, LLDP OSPF, BGP and SNMP. You can notify via email, IRC or Slack with this flexible alerting system. An API that allows you to retrieve, graph, and manage data from your network. Based on the amount of data transferred or used, you can generate bandwidth bills for ports on your network. Keep up-to-date with bug fixes, new features and more. Horizontal scaling allows you to grow your network. The native iPhone App provides core functionality. Native Android App is available to provide core functionality. Mobile-friendly web interface, Unix agent, extensive device support. You can try the online demo before you install. Support for Apache and Nginx web server. Automatic discovery and customizable alarming.

Gigablast was founded by Matt Wells in 2000 to index hundreds of millions of pages using as little hardware as possible. Gigablast offers high-performance, large-scale, real-time information retrieval technology for partner sites. The company offers many features, including topic generation and the ability of indexing multiple document formats. This search delivery method gives a partner "turnkey" search capability. It can instantly offer search at maximum scale and minimum cost. The clients range from NASDAQ 100 companies to small businesses. Gigablast is among a few search engines that have their own searchable index with over a billion pages. Gigablast has teamed with freenode in order to create a private search engine. It is the only search engine that uses encryption to protect privacy.

Database Management Solutions for IMS™, z/OS® will speed up data retrieval, create and rebuild indexes quickly, implement secondary indexes using Fast Path databases, reduce backup and recovery times and increase data availability. You'll also be able to recover data in a short time with consistent, secure data. Reduce the stress of maintaining and managing IMS structures, and increase productivity. Make backups, create recovery procedures, and carry out disaster recovery. To ensure that IMS structures work optimally, use sophisticated analysis. Our IMS Database Management Solutions can optimize and automate your database performance to reduce backup and recovery times, better CPU usage, faster data retrieval and recovery, and better data availability.

eSpatial Mapping Software allows you to find hidden details in your data using location analytics. This allows you to segment and filter data to link business activity directly to a specific location. An interactive snapshot of your data is available for you to quickly understand with color-coded charts, graphs, charts, annotations, and maps. You can quickly map and manage your territories to facilitate assignment and balancing, which helps you manage your resources more effectively. Optimized routing allows you to maximize your field time and plan sales and service calls using radius and nearest neighbor analysis. eSpatial allows you to increase revenue, reduce costs and improve customer service. All your analysis and maps can be shared as interactive maps, images, or raw data depending upon your needs.