Vespa Alternatives

Long-term memory for AI. The Pinecone vector database makes it easy to build high-performance vector search applications. 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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Vectara is LLM-powered search-as-a-service. The platform provides a complete ML search pipeline from extraction and indexing to retrieval, re-ranking and calibration. Every element of the platform is API-addressable. Developers can embed the most advanced NLP models for app and site search in minutes. Vectara automatically extracts text from PDF and Office to JSON, HTML, XML, CommonMark, and many more. Encode at scale with cutting edge zero-shot models using deep neural networks optimized for language understanding. Segment data into any number of indexes storing vector encodings optimized for low latency and high recall. Recall candidate results from millions of documents using cutting-edge, zero-shot neural network models. Increase the precision of retrieved results with cross-attentional neural networks to merge and reorder results. Zero in on the true likelihoods that the retrieved response represents a probable answer to the query.

Build a natural language interface for your data. NLP is at the core of modern enterprise data processing. We provide developers with the right tools to build production-ready NLP systems quickly and efficiently. Our open-source framework for scalable, API-driven NLP application architectures. We believe in sharing. Our software is open source. We value our community, and we make modern NLP easily accessible, practical, and scalable. Natural language processing (NLP) is a branch of AI that enables machines to process and interpret human language. In general, by implementing NLP, companies can leverage human language to interact with computers and data. Areas of NLP include semantic search, question answering (QA), conversational AI (chatbots), semantic search, text summarization, question generation, text generation, machine translation, text mining, speech recognition, to name a few use cases.

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.

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.

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.

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.

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.

Vector database built for scalable similarity search. Open-source, highly scalable, and blazing fast. Store, index, and manage massive embedding vectors generated by deep neural networks and other machine learning (ML) models. With Milvus vector database, you can create a large-scale similarity search service in less than a minute. Simple and intuitive SDKs are also available for a variety of different languages. Milvus is hardware efficient and provides advanced indexing algorithms, achieving a 10x performance boost in retrieval speed. Milvus vector database has been battle-tested by over a thousand enterprise users in a variety of use cases. With extensive isolation of individual system components, Milvus is highly resilient and reliable. The distributed and high-throughput nature of Milvus makes it a natural fit for serving large-scale vector data. Milvus vector database adopts a systemic approach to cloud-nativity, separating compute from storage.

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.

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.

Improve your embedding metadata and embedding tokens with a user-friendly UI. Seamlessly apply advanced NLP cleansing techniques like TF-IDF, normalize, and enrich your embedding tokens, improving efficiency and accuracy in your LLM-related applications. Optimize the relevance of the content you get back from a vector database, intelligently splitting or merging the content based on its structure and adding void or hidden tokens, making chunks even more semantically coherent. Get full control over your data, effortlessly deploying Embedditor locally on your PC or in your dedicated enterprise cloud or on-premises environment. Applying Embedditor advanced cleansing techniques to filter out embedding irrelevant tokens like stop-words, punctuations, and low-relevant frequent words, you can save up to 40% on the cost of embedding and vector storage while getting better search results.

Access countless systems and corporate documents, regardless of platform, and keep track of millions of pieces of data. State-of-the-art neural search techniques combined with enterprise search functionality and numerous standard connectors enable a completely new search experience. INTERGATOR Cloud can be hosted by a German hoster and thus comply with the strict requirements of German and European law (especially data protection). We grow with your requirements. INTERGATOR Cloud can easily be scaled whenever you need more or less search. Search your company data from anywhere in the world and get information without complex VPN solutions. With the help of Natural Language Processing (NLP) and neural networks, models are trained that extract essential information from data and documents and consider the information stock in its entirety. You receive a comprehensive solution for up-to-date information and knowledge management.

Zeta Alpha is the best Neural Discovery Platform for AI and beyond. Use state-of-the-art Neural Search to improve how you and your team discover, organize and share knowledge. Make better decisions, avoid reinventing the wheel, and make staying in the know effortless: the power of modern AI to make an impact with your work faster. With state-of-the-art neural discovery across all relevant AI research and engineering information sources. Ensure that nothing falls through the cracks with a seamless combination of powerful search, organization, and recommendation features. Steer decision-making across the organization and reduce associated risks by maintaining a unified view of relevant internal and external information. Get a clear overview of what your team is reading and working on.

A true second brain for knowledge work. Orchard is a conversational AI assistant that understands complex requests and cites itself with your knowledge. Orchard Classic is still the best AI text editor for editing. Ask questions about your documents, wherever they live. Neural search across your docs + synthesis with AI = the best way to learn from your own work. A text editor that finishes your sentences and suggests related ideas, informed by your institutional knowledge. AI text editing is now contextually aware. We want Orchard to be a personal analyst that understands you and your work. Each time you make a request, Orchard determines whether and how to use what it knows about you. It's like if ChatGPT cited its sources with resources relevant to your work. Orchard can also break down complex tasks more reliably than ChatGPT. Orchard builds a search engine for all of your data. We are actively integrating Orchard with businesses.

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.

Semantee is a hassle-free easily configurable managed database optimized for semantic search. It is provided as a set of REST APIs, which can be integrated into any app in minutes and offers multilingual semantic search for applications of virtually any size both in the cloud and on-premise. The product is priced significantly more transparently and cheaply compared to most providers and is especially optimized for large-scale apps. 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.

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.

Zevi is a site search engine that leverages natural language processing (NLP) and machine learning (ML) to better understand the search intent of users. Instead of relying on keywords to produce the most relevant search results, Zevi relies on its ML models, which have been trained on vast amounts of multilingual data. As a result, Zevi can deliver extremely relevant results regardless of the search query used, thus providing users with an intuitive search experience that minimizes their cognitive load. In addition, Zevi allows website owners to provide personalized results, promote particular search results based on various criteria, and to use search data to make informed business decisions.

Empower businesses and developers to create cutting-edge neural search, generative AI, and multimodal services using state-of-the-art LMOps, MLOps and cloud-native technologies. Multimodal data is everywhere: from simple tweets to photos on Instagram, short videos on TikTok, audio snippets, Zoom meeting records, PDFs with figures, 3D meshes in games. It is rich and powerful, but that power often hides behind different modalities and incompatible data formats. To enable high-level AI applications, one needs to solve search and create first. Neural Search uses AI to find what you need. A description of a sunrise can match a picture, or a photo of a rose can match a song. Generative AI/Creative AI uses AI to make what you need. It can create an image from a description, or write poems from a picture.

Sinequa provides intelligent enterprise search connecting workers in the digital workplace with the information, expertise and insights they need to do their jobs. It handles vast and heterogeneous data volumes, offering security and compliance even in the most complex environments. Enabling employees to get relevant information & insights speeds up innovation and optimizes responsiveness to customers. Organizations powered by intelligent search enable people to do their jobs better, resulting in significant cost savings. Delivering insights to employees within the context of their work provides the transparency and speed needed to comply with regulations on a timely basis and mitigate financial and reputational risk. Sinequa’s Neural Search provides the most sophisticated engine for discovering enterprise information assets available on the market today.

Open-source vector similarity search for Postgres. Supports exact and approximate nearest neighbor search for L2 distance, inner product, and cosine distance.

The Databricks Data Intelligence Platform allows your entire organization to use data and AI. It’s built on a lakehouse to provide an open, unified foundation for all data and governance, and is powered by a Data Intelligence Engine that understands the uniqueness of your data. The winners in every industry will be data and AI companies. From ETL to data warehousing to generative AI, Databricks helps you simplify and accelerate your data and AI goals. Databricks combines generative AI with the unification benefits of a lakehouse to power a Data Intelligence Engine that understands the unique semantics of your data. This allows the Databricks Platform to automatically optimize performance and manage infrastructure in ways unique to your business. The Data Intelligence Engine understands your organization’s language, so search and discovery of new data is as easy as asking a question like you would to a coworker.

With Jina Search, you can search for anything in seconds - faster and more accurately than any traditional search engine. Our AI search captures all the information stored in images and text, providing you with the most comprehensive results. Unlock the power of search and revolutionize the way you find what you're looking for with Jina Search. In this example, not all items on the dataset had the correct label, making it impossible for Classical Search to retrieve relevant results. Since Jina Search doesn't rely on tags, was successful on finding better items. Take full advantage of using state-of-the-art ML models that are optimized to work with multiple modalities of data, such as images and text while maintaining all your Elasticsearch customization. This means you don’t need to annotate each image in your dataset with labels, Jina Search will automatically understand the image and store it accordingly.

The end to end platform for research. Instantly retrieve and wrangle the 
insights you need, no matter your source
 of unstructured data. Uncover answers across millions of public sources, like SEC Filings, Earnings Calls, and expert network transcripts, or leverage your firm's knowledge. Hebbia instantly hooks into any source of unstructured data in your organization, ingesting any file type or API. Tooling for diligence and research processes lets you work faster, no matter the task. Spread financials, find public comps, or structure unstructured data with the a single button click. The world's largest governments and financial institutions trust Hebbia with their most sensitive data. ‍ Security is at our core. Hebbia is the first and only encrypted search engine on the market.

Build natural language understanding and generation into your product with a few lines of code. The Cohere API provides access to models that read billions of web pages and learn to understand the meaning, sentiment, and intent of the words we use. Use the Cohere API to write human-like text by completing a prompt or filling in blanks. You can write copy, generate code, summarize text, and more. Compute the likelihood of text and retrieve representations from the model. Use the likelihood API to filter text based on chosen categories or selected criteria. With representations, you can train your own downstream models on a wide variety of domain-specific natural language tasks. The Cohere API can compute the similarity between pieces of text, and make categorical predictions by comparing the likelihood of different text options. The model has multiple lenses through which to view ideas, so that it can recognize abstract similarities between concepts as distinct as DNA and computers.

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.

Deploy product discovery that automatically learns customer intent. Optimize results across the board with machine learning. Personalize search, autosuggest, browse, recommendation results for every user. Computational approaches that infer intent from
 a user’s search query. Continuously updated search result rankings through automated learning from behavioral data. Integrated, data-driven effort to deliver unique results for each person and query. Advanced tools to supplement automated results with merchant expertise. Deploy product discovery that automatically learns customer intent. Optimize results across the board with machine learning. Personalize search, autosuggest, browse, recommendation results for every user. As users type in their query, natural language processing deciphers intent and presents conversion-worthy results. Constructor Search collects and learns from behavioral data.

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.

Apply the latest NLP technology to your own data with the use of Haystack's pipeline architecture. Implement production-ready semantic search, question answering, summarization and document ranking for a wide range of NLP applications. Evaluate components and fine-tune models. Ask questions in natural language and find granular answers in your documents using the latest QA models with the help of Haystack pipelines. Perform semantic search and retrieve ranked documents according to meaning, not just keywords! Make use of and compare the latest pre-trained transformer-based languages models like OpenAI’s GPT-3, BERT, RoBERTa, DPR, and more. Build semantic search and question-answering applications that can scale to millions of documents. Building blocks for the entire product development cycle such as file converters, indexing functions, models, labeling tools, domain adaptation modules, and REST API.

Google Cloud Pub/Sub. Scalable, in-order message delivery with pull and push modes. Auto-scaling and auto-provisioning with support from zero to hundreds of GB/second. Independent quota and billing for publishers and subscribers. Global message routing to simplify multi-region systems. High availability made simple. Synchronous, cross-zone message replication and per-message receipt tracking ensure reliable delivery at any scale. No planning, auto-everything. Auto-scaling and auto-provisioning with no partitions eliminate planning and ensures workloads are production-ready from day one. Advanced features, built in. Filtering, dead-letter delivery, and exponential backoff without sacrificing scale help simplify your applications. A fast, reliable way to land small records at any volume, an entry point for real-time and batch pipelines feeding BigQuery, data lakes and operational databases. Use it with ETL/ELT pipelines in Dataflow.

Ship AI faster with managed, cloud-hosted AI packages. Full, built-in support for GPT-4. No API tokens are necessary. Build with our low code framework. Integrations with all major models are built-in. Deploy for an instant API. Scale and share without managing infrastructure. Turn prompts, prompt chains, and basic Python into a managed API. Turn a clever prompt into a published API you can share. Add logic and routing smarts with Python. Steamship connects to your favorite models and services so that you don't have to learn a new API for every provider. Steamship persists in model output in a standardized format. Consolidate training, inference, vector search, and endpoint hosting. Import, transcribe, or generate text. Run all the models you want on it. Query across the results with ShipQL. Packages are full-stack, cloud-hosted AI apps. Each instance you create provides an API and private data workspace.

The fastest path to relevant recommendations and search. Increase engagement, conversion, and revenue with a configurable system that adapts in real time. We help your users find what they're looking for by surfacing the products or content that are most relevant to them. We do this whilst taking into account your business objectives to ensure all sides of your platform or marketplace are being optimized fairly. Under the hood, Shaped is a real-time, 4-stage, recommendation system containing all the data and machine-learning infrastructure needed to understand your data and serve your discovery use-case at scale. Connect and deploy rapidly with direct integration to your existing data sources. Ingest and re-rank in real-time using behavioral signals. Fine-tune LLMs and neural ranking models for state-of-the-art performance. Build and experiment with ranking and retrieval components for any use case.

NVIDIA NeMo Megatron is an end-to-end framework for training and deploying LLMs with billions and trillions of parameters. NVIDIA NeMo Megatron, part of the NVIDIA AI platform, offers an easy, efficient, and cost-effective containerized framework to build and deploy LLMs. Designed for enterprise application development, it builds upon the most advanced technologies from NVIDIA research and provides an end-to-end workflow for automated distributed data processing, training large-scale customized GPT-3, T5, and multilingual T5 (mT5) models, and deploying models for inference at scale. Harnessing the power of LLMs is made easy through validated and converged recipes with predefined configurations for training and inference. Customizing models is simplified by the hyperparameter tool, which automatically searches for the best hyperparameter configurations and performance for training and inference on any given distributed GPU cluster configuration.

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.

Make AI a trusted part of your business. Build better, deploy faster, and audit easily with the flexibility of a cloud-native platform and the power to deploy anywhere. Easily import models and search cataloged models from across your organization. Save time by annotating data rapidly with model-in-the-loop hinting. Understand the full provenance of your data, models, workflows, and inferences. Deploy models where you need them, including for edge and IoT use cases. Getting valuable insights from your data is not just for data scientists. With Chariot’s low-code interface, meaningful collaboration can take place across teams. Train models rapidly using your organization's production data. Deploy models with one click and monitor models in production at scale.

Lexical is comprised of editor instances that each attach to a single content editable element. A set of editor states represent the current and pending states of the editor at any given time. Lexical is designed for everyone. It follows best practices established in WCAG and is compatible with screen readers and other assistive technologies. Lexical is minimal. It doesn't directly concern itself with UI components, toolbars or rich-text features and markdown. The logic for these features can be included via a plugin interface. It's super easy to get started with Lexical in any environment. Lexical is framework agnostic, but provides a set of bindings for React to help you get off the ground even quicker. After the initial setup, delightfully ergonomic APIs make building custom functionality straightforward and downright fun!

Effortlessly build new services, and extend your existing applications with Amplication AI. Go from idea to production in four minutes. AI-powered assistant that generates production-ready code, ensuring consistency, predictability, and adherence to the highest standards. The transition from concept to deployment in minutes with production-ready code that’s built to scale. Amplication’s AI delivers more than prototypes, get fully operational, robust backend services ready to go live. Streamline development workflows, reduce time, and optimize your resources. Do more with what you have with the power of AI. Input your requirements and watch Jovu translate them into ready-to-use code components. Production-ready data models, APIs, authentication, authorization, event-driven architecture, and everything else that is needed to get your service up and running. Add architecture components, and integrations and extend with the Amplication plugins.

Deploy machine learning applications to production in minutes, rather than months. Automate the transformation of raw data, generate training data sets, and serve features for online inference at scale. Save months of work by replacing bespoke data pipelines with robust pipelines that are created, orchestrated and maintained automatically. Increase your team’s efficiency by sharing features across the organization and standardize all of your machine learning data workflows in one platform. Serve features in production at extreme scale with the confidence that systems will always be up and running. Tecton meets strict security and compliance standards. Tecton is not a database or a processing engine. It plugs into and orchestrates on top of your existing storage and processing infrastructure.

PostgresML is a complete platform in a PostgreSQL extension. Build simpler, faster, and more scalable models right inside your database. Explore the SDK and test open source models in our hosted database. Combine and automate the entire workflow from embedding generation to indexing and querying for the simplest (and fastest) knowledge-based chatbot implementation. Leverage multiple types of natural language processing and machine learning models such as vector search and personalization with embeddings to improve search results. Leverage your data with time series forecasting to garner key business insights. Build statistical and predictive models with the full power of SQL and dozens of regression algorithms. Return results and detect fraud faster with ML at the database layer. PostgresML abstracts the data management overhead from the ML/AI lifecycle by enabling users to run ML/LLM models directly on a Postgres database.