Alternatives to Marqo

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.

The AI Knowledge Platform. The Pinecone Database, Inference, and Assistant make 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.

Start building in just minutes. Vectorize provides fast and cost-effective vector storage for your AI Retrieval augmented generation (RAG) & search applications. Vectorize integrates seamlessly with Cloudflare’s AI developer platform & AI gateway to centralize development, monitoring, and control of AI applications at a global level. Vectorize is a globally-distributed vector database that allows you to build AI-powered full-stack applications using Cloudflare Workers AI. Vectorize makes it easier and cheaper to query embeddings - representations of objects or values such as text, images, audio, etc. - that are intended to be consumed by machine intelligence models and semantic search algorithms. Search, similarity and recommendation, classification, anomaly detection, and classification based on your data. Search results are improved and faster. Support for string, number and boolean type.

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.

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.

txtai, an open-source embeddings database, is designed for semantic search and large language model orchestration. It also supports language model workflows. It unifies vector indices (both dense and sparse), graph networks, relational databases and provides a robust foundation to vector search. Users can create autonomous agents, implement retrieval augmented creation processes, and develop multimodal workflows with txtai. The key features include vector searching with SQL support, object-storage integration, topic modeling and graph analysis, as well as multimodal indexing capabilities. It allows the creation of embeddings from various data types including text, audio, images and video. txtai also offers pipelines powered with language models to handle tasks like LLM prompting and question-answering. It can also be used for labeling, transcriptions, translations, and summaries.

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.

LanceDB is an open-source database for AI that is developer-friendly. LanceDB provides the best foundation for AI applications. From hyperscalable vector searches and advanced retrieval of RAG data to streaming training datasets and interactive explorations of large AI datasets. Installs in seconds, and integrates seamlessly with your existing data and AI tools. LanceDB is an embedded database with native object storage integration (think SQLite, DuckDB), which can be deployed anywhere. It scales down to zero when it's not being used. LanceDB is a powerful tool for rapid prototyping and hyper-scale production. It delivers lightning-fast performance in search, analytics, training, and multimodal AI data. Leading AI companies have indexed petabytes and billions of vectors, as well as text, images, videos, and other data, at a fraction the cost of traditional vector databases. More than just embedding. Filter, select and stream training data straight from object storage in order to keep GPU utilization at a high level.

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.

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.

Vector search can give you a competitive edge. Streamline your AI/ML workflows, reduce costs and stay ahead with up to a 10x faster time-to market. ApertureDB’s unified multimodal management of data will free your AI teams from data silos and allow them to innovate. Setup and scale complex multimodal infrastructure for billions objects across your enterprise in days instead of months. Unifying multimodal data with advanced vector search and innovative knowledge graph, combined with a powerful querying engine, allows you to build AI applications at enterprise scale faster. ApertureDB will increase the productivity of your AI/ML team and accelerate returns on AI investment by using all your data. You can try it for free, or schedule a demonstration to see it in action. Find relevant images using labels, geolocation and regions of interest. Prepare large-scale, multi-modal medical scanning for ML and Clinical studies.

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.

Azure Managed Redis offers the latest Redis innovations and industry-leading availability. It also has a cost-effective Total Cost Of Ownership (TCO) that is designed for hyperscale clouds. Azure Managed Redis provides these capabilities on a trusted platform, empowering businesses with the ability to scale and optimize generative AI applications in a seamless manner. Azure Managed Redis uses the latest Redis innovations for high-performance and scalable AI applications. Its features, such as in-memory storage, vector similarity searches, and real-time computing, allow developers to handle large datasets, accelerate machine-learning, and build faster AI applications. Its interoperability to Azure OpenAI Service allows AI workloads that are ready for mission-critical applications to be faster, more scalable and more reliable.

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.

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.

Vald is a distributed, fast, dense and highly scalable vector search engine that approximates nearest neighbors. Vald was designed and implemented using the Cloud-Native architecture. It uses the fastest ANN Algorithm NGT for searching neighbors. Vald supports automatic vector indexing, index backup, horizontal scaling, which allows you to search from billions upon billions of feature vector data. Vald is simple to use, rich in features, and highly customizable. Usually, the graph must be locked during indexing. This can cause stop-the world. Vald uses distributed index graphs so that it continues to work while indexing. Vald has its own highly customizable Ingress/Egress filter. This can be configured to work with the gRPC interface. Horizontal scaling is available on memory and cpu according to your needs. Vald supports disaster recovery by enabling auto backup using Persistent Volume or Object Storage.

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).

VectorDB is a lightweight Python program for storing and retrieving texts using chunking techniques, embedding techniques, and vector search. It offers an easy-to use interface for searching, managing, and saving textual data, along with metadata, and is designed to be used in situations where low latency and speed are essential. When working with large language model datasets, vector search and embeddings become essential. They allow for efficient and accurate retrieval relevant information. These techniques enable quick comparisons and search, even with millions of documents. This allows you to find the most relevant search results in a fraction the time of traditional text-based methods. The embeddings also capture the semantic meaning in the text. This helps improve the search results, and allows for more advanced natural-language processing tasks.

Vectorize is an open-source platform that transforms unstructured data to optimized vector search indices. This allows for retrieval-augmented generation pipelines. It allows users to import documents, or connect to external systems of knowledge management to extract natural languages suitable for LLMs. The platform evaluates chunking and embedding methods in parallel. It provides recommendations or allows users to choose the method they prefer. Vectorize automatically updates a real-time pipeline vector with any changes to data once a vector configuration has been selected. This ensures accurate search results. The platform provides connectors for various knowledge repositories and collaboration platforms as well as CRMs. This allows seamless integration of data in generative AI applications. Vectorize also supports the creation and update of vector indexes within preferred vector databases.

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.

Substrate is a platform for agentic AI. Elegant abstractions, high-performance components such as optimized models, vector databases, code interpreter and model router, as well as vector databases, code interpreter and model router. Substrate was designed to run multistep AI workloads. Substrate will run your task as fast as it can by connecting components. We analyze your workload in the form of a directed acyclic network and optimize it, for example merging nodes which can be run as a batch. Substrate's inference engine schedules your workflow graph automatically with optimized parallelism. This reduces the complexity of chaining several inference APIs. Substrate will parallelize your workload without any async programming. Just connect nodes to let Substrate do the work. Our infrastructure ensures that your entire workload runs on the same cluster and often on the same computer. You won't waste fractions of a sec per task on unnecessary data transport and cross-regional HTTP transport.

Deliver high-quality answers with a database that is built for advanced retrieval, augmented generation (RAG), and modern search. Focus on exponential growth using a vector database built for enterprise that includes security, compliance and responsible AI practices. With sophisticated retrieval strategies that are backed by decades worth of research and validation from customers, you can build better applications. Rapidly deploy your generative AI application with seamless platform and integrations of data sources, AI models and frameworks. Upload data automatically from a variety of supported Azure and 3rd-party sources. Streamline vector data with integrated extraction, chunking and enrichment. Support for multivectors, hybrids, multilinguals, and metadata filters. You can go beyond vector-only searching with keyword match scoring and reranking. Also, you can use geospatial searches, autocomplete, and geospatial search.

Belva's AiDB, an AI-optimized data base, is designed to enhance large languages models by automatically creating relational mappings that make your LLMs smarter with every new input. This results in fewer context tokens being used and better results without any extra tuning. With just 15 lines of code you can create a knowledgebase that improves AI performance and reduces context token usage. It also scales easily. AiDB can be set up in just 5 minutes. This is faster than custom RAG systems. AiDB only requires one API key. Switch to AiDB and see how your LLMs can get more done in just 15 lines of coding. Belva has reimagined the way AI uses data. Context windows are almost obsolete with our unique indexing, relational mapping and relational mapping. Integrate AiDB in your stack and watch your AI soar. AiDB is required if your AI needs or uses a knowledge database. Better performance means less waste when scaled up.

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.

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

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.

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.

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.

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.

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.

The Databricks Data Intelligence Platform enables your entire organization to utilize data and AI. It is built on a lakehouse that provides an open, unified platform for all data and governance. It's powered by a Data Intelligence Engine, which understands the uniqueness in your data. Data and AI companies will win in every industry. Databricks can help you achieve your data and AI goals faster and easier. Databricks combines the benefits of a lakehouse with generative AI to power a Data Intelligence Engine which understands the unique semantics in your data. The Databricks Platform can then optimize performance and manage infrastructure according to the unique needs of your business. The Data Intelligence Engine speaks your organization's native language, making it easy to search for and discover new data. It is just like asking a colleague a question.

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.

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.

We've already done the hard work of bundling, pre-configuring and integrating all the components that you need to build solutions and integrate LLMs into your business processes. ConfidentialMind allows you to jump into action. Deploy an endpoint for powerful open-source LLMs such as Llama-2 and turn it into an LLM API. Imagine ChatGPT on your own cloud. This is the most secure option available. Connects the rest with the APIs from the largest hosted LLM provider like Azure OpenAI or AWS Bedrock. ConfidentialMind deploys a Streamlit-based playground UI with a selection LLM-powered productivity tool for your company, such as writing assistants or document analysts. Includes a vector data base, which is critical for most LLM applications to efficiently navigate through large knowledge bases with thousands documents. You can control who has access to your team's solutions and what data they have.

The most innovative cloud database service available, with unmatched data mobility across AWS, Azure and Google Cloud, as well as built-in automation for resource optimization and workload optimization. MongoDB Atlas is a global cloud database service that supports modern applications. Fully managed MongoDB can be deployed across AWS, Google Cloud and Azure. This is possible with best-in class automation and proven practices that ensure availability, scalability and compliance with the highest data security and privacy standards. This is the best way to deploy, scale, and run MongoDB in cloud. MongoDB Atlas provides security controls for all data. Allow enterprise-grade features to be integrated with your existing security protocols or compliance standards. MongoDB Atlas protects your data with preconfigured security features that allow for authentication, authorization and encryption.

A modern Postgres dataplatform for operators, developers and data engineers. AI builders can also use it to power mission-critical workloads. Flexible deployment across hybrid cloud and multi-cloud. EDB Postgres is the first intelligent data-platform for transactional, analytic, and new AI workloads, powered by a Postgres engine enhanced. It can be deployed either as a cloud managed service, as self-managed software or as a physical device. It provides built-in observability and AI-driven assistance. It also includes migration tooling and a single pane-of-glass for managing hybrid data estates. EDB Postgres AI elevates data infrastructure into a strategic technology asset, bringing analytical and AI systems close to customers' core transactional and operational data. All managed through Postgres, the world's most popular database. Modernize legacy systems with the most comprehensive Oracle compatibility and a suite migration tools to get customers onboard.

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.

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.

ParadeDB adds column-oriented storage to Postgres tables and vectorized query processing. Users can choose between column- and row-oriented storage when creating tables. Column-oriented tables can be stored as Parquet and managed by Delta Lake. Search by keyword, with BM25 scoring and configurable tokenizers. Multi-language support. Search by semantic meaning, with support for dense and sparse vectors. Combining the strengths of both full text and similarity searches, you can get better results. ParadeDB is ACID compliant and has concurrency control across all transactions. ParadeDB integrates seamlessly with the Postgres ecosystem including clients, extensions and libraries.

Dgraph is an open-source, low-latency, high throughput native and distributed graph database. DGraph is designed to scale easily to meet the needs for small startups and large companies with huge amounts of data. It can handle terabytes structured data on commodity hardware with low latency to respond to user queries. It addresses business needs and can be used in cases that involve diverse social and knowledge networks, real-time recommendation engines and semantic search, pattern matching, fraud detection, serving relationship information, and serving web applications.

Our customers can leverage their data fabric to achieve remarkable outcomes in automation, data-driven decision making and other areas. We provide a complete view into the data supply chain through ingestion, indexing and data remediations. This allows businesses to scale, increase their profitability, and realize measurable impact. Through a robust, scalable data pipeline, we can ingestion data from client data lakes to secure storage systems. Advanced image classification techniques and NLP techniques allow for quick validation, classification, and categorizing of scanned documents. The user can search the data using a query interface and personalizable dashboard. The results will be presented in readable format so that they can bookmark, filter, or restructure the view.

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.

Build your RAG system intuitively with fewer abstractions than solutions like LangChain. You can choose from a variety of hosted and remote providers, including vector databases, datasets and Large Language Models. SciPhi allows you to version control and deploy your system from anywhere using Git. SciPhi's platform is used to manage and deploy an embedded semantic search engine that has over 1 billion passages. The team at SciPhi can help you embed and index your initial dataset into a vector database. The vector database will be integrated into your SciPhi workspace along with your chosen LLM provider.

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.

Cloud-hosted AI packages that are managed and cloud-hosted will make it easier to ship AI faster. GPT-4 support is fully integrated. API tokens do not need to be used. Use our low-code framework to build. All major models can be integrated. Get an instant API by deploying. Scale and share your API without having to manage infrastructure. Make prompts, prompt chains, basic Python, and managed APIs. A clever prompt can be turned into a publicly available API that you can share. Python allows you to add logic and routing smarts. Steamship connects with your favorite models and services, so you don't need to learn a different API for each provider. Steamship maintains model output in a standard format. Consolidate training and inference, vector search, endpoint hosting. Import, transcribe or generate text. It can run all the models that you need. ShipQL allows you to query across all the results. Packages are fully-stack, cloud-hosted AI applications. Each instance you create gives you an API and private data workspace.

Hydrolix is a streaming lake of data that combines decoupled archiving, indexed searching, and stream processing for real-time query performance on terabyte scale at a dramatically lower cost. CFOs love that data retention costs are 4x lower. Product teams appreciate having 4x more data at their disposal. Scale up resources when needed and down when not. Control costs by fine-tuning resource consumption and performance based on workload. Imagine what you could build if you didn't have budget constraints. Log data from Kafka, Kinesis and HTTP can be ingested, enhanced and transformed. No matter how large your data, you will only get the data that you need. Reduce latency, costs, and eliminate timeouts and brute-force queries. Storage is decoupled with ingest and queries, allowing them to scale independently to meet performance and cost targets. Hydrolix's HDX (high-density compress) reduces 1TB to 55GB.

Turso, a globally distributed SQLite compatible database service, is designed to provide low latency data access on various platforms including online, off-line, and on devices. Turso, which is built on libSQL (an open-source fork SQLite), allows developers to deploy databases closer to their users and enhance application performance. It integrates seamlessly with multiple frameworks and infrastructure providers. This allows for efficient data management in applications such as AI agents and large language models. Turso provides features such as unlimited databases, instant branching with rollback, and native vector searches at scale. This allows for efficient parallel vector searching across users, instances or contexts by using SQL database integration. The platform is designed to be secure, with encryption in transit and at rest. It also offers an API-first approach to programmatic database management.

The AI character developer platform. Get a fully-integrated platform for AI characters, which goes beyond large language model (LLMs) and adds configurable knowledge, memory and narrative controls. Create characters with distinct personalities, contextual awareness and brand consistency. Integrate seamlessly into real-time apps, with built-in optimization for performance and scale. Inworld's low-latency interactions are optimized for real-time experiences and scale with your application. We can deliver high-quality interaction with faster inferences and lower costs by orchestrating across LLMs. Models need to be aware that every interaction has a context. Add custom knowledge, safety and content guardrails, as well as narrative controls, to keep your AI in character or in-world. Put personality at its core. Our multimodal AI mimics human expression in all its forms.

The Exa API uses embeddings to search for the best content available on the web. Exa understands meaning and gives results that search engines cannot. Exa uses a new link prediction transformer to predict the links that match the meaning of an prompt. Search with our SOTA web embeddeddings model instead of our custom index for queries that require semantic understanding. We offer keyword-based searches for all other queries. Stop learning HTML parsing or web scraping. Get the full, clean text of any page from our index or intelligently embedded highlights related to your query. You can select any date range and include or exclude any domain. You can also choose a custom data vertical or get up 10 million results.

AWS HealthImaging was designed for developers who create cloud-native applications for medical imaging. HealthImaging ingests DICOM P10 data and provides APIs to retrieve and store it with low latency. By using advanced compression and storing only one copy of each image, you can reduce costs by up to 40%. Cloud-native APIs, applications and APIs allow you to access medical imaging data from anywhere with a subsecond latency. Reduce the burden of managing infrastructure to focus on providing high-quality patient healthcare. Major medical imaging vendors support cloud-native workflows for medical imaging. Store and stream medical images directly from AWS, while maintaining low-latency performance. Save money on long-term archiving while maintaining access to images in subseconds. With the help of other tools and services, run artificial intelligence and machine-learning inferences over the imaging archives.