Alternatives to SuperDuperDB

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.

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.

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.

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.

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.

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.

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.

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.

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.

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 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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

IBM Watson Machine Learning, a full-service IBM Cloud offering, makes it easy for data scientists and developers to work together to integrate predictive capabilities into their applications. The Machine Learning service provides a set REST APIs that can be called from any programming language. This allows you to create applications that make better decisions, solve difficult problems, and improve user outcomes. Machine learning models management (continuous-learning system) and deployment (online batch, streaming, or online) are available. You can choose from any of the widely supported machine-learning frameworks: TensorFlow and Keras, Caffe or PyTorch. Spark MLlib, scikit Learn, xgboost, SPSS, Spark MLlib, Keras, Caffe and Keras. To manage your artifacts, you can use the Python client and command-line interface. The Watson Machine Learning REST API allows you to extend your application with artificial intelligence.

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.

Klu.ai, a Generative AI Platform, simplifies the design, deployment, and optimization of AI applications. Klu integrates your Large Language Models and incorporates data from diverse sources to give your applications unique context. Klu accelerates the building of applications using language models such as Anthropic Claude (Azure OpenAI), GPT-4 (Google's GPT-4), and over 15 others. It allows rapid prompt/model experiments, data collection and user feedback and model fine tuning while cost-effectively optimising performance. Ship prompt generation, chat experiences and workflows in minutes. Klu offers SDKs for all capabilities and an API-first strategy to enable developer productivity. Klu automatically provides abstractions to common LLM/GenAI usage cases, such as: LLM connectors and vector storage, prompt templates, observability and evaluation/testing tools.

OpenPipe provides fine-tuning for developers. Keep all your models, datasets, and evaluations in one place. New models can be trained with a click of a mouse. Automatically record LLM responses and requests. Create datasets using your captured data. Train multiple base models using the same dataset. We can scale your model to millions of requests on our managed endpoints. Write evaluations and compare outputs of models side by side. You only need to change a few lines of code. OpenPipe API Key can be added to your Python or Javascript OpenAI SDK. Custom tags make your data searchable. Small, specialized models are much cheaper to run than large, multipurpose LLMs. Replace prompts in minutes instead of weeks. Mistral and Llama 2 models that are fine-tuned consistently outperform GPT-4-1106 Turbo, at a fraction the cost. Many of the base models that we use are open-source. You can download your own weights at any time when you fine-tune Mistral or Llama 2.

Carbon is a cost-effective alternative to expensive pipelines. You only pay monthly for usage. Utilise less and spend less with our usage-based pricing; use more and save more. Use our ready-made components for file uploading, web scraping, and 3rd party verification. A rich library of APIs designed for developers that import AI-focused data. Create and retrieve chunks, embeddings and data from all sources. Unstructured data can be searched using enterprise-grade keyword and semantic search. Carbon manages OAuth flows from 10+ sources. It transforms source data to vector store-optimized files and handles data synchronization automatically.

Accelerate the entire machine learning lifecycle. Developers and data scientists can have more productive experiences building, training, and deploying machine-learning models faster by empowering them. Accelerate time-to-market and foster collaboration with industry-leading MLOps -DevOps machine learning. Innovate on a trusted platform that is secure and trustworthy, which is designed for responsible ML. Productivity for all levels, code-first and drag and drop designer, and automated machine-learning. Robust MLOps capabilities integrate with existing DevOps processes to help manage the entire ML lifecycle. Responsible ML capabilities – understand models with interpretability, fairness, and protect data with differential privacy, confidential computing, as well as control the ML cycle with datasheets and audit trials. Open-source languages and frameworks supported by the best in class, including MLflow and Kubeflow, ONNX and PyTorch. TensorFlow and Python are also supported.

A system that can scale to millions of users, without requiring an engineering team. Create, customize and deploy your chat-based AI. Finance chatbot. xFinance is a 13-billion-parameter model fine-tuned using LoRA. Our goal was show that impressive results can be achieved in financial NLP without breaking the bank. Your own AI assistant to chat with documents. Single or multiple documents. Simple or complex questions. Easy-to-use deep learning platform, hardware efficient algorithms that speed up inference and lower costs. Real-time monitoring and logging of resource usage and cloud costs for deployed models. xTuring, an open-source AI software for personalization, is a powerful tool. xTuring provides a simple interface for personalizing LLMs based on your data and application.

TorchScript allows you to seamlessly switch between graph and eager modes. TorchServe accelerates the path to production. The torch-distributed backend allows for distributed training and performance optimization in production and research. PyTorch is supported by a rich ecosystem of libraries and tools that supports NLP, computer vision, and other areas. PyTorch is well-supported on major cloud platforms, allowing for frictionless development and easy scaling. Select your preferences, then run the install command. Stable is the most current supported and tested version of PyTorch. This version should be compatible with many users. Preview is available for those who want the latest, but not fully tested, and supported 1.10 builds that are generated every night. Please ensure you have met the prerequisites, such as numpy, depending on which package manager you use. Anaconda is our preferred package manager, as it installs all dependencies.

Multi-model AI can help you build a better AI. AI models are an entirely new type of building block. Sieve makes it easy to use these building block to understand audio, create video, and more. The latest models are available in just a few line of code and there is a set of production-ready applications for many different use cases. Import your favorite models like Python packages. Visualize results using auto-generated interfaces created for your entire team. Easily deploy custom code. Define your environment computation in code and deploy it with a single command. Fast, scalable infrastructure with no hassle. Sieve is designed to scale automatically as your traffic grows with no extra configuration. Package models using a simple Python decorator, and deploy them instantly. A fully-featured observability layer that allows you to see what's going on under the hood. Pay only for the seconds you use. Take full control of your costs.

No more complicated templates and file restrictions. Integrate LLMs such as ChatGPT easily with vector databases, OCR PDF, and more. Chain prompts and transforms to create tailor-made AI experiences. From templates to adaptive chains. Our unique LLM features, such as quality control and semantic cache, can help you to save money and prevent hallucinations. We will take care of infrastructure management, hosting and scaling. Relevance AI will do the heavy lifting in just minutes. It can extract data from unstructured data in a flexible way. Relevance AI allows the team to extract data with over 90% accuracy within an hour.

You can build, run, and manage AI models and optimize decisions across any cloud. IBM Watson Studio allows you to deploy AI anywhere with IBM Cloud Pak®, the IBM data and AI platform. Open, flexible, multicloud architecture allows you to unite teams, simplify the AI lifecycle management, and accelerate time-to-value. ModelOps pipelines automate the AI lifecycle. AutoAI accelerates data science development. AutoAI allows you to create and programmatically build models. One-click integration allows you to deploy and run models. Promoting AI governance through fair and explicable AI. Optimizing decisions can improve business results. Open source frameworks such as PyTorch and TensorFlow can be used, as well as scikit-learn. You can combine the development tools, including popular IDEs and Jupyter notebooks. JupterLab and CLIs. This includes languages like Python, R, and Scala. IBM Watson Studio automates the management of the AI lifecycle to help you build and scale AI with trust.

Monitor your LLMs during production and discover and correct hallucinations and errors related to accuracy and quality with LLM outputs. Check your outputs to see if they contain hallucinations, misinformation or other issues. Configurable for any LLM application. Segment data to analyze in depth your cost, accuracy and response times. To debug generation, you can search, sort and filter your inference calls and trace your queries, retrievals and responses. Explore your conversations to learn what your users feel and what they are saying. You can also find out which conversations were unsuccessful. Compare your performance metrics between different models and prompts. Our insights will guide you to the best model for each use case. Our evaluators analyze and improve the outputs by using your data, configurations and feedback.

Build complex backend agents using multiple steps with an action-based orchestration. All LLMs are supported. Build a fully customized UI without writing a line of code for your agent, and serve it on your own domain. Track your agent's every move, literally, to cope with the nondeterministic nature LLMs. Access control can be built at a finer grain for your application, data and agent. A fine-tuned, specialized model to accelerate software development. Automatically handle concurrency and rate limiting.

DeepSpeed is a deep learning optimization library that is open source for PyTorch. It is designed to reduce memory and computing power, and to train large distributed model with better parallelism using existing computer hardware. DeepSpeed is optimized to provide high throughput and low latency training. DeepSpeed can train DL-models with more than 100 billion parameters using the current generation GPU clusters. It can also train as many as 13 billion parameters on a single GPU. DeepSpeed, developed by Microsoft, aims to provide distributed training for large models. It's built using PyTorch which is a data parallelism specialist.

Make AI an integral part of your business. With the flexibility and power of a cloud native platform, you can build better, deploy faster and audit easier. Import models and search cataloged model from across your organization. Save time by quickly annotating data with model-in the-loop hinting. Flyte's integration with Chariot allows you to quickly create and launch custom workflows. Understand the full origin of your data, models and workflows. Deploy models wherever you need them. This includes edge and IoT applications. Data scientists are not the only ones who can get valuable insights from their data. With Chariot's low code interface, teams can collaborate effectively.

Even the most complex requests from users can be fulfilled by our advanced planning engine. Out-of-the-box automation, inside your product. Your users can ask the system to do amazing things by using simple text, such as "Please show me sales from last month and give me some suggestions". OpenCopilot can be integrated into your product by using our chat bubble. No coding knowledge is required. You can also use our SDKs for your copilot to blend in. You can also feed all kinds of data to your copilot and it will be capable of understanding it and offering help to your users. OpenCopilot can be hosted on your own website with a simple make install command. All paid plans come with personal support from our team. Your users can ask complicated questions that require multiple actions to be executed in one go. The single platform for building, managing, and deploying your next AI-powered features. You will be the first to get new features, which is great since we ship so much.

You can embed and store images, documents, and other information. No additional work required for high-performance retrieval workflows. Connect your data via the UI and API. Baseplate handles storage, embedding, and version control to ensure that your data is always up-to-date and in-sync. Hybrid Search with customized embeddings that are tailored to your data. No matter what type, size or domain of data you are searching for, you will get accurate results. Any LLM can be generated using data from your database. Connect search results to an App Builder prompt. It takes just a few clicks to deploy your app. Baseplate Endpoints allow you to collect logs, human feedback, etc. Baseplate Databases enable you to embed and store data in the same table with images, links, text, and other elements that make your LLM app great. You can edit your vectors via the UI or programmatically. We can version your data so that you don't have to worry about duplicates or stale data.

LlamaIndex, a "dataframework", is designed to help you create LLM apps. Connect semi-structured API data like Slack or Salesforce. LlamaIndex provides a flexible and simple data framework to connect custom data sources with large language models. LlamaIndex is a powerful tool to enhance your LLM applications. Connect your existing data formats and sources (APIs, PDFs, documents, SQL etc.). Use with a large-scale language model application. Store and index data for different uses. Integrate downstream vector stores and database providers. LlamaIndex is a query interface which accepts any input prompts over your data, and returns a knowledge augmented response. Connect unstructured data sources, such as PDFs, raw text files and images. Integrate structured data sources such as Excel, SQL etc. It provides ways to structure data (indices, charts) so that it can be used with LLMs.

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

Discuro is an all-in-one platform that allows developers to quickly build, test and consume complex AI workflows. Our UI makes it easy to define your workflow. When you are ready to execute, just make one API call to Discuro with any inputs and any meta-data. You can use an Orchestrator to feed the generated data back into GPT-3. Integrate with OpenAI to extract the data you need quickly. In minutes, create and consume your own flows. Everything you need to integrate OpenAI at scale has been built by us so that you can concentrate on the product. Integrating with OpenAI is not easy. We'll help you extract the data you need by collecting input/output descriptions. You can easily chain completions together to create large data sets. You can use our iterative input feature for GPT-3 output to feed back in and have us make successive calls to expand your data set. Easy to build and test complex, self-transforming AI workflows and datasets.

No one wants to have their AI respond to a client with outdated information. Neum AI provides accurate and current context for AI applications. Set up your data pipelines quickly by using built-in connectors. These include data sources such as Amazon S3 and Azure Blob Storage and vector stores such as Pinecone and Weaviate. Transform and embed your data using built-in connectors to embed models like OpenAI, Replicate and serverless functions such as Azure Functions and AWS Lambda. Use role-based controls to ensure that only the right people have access to specific vectors. Bring your own embedding model, vector stores, and sources. Ask us how you can run Neum AI on your own cloud.

It reduces cloud costs and frees users from vendor-lock-in. Configure your hardware resources such as GPU and memory and specify whether you prefer to use spot instances. dstack provision cloud resources, fetches code and forwards ports to secure access. You can access the cloud dev environment using your desktop IDE. Configure your hardware resources (GPU, RAM, etc.). Indicate whether you would like to use spot instances or on-demand instances. dstack automatically provision cloud resources, forward ports and secure access. Pre-train your own models and fine-tune them in any cloud, easily and cost-effectively. Do you want cloud resources to be provisioned automatically based on your configurations? You can access your data and store outputs artifacts by using declarative configurations or the Python SDK.

Marvin introduces the concept of AI Functions. These functions are different from conventional ones because they do not rely on source codes, but instead produce their outputs with AI on-demand by using an LLM runtime. You don't need to write complex code when using AI functions for tasks such as extracting entities from websites, scoring sentiment or categorizing data in your database. You can call the function and then describe your needs. Marvin introduces more flexible bots in addition to AI functions. Bots are AI assistants who can be programmed with specific instructions, personalities or roles. They can use custom plug-ins, leverage external knowledge and automatically create a thread history.

Lilac is a free open-source tool that allows data and AI practitioners improve their products through better data. Understanding your data is easy with powerful filtering and search. Work together with your team to create a single dataset. Use best practices for data curation to reduce the size of your dataset and training costs and time. Our diff viewer allows you to see how your pipeline affects your data. Clustering is an automatic technique that assigns categories to documents by analyzing their text content. Similar documents are then placed in the same category. This reveals your dataset's overall structure. Lilac uses LLMs and state-of-the art algorithms to cluster the data and assign descriptive, informative titles. We can use keyword search before we do advanced searches, such as concept or semantic searching.

Frontly is an advanced SaaS/web app builder that does not require any coding. It offers powerful features and a rapid development process. Frontly is different from other app builders in that it allows users to create apps and components using AI, to build and maintain a library of reusable components and to duplicate entire apps and web pages. Users can also share and sell their designs on Frontly's marketplace for revenue. OpenAI allows you to automate repetitive tasks, edit and generate content using structured output. Transform your spreadsheets with charts, tables, forms and more into stunning visual interfaces. Access controls allow you to control which data users see down to the individual row level. Add your logo, brand colours, and custom domain to create a professional client-facing portal. Create apps in minutes that display data in a customized visual interface, filter the data based on permissions, generate with AI and revise your data, and connect your external systems via workflow automation integrations.

Fireworks works with the leading generative AI researchers in the world to provide the best models at the fastest speed. Independently benchmarked for the fastest inference providers. Use models curated by Fireworks, or our multi-modal and functionality-calling models that we have trained in-house. Fireworks is also the 2nd most popular open-source model provider, and generates more than 1M images/day. Fireworks' OpenAI-compatible interface makes it simple to get started. Dedicated deployments of your models will ensure uptime and performance. Fireworks is HIPAA-compliant and SOC2-compliant and offers secure VPC connectivity and VPN connectivity. Own your data and models. Fireworks hosts serverless models, so there's no need for hardware configuration or deployment. Fireworks.ai provides a lightning fast inference platform to help you serve generative AI model.

Use tools to bring LLM-powered features into production, including tools for rapid engineering, semantic searching, version control, quantitative testing, and performance monitoring. Compatible with all major LLM providers. Develop an MVP quickly by experimenting with various prompts, parameters and even LLM providers. Vellum is a low-latency and highly reliable proxy for LLM providers. This allows you to make version controlled changes to your prompts without needing to change any code. Vellum collects inputs, outputs and user feedback. These data are used to build valuable testing datasets which can be used to verify future changes before going live. Include dynamically company-specific context to your prompts, without managing your own semantic searching infrastructure.

Use AI algorithms to receive intelligent suggestions about your API design. Automated recommendations tailored to your project will accelerate development. AI can automatically generate your database. Streamline deployment and increase productivity. Create and implement automated workflows to ensure a smooth, efficient development cycle. Customize automation processes to meet your project requirements. Workflows that are adaptable will allow you to create a personalized experience. Enjoy the flexibility to manage diverse data sources in a single, organized workspace. Workspaces can be designed to reflect the structure of projects. Create dedicated workspaces that can house multiple data sources seamlessly. Streamlining tasks by automating processes, increasing efficiency, and reducing the amount of manual effort. Each user has their own instance(s). Custom logic can be used to handle complex data operations.

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.