Deep Lake 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.

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.

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.

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.

Klu.ai is a Generative AI platform that simplifies the process of designing, deploying, and optimizing AI applications. Klu integrates with your preferred Large Language Models, incorporating data from varied sources, giving your applications unique context. Klu accelerates building applications using language models like Anthropic Claude, Azure OpenAI, GPT-4, and over 15 other models, allowing rapid prompt/model experimentation, data gathering and user feedback, and model fine-tuning while cost-effectively optimizing performance. Ship prompt generations, chat experiences, workflows, and autonomous workers in minutes. Klu provides SDKs and an API-first approach for all capabilities to enable developer productivity. Klu automatically provides abstractions for common LLM/GenAI use cases, including: LLM connectors, vector storage and retrieval, prompt templates, observability, and evaluation/testing tooling.

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.

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.

Capture production data, evaluate outputs collaboratively, and fine-tune your LLM's performance. Know exactly what goes on in production with an in-depth log overview. Collaborate with product managers, domain experts and engineers to build reliable model outputs. Track AI metrics such as speed, quality scores, and token usage. Copilot automates evaluations and model improvements for your use case. Create, manage, and optimize prompts to achieve precise and relevant interactions between users and AI models. Compare foundation models, and fine-tuned versions to improve prompt performance and save tokens. Collaborate with your team to build a proprietary fine-tuning dataset for your AI models. Build custom fine-tuning datasets to optimize model performance for specific use cases.

OpenPipe provides fine-tuning for developers. Keep your datasets, models, and evaluations all in one place. Train new models with the click of a button. Automatically record LLM requests and responses. Create datasets from your captured data. Train multiple base models on the same dataset. We serve your model on our managed endpoints that scale to millions of requests. Write evaluations and compare model outputs side by side. Change a couple of lines of code, and you're good to go. Simply replace your Python or Javascript OpenAI SDK and add an OpenPipe API key. Make your data searchable with custom tags. Small specialized models cost much less to run than large multipurpose LLMs. Replace prompts with models in minutes, not weeks. Fine-tuned Mistral and Llama 2 models consistently outperform GPT-4-1106-Turbo, at a fraction of the cost. We're open-source, and so are many of the base models we use. Own your own weights when you fine-tune Mistral and Llama 2, and download them at any time.

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.

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.

Query and compare a large selection of open-source and proprietary models at once. Replace costly APIs with cheap custom AI models. Customize foundational models on your private data to adapt them to your particular use case. Small fine-tuned models can perform on par with GPT-4 and are up to 90% cheaper. Airtrain’s LLM-assisted scoring simplifies model grading using your task descriptions. Serve your custom models from the Airtrain API in the cloud or within your secure infrastructure. Evaluate and compare open-source and proprietary models across your entire dataset with custom properties. Airtrain’s powerful AI evaluators let you score models along arbitrary properties for a fully customized evaluation. Find out what model generates outputs compliant with the JSON schema required by your agents and applications. Your dataset gets scored across models with standalone metrics such as length, compression, coverage.

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.

Entry Point AI is the modern AI optimization platform for proprietary and open source language models. Manage prompts, fine-tunes, and evals all in one place. When you reach the limits of prompt engineering, it’s time to fine-tune a model, and we make it easy. Fine-tuning is showing a model how to behave, not telling. It works together with prompt engineering and retrieval-augmented generation (RAG) to leverage the full potential of AI models. Fine-tuning can help you to get better quality from your prompts. Think of it like an upgrade to few-shot learning that bakes the examples into the model itself. For simpler tasks, you can train a lighter model to perform at or above the level of a higher-quality model, greatly reducing latency and cost. Train your model not to respond in certain ways to users, for safety, to protect your brand, and to get the formatting right. Cover edge cases and steer model behavior by adding examples to your dataset.

Unlock 3-5x better prices than traditional clouds. FluidStack aggregates under-utilized GPUs from data centers around the world to deliver the industry’s best economics. Deploy 50,000+ high-performance servers in seconds via a single platform and API. Access large-scale A100 and H100 clusters with InfiniBand in days. Train, fine-tune, and deploy LLMs on thousands of affordable GPUs in minutes with FluidStack. FluidStack unites individual data centers to overcome monopolistic GPU cloud pricing. Compute 5x faster while making the cloud efficient. Instantly access 47,000+ unused servers with tier 4 uptime and security from one simple interface. Train larger models, deploy Kubernetes clusters, render quicker, and stream with no latency. Setup in one click with custom images and APIs to deploy in seconds. 24/7 direct support via Slack, emails, or calls, our engineers are an extension of your team.

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.

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.

You can now fine-tune GPT with absolutely zero technical skills. Enhance AI models by tailoring them to your specific needs. Prompteasy.ai helps you fine-tune AI models in a matter of seconds. We make AI tailored to your needs by helping you fine-tune it. The best part is, that you don't even have to know AI fine-tuning. Our AI models will take care of everything. We will be offering prompteasy for free as part of our initial launch. We'll be rolling out pricing plans later this year. Our vision is to make AI smart and easily accessible to anyone. We believe that the true power of AI lies in how we train and orchestrate the foundational models, as opposed to just using them off the shelf. Forget generating massive datasets, just upload relevant materials and interact with our AI through natural language. We take care of building the dataset ready for fine-tuning. You just chat with the AI, download the dataset, and fine-tune GPT.

Build and optimize text and image AI for your needs, train, fine-tune, and generate from your data. We use best-in-class open source models for image and language generation and can train them in minutes thanks to LoRA fine-tuning. Click the share button to create a link to your session, or create a bot. Optionally deploy to your own fully private infrastructure. You can start chatting with open source language models and generating images with Stable Diffusion XL by creating a free account right now. Fine-tuning your model on your own text or image data is as simple as drag’n’drop, and takes 3-10 minutes. You can then chat with and generate images from those fine-tuned models straight away, all using a familiar chat interface.

The quickest way to get started with our illustration generator is to use pre-made models. If you want to depict a style or an object that is not available in the premade models you can train your own fine tune by uploading 5-15 illustrations. there are no limits to fine-tuning you can use it for illustrations icons or any assets you need. Read more about fine-tuning. Illustrations are exportable in PNG and SVG formats. Fine-tuning allows you to train the stable-diffusion AI model, on a particular object or style, and create a new model that generates images of those objects or styles. The fine-tuning will be only as good as the data you provide. Around 5-15 images are recommended for fine-tuning. Images can be of any unique object or style. Images should contain only the subject itself, without background noise or other objects. Images must not include any gradients or shadows if you want to export it as SVG later. PNG export still works fine with gradients and shadows.

Lamini makes it possible for enterprises to turn proprietary data into the next generation of LLM capabilities, by offering a platform for in-house software teams to uplevel to OpenAI-level AI teams and to build within the security of their existing infrastructure. Guaranteed structured output with optimized JSON decoding. Photographic memory through retrieval-augmented fine-tuning. Improve accuracy, and dramatically reduce hallucinations. Highly parallelized inference for large batch inference. Parameter-efficient finetuning that scales to millions of production adapters. Lamini is the only company that enables enterprise companies to safely and quickly develop and control their own LLMs anywhere. It brings several of the latest technologies and research to bear that was able to make ChatGPT from GPT-3, as well as Github Copilot from Codex. These include, among others, fine-tuning, RLHF, retrieval-augmented training, data augmentation, and GPU optimization.

Fine-tuning happens when you take a dataset and build out a model to use with AI. It isn't always easy to do this without code so we built a solution into RIku which handles everything in a very simple format. Fine-tuning unlocks a whole new level of power for AI and we're excited to help you explore it. Public Share Links are individual landing pages that you can create for any of your prompts. You can design these with your brand in mind in terms of colors and adding a logo and your own welcome text. Share these links with anyone publicly and if they have the password to unlock it, they will be able to make generations. A no-code writing assistant builder on a micro scale for your audience! One of the big headaches we found with projects using multiple large language models is that they all return their outputs slightly differently.

Whether prompt engineering, fine-tuning, or training, we are ready to meet your business demands. Easily integrate your new model into your production application using the Together Inference API. With the fastest performance available and elastic scaling, Together AI is built to scale with your needs as you grow. Inspect how models are trained and what data is used to increase accuracy and minimize risks. You own the model you fine-tune, not your cloud provider. Change providers for whatever reason, including price changes. Maintain complete data privacy by storing data locally or in our secure cloud.

With our enterprise Gen AI stack, go beyond your imagination and offload manual tasks to powerful assistants instantly – the sky is the limit. For enterprises where data security is paramount, fine-tune and deploy generative AI models on your own cloud, securely.

AI agents that interact with users, answer questions, and complete tasks, using your internal data and APIs. AI that answers questions, summarize, and extract insights from any document, no matter how long. Generate tags, summaries, and transfer styles or formats between documents and data sources. Developer teams use Stack AI to automate customer support, process documents, qualify sales leads, and search through libraries of data. Try multiple prompts and LLM architectures with the ease of a button. Collect data and run fine-tuning jobs to build the optimal LLM for your product. We host all your workflows as APIs so that your users can access AI instantly. Select from the different LLM providers to compare fine-tuning jobs that satisfy your accuracy, price, and latency needs.

Select from our library, fine-tune, or build your own capabilities to analyze and process text, audio and video at scale. Integrate advanced NLP into your app or workflow. Select from the library or build your own. Summarize, tag and analyze language with stackable, composable NLP building blocks, built on state-of-the-art models, all with a single API call. Build and fine-tune custom Language Skills with your data using our powerful Custom-Skill engine. Only 5% of the world's population speaks English as their native language. Most of One AI’s capabilities are multilingual. So whether you build a podcast platform, CRM, content publishing tool, or any other product, the language detection, processing, transcription, analytics, and comprehension capabilities are here.

Eye-balling a few examples isn't enough. Collect end-user feedback at scale to unlock actionable insights on how to improve your models. Easily A/B test models and prompts with the improvement engine built for GPT. Prompts only get your so far. Get higher quality results by fine-tuning on your best data – no coding or data science required. Integration in a single line of code. Experiment with Claude, ChatGPT and other language model providers without touching it again. You can build defensible and innovative products on top of powerful APIs – if you have the right tools to customize the models for your customers. Copy AI fine tune models on their best data, enabling cost savings and a competitive advantage. Enabling magical product experiences that delight over 2 million active users.

Fine-tune and get completions on private LLMs with a simple web API. No infrastructure is needed. Build private, SOC2-compliant AI applications instantly. Personalize models to your use case easily with our developer platform. Simply define the data you want to teach it and pick the base model - we take care of the rest. Put private LLMs into applications with a single API call, no more dealing with deployment, orchestration, or infrastructure hassles. The most powerful OSS model available—highly generalized capabilities with amazing narrative and reasoning capabilities. Harness a fully unlocked LLM to build the highest quality internal automation systems for your company.

Describe example API requests and responses. Define API endpoint logic in natural language. Test your API endpoints and fine-tune your prompt, response structure, and request structure. Deploy API endpoints with a single click and integrate into your applications. Build and deploy sophisticated application logic without writing any code in less than a minute. No individual LLM accounts required. Just sign up to Backengine and start building. Your endpoints run on our super fast backend architecture, available immediately. All endpoints are secure and protected so only you and your applications can use them. Easily manage your team members so everyone can work on your Backengine endpoints. Augment your Backengine endpoints with persistent data. A complete backend replacement. Use external APIs into your endpoints without doing any integration work yourself.

Optimizing continual pre-training for model enrichment with proprietary data. Ensuring that domain-specific models offer a smooth experience. Creating a production-friendly RAG pipeline that offers ongoing support. With Arcee's SLM Adaptation system, you do not have to worry about fine-tuning, infrastructure set-up, and all the other complexities involved in stitching together solutions using a plethora of not-built-for-purpose tools. Thanks to the domain adaptability of our product, you can efficiently train and deploy your own SLMs across a plethora of use cases, whether it is for internal tooling, or for your customers. By training and deploying your SLMs with Arcee’s end-to-end VPC service, you can rest assured that what is yours, stays yours.

We just launched X1 large series of Models. Giga ML's most powerful model is available for pre-training and fine-tuning with on-prem deployment. Since we are Open AI compatible, your existing integrations with long chain, llama-index, and all others work seamlessly. You can continue pre-training of LLM's with domain-specific data books or docs or company docs. The world of large language models (LLMs) rapidly expanding, offering unprecedented opportunities for natural language processing across various domains. However, some critical challenges have remained unaddressed. At Giga ML, we proudly introduce the X1 Large 32k model, a pioneering on-premise LLM solution that addresses these critical issues.

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.

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.

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.

Vespa is forBig Data + AI, online. At any scale, with unbeatable performance. To build production-worthy online applications that combine data and AI, you need more than point solutions: You need a platform that integrates data and compute to achieve true scalability and availability - and which does this without limiting your freedom to innovate. Only Vespa does this. Vespa is a fully featured search engine and vector database. It supports vector search (ANN), lexical search, and search in structured data, all in the same query. Users can easily build recommendation applications on Vespa. Integrated machine-learned model inference allows you to apply AI to make sense of your data in real-time. Together with Vespa's proven scaling and high availability, this empowers you to create production-ready search applications at any scale and with any combination of features.

Fast and accurate AI models suited for production. Highly-available inference API leveraging the most advanced NVIDIA GPUs. We selected the best open-source natural language processing (NLP) models from the community and deployed them for you. Fine-tune your own models - including GPT-J - or upload your in-house custom models, and deploy them easily to production. Upload or Train/Fine-Tune your own AI models - including GPT-J - from your dashboard, and use them straight away in production without worrying about deployment considerations like RAM usage, high-availability, scalability... You can upload and deploy as many models as you want to production.

Generative AI and Large Language Models (LLMs) present a promising solution to unlock the untapped value of unstructured data, providing enterprises with instant access to valuable insights. This has opened up new possibilities for businesses to reimagine customer experience, products, and services, and increase productivity for their teams. baioniq is Quantiphi's enterprise-ready Generative AI Platform on AWS is designed to help organizations rapidly onboard generative AI capabilities and apply them to domain-specific tasks. For AWS customers, baioniq is containerized and deployed on AWS. It provides a modular solution that allows modern enterprises to fine-tune LLMs to incorporate domain-specific data and perform enterprise-specific tasks in four simple steps.

The first integrated hardware and software compute protocol to train and fine-tune your AI models. Lower your training costs by up to 80%. Deploy in seconds with open-source model templates or bring your own model. Seamlessly debug containers with access to GPU, CPU, Memory, and other metrics. You can monitor logs in real time. Trace all models and training sets with cryptographic verified proofs for complete accountability. Control the entire training workflow with a few simple commands. Earn block rewards for adding your computer to the network. Track key metrics such as connectivity and uptime.

Instill Core is an all-in-one AI infrastructure tool for data, model, and pipeline orchestration, streamlining the creation of AI-first applications. Access is easy via Instill Cloud or by self-hosting from the instill-core GitHub repository. Instill Core includes: Instill VDP: The Versatile Data Pipeline (VDP), designed for unstructured data ETL challenges, providing robust pipeline orchestration. Instill Model: An MLOps/LLMOps platform that ensures seamless model serving, fine-tuning, and monitoring for optimal performance with unstructured data ETL. Instill Artifact: Facilitates data orchestration for unified unstructured data representation. Instill Core simplifies the development and management of sophisticated AI workflows, making it indispensable for developers and data scientists leveraging AI technologies.

Langtail is an end-to-end platform that accelerates the development and deployment of language model (LLM) applications. It enables companies to rapidly experiment, collaborate, and launch production-grade LLM products. Key features include: 1. No-code LLM playground for prompt debugging and ideation 2. Collaborative workspaces for sharing prompts and insights 3. Comprehensive observability suite with logging and analytics 4. Evaluation framework for systematically testing prompt performance 5. Deployment infrastructure for serving prompts via API in multiple environments 6. Upcoming fine-tuning capabilities to improve models with user feedback Langtail empowers both technical and non-technical teams to find high-value LLM use cases, refine prompts for reliable performance, and deploy applications with ease. It's the all-in-one platform to take your LLM projects from prototype to production faster than ever.

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.

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.

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.

Powerful language models a click away. Join over 8,000 developers building the next wave of world-changing applications. Fine-tune and deploy GPT-J, GPT-NeoX, Codegen, and FLAN-T5. Multiple models, each with different capabilities and price points. GPT-J is the fastest model, while GPT-NeoX is the most powerful—and more are on the way. Use these models for classification, entity extraction, code generation, chatbots, content generation, summarization, paraphrasing, sentiment analysis, and much more. These models have been pre-trained on a vast amount of text from the open internet. Fine-tuning improves upon this for specific tasks by training on many more examples than can fit in a prompt, letting you achieve better results on a wide number of tasks.

vishwa.ai is an AutoOps platform for AI and ML use cases. It provides expert prompt delivery, fine-tuning, and monitoring of Large Language Models (LLMs). Features: Expert Prompt Delivery: Tailored prompts for various applications. Create no-code LLM Apps: Build LLM workflows in no time with our drag-n-drop UI Advanced Fine-Tuning: Customization of AI models. LLM Monitoring: Comprehensive oversight of model performance. Integration and Security Cloud Integration: Supports Google Cloud, AWS, Azure. Secure LLM Integration: Safe connection with LLM providers. Automated Observability: For efficient LLM management. Managed Self-Hosting: Dedicated hosting solutions. Access Control and Audits: Ensuring secure and compliant operations.

Google AI Studio is a free, web-based tool that allows individuals and small teams to develop apps and chatbots using natural-language prompting. It also allows users to create prompts and API keys for app development. Google AI Studio is a development environment that allows users to discover Gemini Pro APIs, create prompts, and fine-tune Gemini. It also offers a generous free quota, allowing 60 requests per minute. Google also has a Generative AI Studio, which is a product on Vertex AI. It includes models of different types, allowing users to generate content that may be text, image, or audio.

Your platform for developing generative AI solutions and custom copilots. Build solutions faster, using pre-built and customizable AI models on your data—securely—to innovate at scale. Explore a robust and growing catalog of pre-built and customizable frontier and open-source models. Create AI models with a code-first experience and accessible UI validated by developers with disabilities. Seamlessly integrate all your data from OneLake in Microsoft Fabric. Integrate with GitHub Codespaces, Semantic Kernel, and LangChain. Access prebuilt capabilities to build apps quickly. Personalize content and interactions and reduce wait times. Lower the burden of risk and aid in new discoveries for organizations. Decrease the chance of human error using data and tools. Automate operations to refocus employees on more critical tasks.

Explore a new library or dataset in a notebook. Automate preprocessing, training, or testing with a 2orkflow. Bring your application to life with a deployment. Use notebooks, workflows, and deployments together or independently. Compatible with everything. Gradient supports all major frameworks and libraries. Gradient is powered by Paperspace's world-class GPU instances. Move faster with source control integration. Connect to GitHub to manage all your work & compute resources with git. Launch a GPU-enabled Jupyter Notebook from your browser in seconds. Use any library or framework. Easily invite collaborators or share a public link. A simple cloud workspace that runs on free GPUs. Get started in seconds with a notebook environment that's easy to use and share. Perfect for ML developers. A powerful no-fuss environment with loads of features that just works. Choose a pre-built template or bring your own. Try a free GPU!

The most flexible data annotation tool. Quickly installable. Build custom UIs or use pre-built labeling templates. Configurable layouts and templates adapt to your dataset and workflow. Detect objects on images, boxes, polygons, circular, and key points supported. Partition the image into multiple segments. Use ML models to pre-label and optimize the process. Webhooks, Python SDK, and API allow you to authenticate, create projects, import tasks, manage model predictions, and more. Save time by using predictions to assist your labeling process with ML backend integration. Connect to cloud object storage and label data there directly with S3 and GCP. Prepare and manage your dataset in our Data Manager using advanced filters. Support multiple projects, use cases, and data types in one platform. Start typing in the config, and you can quickly preview the labeling interface. At the bottom of the page, you have live serialization updates of what Label Studio expects as an input.

Virtualization Software for AI Infrastructure. Gain visibility and control over AI workloads to increase GPU utilization. Run:AI has built the world’s first virtualization layer for deep learning training models. By abstracting workloads from underlying infrastructure, Run:AI creates a shared pool of resources that can be dynamically provisioned, enabling full utilization of expensive GPU resources. Gain control over the allocation of expensive GPU resources. Run:AI’s scheduling mechanism enables IT to control, prioritize and align data science computing needs with business goals. Using Run:AI’s advanced monitoring tools, queueing mechanisms, and automatic preemption of jobs based on priorities, IT gains full control over GPU utilization. By creating a flexible ‘virtual pool’ of compute resources, IT leaders can visualize their full infrastructure capacity and utilization across sites, whether on premises or in the cloud.