Alternatives to Metal

LM-Kit.NET is an enterprise-grade toolkit designed for seamlessly integrating generative AI into your .NET applications, fully supporting Windows, Linux, and macOS. Empower your C# and VB.NET projects with a flexible platform that simplifies the creation and orchestration of dynamic AI agents. Leverage efficient Small Language Models for on‑device inference, reducing computational load, minimizing latency, and enhancing security by processing data locally. Experience the power of Retrieval‑Augmented Generation (RAG) to boost accuracy and relevance, while advanced AI agents simplify complex workflows and accelerate development. Native SDKs ensure smooth integration and high performance across diverse platforms. With robust support for custom AI agent development and multi‑agent orchestration, LM‑Kit.NET streamlines prototyping, deployment, and scalability—enabling you to build smarter, faster, and more secure solutions trusted by professionals worldwide.

Amazon SageMaker, a fully managed service, provides data scientists and developers with the ability to quickly build, train, deploy, and deploy machine-learning (ML) models. SageMaker takes the hard work out of each step in the machine learning process, making it easier to create high-quality models. Traditional ML development can be complex, costly, and iterative. This is made worse by the lack of integrated tools to support the entire machine learning workflow. It is tedious and error-prone to combine tools and workflows. SageMaker solves the problem by combining all components needed for machine learning into a single toolset. This allows models to be produced faster and with less effort. Amazon SageMaker Studio is a web-based visual interface that allows you to perform all ML development tasks. SageMaker Studio allows you to have complete control over each step and gives you visibility.

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.

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.

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.

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.

Unstructured data is the future for AI. This future is now possible. Build an ML/AI pipeline to scale all your unstructured data from one place. Graviti allows you to use better data to create better models. Learn about Graviti, the data platform that allows AI developers to manage, query and version control unstructured data. Quality data is no longer an expensive dream. All your metadata, annotations, and predictions can be managed in one place. You can customize filters and see the results of filtering to find the data that meets your needs. Use a Git-like system to manage data versions and collaborate. Role-based access control allows for safe and flexible team collaboration. Graviti's built in marketplace and workflow creator makes it easy to automate your data pipeline. No more grinding, you can quickly scale up to rapid model iterations.

Simplismart’s fastest inference engine allows you to fine-tune and deploy AI model with ease. Integrate with AWS/Azure/GCP, and many other cloud providers, for simple, scalable and cost-effective deployment. Import open-source models from popular online repositories, or deploy your custom model. Simplismart can host your model or you can use your own cloud resources. Simplismart allows you to go beyond AI model deployment. You can train, deploy and observe any ML models and achieve increased inference speed at lower costs. Import any dataset to fine-tune custom or open-source models quickly. Run multiple training experiments efficiently in parallel to speed up your workflow. Deploy any model to our endpoints, or your own VPC/premises and enjoy greater performance at lower cost. Now, streamlined and intuitive deployments are a reality. Monitor GPU utilization, and all of your node clusters on one dashboard. On the move, detect any resource constraints or model inefficiencies.

Google AI Studio is an online tool that's free and allows individuals and small groups to create apps and chatbots by using natural language prompting. It allows users to create API keys and prompts for app development. Google AI Studio allows users to discover Gemini Pro's APIs, create prompts and fine-tune Gemini. It also offers generous free quotas, allowing 60 requests a minute. Google has also developed a Generative AI Studio based on Vertex AI. It has models of various types that allow users to generate text, images, or audio content.

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.

With just one line of code, you can deploy all major ML frameworks like Pytorch and Onnx. Do you not have your own models? Prebuilt models can be deployed to reduce latency and cost. You can fine-tune models for specific tasks to reduce latency and costs while increasing performance. It's easy to do and you don't have to worry about infrastructure. Integrate with the top ML observability platform to be alerted on feature or prediction drift, compare models versions, and resolve issues quickly. To resolve model performance problems, discover the root causes of prediction and feature drift. Find out which features contribute the most to your model's performance.

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).

You can build, deploy and monitor AI-powered applications in just a few simple clicks. No coding or machine learning expertise is required. Stop puzzling together disjointed tools, featuring-engineering your way to production, and calling in favors to get results. With a platform that is designed to build, monitor and improve AI solutions throughout their entire lifecycle, managing all your AI initiatives will be a breeze. No more hyperparameter tuning and feature engineering. Graft guarantees that everything you build will work in production because the platform is production. Your AI solution should be tailored to your business. You retain control over the AI solution, from foundation models to pretraining and fine-tuning. Unlock the value in your unstructured data, such as text, images, videos, audios, and graphs. Control and customize solutions at scale.

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.

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.

Vishwa.ai, an AutoOps Platform for AI and ML Use Cases. It offers expert delivery, fine-tuning and monitoring of Large Language Models. Features: Expert Prompt Delivery : Tailored prompts tailored to various applications. Create LLM Apps without Coding: Create LLM workflows with our drag-and-drop UI. Advanced Fine-Tuning : Customization AI models. LLM Monitoring: Comprehensive monitoring of model performance. Integration and Security Cloud Integration: Supports Google Cloud (AWS, Azure), Azure, and Google Cloud. Secure LLM Integration - Safe connection with LLM providers Automated Observability for efficient LLM Management Managed Self Hosting: Dedicated hosting solutions. Access Control and Audits - Ensure secure and compliant operations.

SparkCognition, an industrial AI company, has created a natural language processing solution that automates the workflows of unstructured data within companies so that humans can concentrate on high-value business decisions. DeepNLP uses machine learning to automate the retrieval, classification, and analysis of information. DeepNLP integrates with existing workflows to allow organizations to respond more quickly to changes in their businesses and get quick answers to specific queries.

Our platform allows you to create AI products, train, fine-tune, and deploy models on the cloud. You don't have to worry about scaling, infrastructure, cost management, or other technical issues. Prebuilt, fully customizable modular components make it easy to train, fine tune, and deploy models. The science, not the engineering, should be your focus. Lightning components organize code to run on the cloud and manage its own infrastructure, cloud cost, and other details. 50+ optimizations to lower cloud cost and deliver AI in weeks, not months. Enterprise-grade control combined with consumer-level simplicity allows you to optimize performance, reduce costs, and take on less risk. Get more than a demo. In days, not months, you can launch your next GPT startup, diffusion startup or cloud SaaSML service.

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.

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.

The Xilinx AI development platform for AI Inference on Xilinx hardware platforms consists optimized IP, tools and libraries, models, examples, and models. It was designed to be efficient and easy-to-use, allowing AI acceleration on Xilinx FPGA or ACAP. Supports mainstream frameworks as well as the most recent models that can perform diverse deep learning tasks. A comprehensive collection of pre-optimized models is available for deployment on Xilinx devices. Find the closest model to your application and begin retraining! This powerful open-source quantizer supports model calibration, quantization, and fine tuning. The AI profiler allows you to analyze layers in order to identify bottlenecks. The AI library provides open-source high-level Python and C++ APIs that allow maximum portability from the edge to the cloud. You can customize the IP cores to meet your specific needs for many different applications.

Dynamiq was built for engineers and data scientist to build, deploy and test Large Language Models, and to monitor and fine tune them for any enterprise use case. Key Features: Workflows: Create GenAI workflows using a low-code interface for automating tasks at scale Knowledge & RAG - Create custom RAG knowledge bases in minutes and deploy vector DBs Agents Ops - Create custom LLM agents for complex tasks and connect them to internal APIs Observability: Logging all interactions and using large-scale LLM evaluations of quality Guardrails: Accurate and reliable LLM outputs, with pre-built validators and detection of sensitive content. Fine-tuning : Customize proprietary LLM models by fine-tuning them to your liking

Entry Point AI is a modern AI optimization platform that optimizes proprietary and open-source language models. Manage prompts and fine-tunes in one place. We make it easy to fine-tune models when you reach the limits. Fine-tuning involves showing a model what to do, not telling it. It works in conjunction with prompt engineering and retrieval augmented generation (RAG) in order to maximize the potential of AI models. Fine-tuning your prompts can help you improve their quality. Imagine it as an upgrade to a few-shot model that incorporates the examples. You can train a model to perform at the same level as a high-quality model for simpler tasks. This will reduce latency and costs. For safety, to protect the brand, or to get the formatting correct, train your model to not respond in a certain way to users. Add examples to your dataset to cover edge cases and guide model behavior.

Our open-source research efforts are focused on both the new "ware" (middleware and "software" which will wrap and integrate LLMs) as well as building high quality, automation-focused enterprise model available in Hugging Face. LLMWare is also a coherent, high quality, integrated and organized framework for developing LLM-applications in an open system. This provides the foundation for creating LLM-applications that are designed for AI Agent workflows and Retrieval Augmented Generation. Our LLM framework was built from the ground-up to handle complex enterprise use cases. We can provide pre-built LLMs tailored to your industry, or we can fine-tune and customize an LLM for specific domains and use cases. We provide an end-toend solution, from a robust AI framework to specialized models.

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.

We are ready to meet all your business needs, whether it is quick engineering, fine-tuning or training. The Together Inference API makes it easy to integrate your new model in your production application. Together AI's elastic scaling and fastest performance allows it to grow with you. To increase accuracy and reduce risks, you can examine how models are created and what data was used. You are the owner of the model that you fine-tune and not your cloud provider. Change providers for any reason, even if the price changes. Store data locally or on our secure cloud to maintain complete data privacy.

Capture production data. Evaluate outputs together and fine-tune the performance of your LLM. A detailed log overview will help you understand what is happening in production. Work with domain experts, product managers and engineers to create reliable model outputs. Track AI metrics, such as speed, token usage, and quality scores. Copilot automates model evaluations and improvements for your use cases. Create, manage, or optimize prompts for precise and relevant interactions between AI models and users. Compare fine-tuned models and foundation models to improve prompt performance. Build a fine-tuning dataset with your team. Create custom fine-tuning data to optimize model performance.

Optimizing continuous pre-training to enrich models with proprietary data. Assuring domain-specific models provide a smooth user experience. Create 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. Our product's domain adaptability allows you to train and deploy SLMs for a variety of use cases. Arcee's VPC service allows you to train and deploy your SLMs while ensuring that what belongs to you, stays yours.

AI agents that interact and answer questions with users and complete tasks using your data and APIs. AI that can answer questions, summarize and extract insights from any long document. Transfer styles and formats, as well as tags and summaries between documents and data sources. Stack AI is used by developer teams to automate customer service, process documents, qualify leads, and search libraries of data. With a single button, you can try multiple LLM architectures and prompts. Collect data, run fine-tuning tasks and build the optimal LLM to fit your product. We host your workflows in APIs, so that your users have access to AI instantly. Compare the fine-tuning services of different LLM providers.

Platform for AI agents testing and debugging by the industry's leading developers. We developed the tools, so you don't need to. Visually track events, such as LLM, tools, and agent interactions. Rewind and playback agent runs with pinpoint precision. Keep a complete data trail from prototype to production of logs, errors and prompt injection attacks. Native integrations with top agent frameworks. Track, save and monitor each token that your agent sees. Monitor and manage agent spending using the most recent price monitoring. Save up to 25x on specialized LLMs by fine-tuning them based on completed completions. Build your next agent using evals and replays. You can visualize the behavior of your agents in your AgentOps dashboard with just two lines of coding. After you set up AgentOps each execution of your program will be recorded as a "session" and the data will automatically be recorded for you.

Choose a model from our open-source collection, run it and access the model API within your product. No matter what model you are using for Image Recognition or Language Processing, all models come pre-trained and packaged with an easy-to use API. There are many models to choose from, and the list is growing. We curate and fine-tune only the best models from HuggingFace or Github. You can either host the model yourself or get your API-Key and endpoint with just one click. Cargoship keeps up with the advancement of AI so you don’t have to. The Cargoship Model Store has a collection that can be used for any ML use case. You can test them in demos and receive detailed guidance on how to implement the model. No matter your level of expertise, our team will pick you up and provide you with detailed instructions.

FinetuneFast allows you to fine-tune AI models, deploy them quickly and start making money online. Here are some of the features that make FinetuneFast unique: - Fine tune your ML models within days, not weeks - The ultimate ML boilerplate, including text-to-images, LLMs and more - Build your AI app to start earning online quickly - Pre-configured scripts for efficient training of models - Efficient data load pipelines for streamlined processing Hyperparameter optimization tools to improve model performance - Multi-GPU Support out of the Box for enhanced processing power - No-Code AI Model fine-tuning for simple customization - Model deployment with one-click for quick and hassle free deployment - Auto-scaling Infrastructure for seamless scaling of your models as they grow - API endpoint creation for easy integration with other system - Monitoring and logging for real-time performance monitoring

Haystack’s pipeline architecture allows you to apply the latest NLP technologies to your data. Implement production-ready semantic searching, question answering and document ranking. Evaluate components and fine tune models. Haystack's pipelines allow you to ask questions in natural language, and find answers in your documents with the latest QA models. Perform semantic search to retrieve documents ranked according to meaning and not just keywords. Use and compare the most recent transformer-based language models, such as OpenAI's GPT-3 and BERT, RoBERTa and DPR. Build applications for semantic search and question answering that can scale up to millions of documents. Building blocks for the complete product development cycle, including file converters, indexing, models, labeling, domain adaptation modules and REST API.

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.

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.

DataChain connects your unstructured cloud files with AI models, APIs and foundational models to enable instant data insights. Its Pythonic stack accelerates the development by tenfold when switching to Python-based data wrangling, without SQL data islands. DataChain provides dataset versioning to ensure full reproducibility and traceability for each dataset. This helps streamline team collaboration while ensuring data integrity. It allows you analyze your data wherever it is stored, storing raw data (S3, GCP or Azure) and metadata in inefficient datawarehouses. DataChain provides tools and integrations which are cloud-agnostic in terms of both storage and computing. DataChain allows you to query your multi-modal unstructured data. You can also apply intelligent AI filters for training data and snapshot your unstructured dataset, the code used for data selection and any stored or computed meta data.

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.

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.

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.

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.

Trifacta's Cloud Dataprep is an intelligent data service that visually explores, cleans, and prepares structured and unstructured data to be used for analysis, reporting, or machine learning. Cloud Dataprep works on any scale and is serverless, so there is no infrastructure to install or manage. Cloud Dataprep will suggest and predict your next data transformation with every UI input. This eliminates the need to write code. Cloud Dataprep, a Trifacta-operated integrated partner service, is based on their industry-leading data prep solution. Trifacta and Google work together to create a seamless user experience. This eliminates the need to install software, pay separate licensing fees, or incur ongoing overhead. Cloud Dataprep is fully managed, scales according to your data preparation requirements so you can focus on analysis.

Train, fine-tune and generate text and image AI based on your data. We use the best open-source models for image and text generation, and can train them within minutes using LoRA fine tuning. Click the share button to generate a link or bot to your session. You can deploy your own private infrastructure. Create a free Stable Diffusion XL account to start chatting and generating images using open source language models. Drag'n'drop is the easiest way to fine-tune your model using your own text or images. It takes between 3-10 minutes. You can chat with the models and create images using a familiar chat interface.

Amazon Bedrock is a managed AWS service designed to make building and scaling generative AI applications easier by providing access to a diverse range of foundation models (FMs) from leading providers such as AI21 Labs, Anthropic, Cohere, Meta, Mistral AI, Stability AI, and Amazon itself. Through a single API, developers can test, fine-tune, and customize these models to meet specific use cases using advanced techniques like Retrieval Augmented Generation (RAG). The platform allows for the creation of intelligent agents that seamlessly integrate with enterprise systems and data sources, enabling enhanced automation and decision-making. Bedrock’s serverless architecture removes the need for infrastructure management, ensuring high scalability and minimal operational complexity. With a focus on security, data privacy, and responsible AI, Amazon Bedrock empowers organizations to accelerate innovation while maintaining trust and compliance. It represents a powerful tool for businesses aiming to integrate cutting-edge AI solutions into their operations effortlessly.

Tune Studio is a versatile and intuitive platform that allows users to fine-tune AI models with minimum effort. It allows users to customize machine learning models that have been pre-trained to meet their specific needs, without needing to be a technical expert. Tune Studio's user-friendly interface simplifies the process for uploading datasets and configuring parameters. It also makes it easier to deploy fine-tuned machine learning models. Tune Studio is ideal for beginners and advanced AI users alike, whether you're working with NLP, computer vision or other AI applications. It offers robust tools that optimize performance, reduce the training time and accelerate AI development.

With our enterprise Gen AI stack you can go beyond your imagination. You can instantly offload manual tasks and give them to powerful assistants. The sky is the limit. For enterprises that place data security first, fine-tune generative AI models and deploy them on your own cloud securely.

Fine-tuning is when you take a dataset, and create a model to use AI. This is not always possible without programming so we created a solution in RIku that handles everything in a very easy format. Fine-tuning unlocks an entirely new level of power for artificial intelligence and we are excited to help you explore this. Public Share Links are landing pages you can create for any of the prompts. These can be designed with your brand in mind, including colors and adding your logo. These links can be shared with anyone, and if they have access to the password to unlock it they will be able make generations. No-code assistant builder for your audience. We found that projects using multiple large languages models have a lot of problems. They all return their outputs in a slightly different way.

Test, launch and refine Gen AI prompting. RAG Agents. Datasets. Workflows. A single workspace for Engineers and Product Managers to explore LLM technology.

A simple web API allows you to fine-tune your LLMs and receive completions. No infrastructure is required. Instantly create private AI applications that comply with SOC2-standards. Our developer platform makes it easy to customize models for your specific use case. Select the base model and define the data that you want to teach. We will take care of everything else. With a single API, you can integrate private LLMs with your applications. No more deployment, orchestration or infrastructure headaches. The most powerful OSS available -- highly generalized capabilities with amazing storytelling and reasoning capabilities. Use a fully unlocked LLM for the best internal automation systems in your company.

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.

Langtail is a cloud-based development tool designed to streamline the debugging, testing, deployment, and monitoring of LLM-powered applications. The platform provides a no-code interface for debugging prompts, adjusting model parameters, and conducting thorough LLM tests to prevent unexpected behavior when prompts or models are updated. Langtail is tailored for LLM testing, including chatbot evaluations and ensuring reliable AI test prompts. Key features of Langtail allow teams to: • Perform in-depth testing of LLM models to identify and resolve issues before production deployment. • Easily deploy prompts as API endpoints for smooth integration into workflows. • Track model performance in real-time to maintain consistent results in production environments. • Implement advanced AI firewall functionality to control and protect AI interactions. Langtail is the go-to solution for teams aiming to maintain the quality, reliability, and security of their AI and LLM-based applications.