Braintrust Alternatives

Langfuse is an open source LLM engineering platform to help teams collaboratively debug, analyze and iterate on their LLM Applications. Observability: Instrument your app and start ingesting traces to Langfuse Langfuse UI: Inspect and debug complex logs and user sessions Prompts: Manage, version and deploy prompts from within Langfuse Analytics: Track metrics (LLM cost, latency, quality) and gain insights from dashboards & data exports Evals: Collect and calculate scores for your LLM completions Experiments: Track and test app behavior before deploying a new version Why Langfuse? - Open source - Model and framework agnostic - Built for production - Incrementally adoptable - start with a single LLM call or integration, then expand to full tracing of complex chains/agents - Use GET API to build downstream use cases and export data

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.

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.

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.

Get the full picture of your model's performance. Log inputs and outputs and seamlessly enrich them with metadata and user feedback. Figure out how your model is really working, and where you can improve. Monitor for errors and discover underperforming cohorts and use cases. The best models are built on user data. Programmatically gather unusual or underperforming examples to retrain your model. Stop manually reviewing thousands of outputs when changing your prompt or model. Evaluate your LLM-powered apps programmatically. Detect and fix degradations quickly. Monitor new deployments in real-time and seamlessly edit the version of your app your users interact with. Connect your self-hosted or third-party model and your existing data sources. Process enterprise-scale data with our serverless streaming dataflow engine. Gantry is SOC-2 compliant and built with enterprise-grade authentication.

Monitor your LLMs in production, and discover and fix hallucinations, accuracy, and quality-related errors with LLM outputs seamlessly. Evaluate your outputs for hallucinations, misinformation, quality issues, and other bad outputs. Configurable for any LLM use case. Segment your data to analyze your cost, accuracy, response times, model usage, and feedback in depth. Search, sort, and filter through your inference calls, and trace through your queries, retrievals, prompts, responses, and feedback metrics to debug generations. Explore your conversations, understand what your users are talking about and how they feel, and learn which conversations ended badly. Compare your performance metrics across different models and prompts. Our insights will help you find the best-performing model for every use case. Our evaluators use your data, configurations, and feedback to get better and analyze the outputs better.

GradientJ provides everything you need to build large language model applications in minutes and manage them forever. Discover and maintain the best prompts by saving versions and comparing them across benchmark examples. Orchestrate and manage complex applications by chaining prompts and knowledge bases into complex APIs. Enhance the accuracy of your models by integrating them with your proprietary data.

Get scores for factual accuracy, context retrieval quality, guideline adherence, tonality, and many more. You can’t improve what you can’t measure. UpTrain continuously monitors your application's performance on multiple evaluation criterions and alerts you in case of any regressions with automatic root cause analysis. UpTrain enables fast and robust experimentation across multiple prompts, model providers, and custom configurations, by calculating quantitative scores for direct comparison and optimal prompt selection. Hallucinations have plagued LLMs since their inception. By quantifying degree of hallucination and quality of retrieved context, UpTrain helps to detect responses with low factual accuracy and prevent them before serving to the end-users.

Use Prompt Mixer to create prompts and chains. Combinе your chains with datasets and improve with AI. Develop a comprehensive set of test scenarios to assess various prompt and model pairings, determining the optimal combination for diverse use cases. Incorporate Prompt Mixer into your everyday tasks, from creating content to conducting R&D. Prompt Mixer can streamline your workflow and boost productivity. Use Prompt Mixer to efficiently create, assess, and deploy content generation models for various applications such as blog posts and emails. Use Prompt Mixer to extract or merge data in a completely secure manner and easily monitor it after deployment.

The prompt engineering platform to experiment with different prompt versions, evaluate and compare prompts across a suite of tests, optimize prompts with one-click, share, and more. Optimize your AI development workflow. Key features to help you get and identify the best prompts for your production use cases. Side-by-side comparison of prompts across test cases with evaluation. CSV import test cases, and define custom evaluation metrics. Improve LLM results with automatic prompt and template optimization. View and manage all prompt versions and create OpenAI functions. Access all of your prompts programmatically, including observability and analytics. Determine the costs, latency, and efficacy of each prompt. Start enhancing your prompt engineering workflow with Parea today. Parea makes it easy for developers to improve the performance of their LLM apps through rigorous testing and version control.

Guardrails are crucial in AI maintenance, LangWatch safeguards you and your business from exposing sensitive data, prompt injection and keeps your AI from going off the rails, avoiding unforeseen damage to your brand. Understanding the behaviour of both AI and users can be challenging for businesses with integrated AI. Ensure accurate and appropriate responses by constantly maintaining quality through oversight. LangWatch’s safety checks and guardrails prevent common AI issues including jailbreaking, exposing sensitive data, and off-topic conversations. Track conversion rates, output quality, user feedback and knowledge base gaps with real-time metrics — gain constant insights for continuous improvement. Powerful data evaluation allows you to evaluate new models and prompts, develop datasets for testing and run experimental simulations on tailored builds.

Build, evaluate, deploy, and refine custom natural language processing models. Empower your team to automate workflows without hiring an AI expert team and costly infra. Metatext simplifies the process of creating customized AI/NLP models, even without expertise in ML, data science, or MLOps. With just a few steps, automate complex workflows, and rely on intuitive UI and APIs to handle the heavy work. Enable AI into your team using a simple but intuitive UI, add your domain expertise, and let our APIs do all the heavy work. Get your custom AI trained and deployed automatically. Get the best from a set of deep learning algorithms. Test it using a Playground. Integrate our APIs with your existing systems, Google Spreadsheets, and other tools. Select the AI engine that best suits your use case. Each one offers a set of tools to assist creating datasets and fine-tuning models. Upload text data in various file formats and annotate labels using our built-in AI-assisted data labeling tool.

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.

Unexpected results happen all the time. With full visibility into the entire chain sequence of calls, you can spot the source of errors and surprises in real time with surgical precision. Software engineering relies on unit testing to build performant, production-ready applications. LangSmith provides that same functionality for LLM applications. Spin up test datasets, run your applications over them, and inspect results without having to leave LangSmith. LangSmith enables mission-critical observability with only a few lines of code. LangSmith is designed to help developers harness the power–and wrangle the complexity–of LLMs. We’re not only building tools. We’re establishing best practices you can rely on. Build and deploy LLM applications with confidence. Application-level usage stats. Feedback collection. Filter traces, cost and performance measurement. Dataset curation, compare chain performance, AI-assisted evaluation, and embrace best practices.

Onboard your data and models to Openlayer and collaborate with the whole team to align expectations surrounding quality and performance. Breeze through the whys behind failed goals to solve them efficiently. The information to diagnose the root cause of issues is at your fingertips. Generate more data that looks like the subpopulation and retrain the model. Test new commits against your goals to ensure systematic progress without regressions. Compare versions side-by-side to make informed decisions and ship with confidence. Save engineering time by rapidly figuring out exactly what’s driving model performance. Find the most direct paths to improving your model. Know the exact data needed to boost model performance and focus on cultivating high-quality and representative datasets.

Prototype and productionize generative AI apps, built for engineers, not just ML practitioners. No more switching between platforms or wrestling with different APIs, focus on creating, not configuring. Use a familiar interface to prompt engineer and work with AI. Use parameters to easily streamline your workbooks into reusable templates. Create workflows by chaining model outputs from LLMs, image, and audio models. Create organizations to manage workbooks amongst your teammates. Share your workbook to the public or specific organizations you define with your team. Comment on workbooks and easily review and compare workbooks with your team. Develop templates for yourself, your team, or the broader developer community, get started quickly with templates to see what people are building.

Apply advanced coding and language models to a variety of use cases. Leverage large-scale, generative AI models with deep understandings of language and code to enable new reasoning and comprehension capabilities for building cutting-edge applications. Apply these coding and language models to a variety of use cases, such as writing assistance, code generation, and reasoning over data. Detect and mitigate harmful use with built-in responsible AI and access enterprise-grade Azure security. Gain access to generative models that have been pretrained with trillions of words. Apply them to new scenarios including language, code, reasoning, inferencing, and comprehension. Customize generative models with labeled data for your specific scenario using a simple REST API. Fine-tune your model's hyperparameters to increase accuracy of outputs. Use the few-shot learning capability to provide the API with examples and achieve more relevant results.

Confident AI offers an open-source package called DeepEval that enables engineers to evaluate or "unit test" their LLM applications' outputs. Confident AI is our commercial offering and it allows you to log and share evaluation results within your org, centralize your datasets used for evaluation, debug unsatisfactory evaluation results, and run evaluations in production throughout the lifetime of your LLM application. We offer 10+ default metrics for engineers to plug and use.

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.

Discuro is the all-in-one platform for developers looking to easily build, test & consume complex AI workflows. Define your workflow in our easy-to-use UI, and when you're ready to execute, simply make one API call to us, with your inputs, any meta-data, and we'll do the rest. Use an Orchestrator to feed generated data back into GPT-3. Reliably integrate with OpenAI and extract the data you need with ease. Create & consume your own flows in minutes. We've built everything you need to integrate with OpenAI, at scale, so you can focus on the product. The first challenge in integrating with OpenAI is extracting the data you need, we'll handle this for you by collecting input/output definitions. Easily chain completions together to build large data sets. Use our iterative input feature to feed GPT-3 output back in, and have us make consecutive calls to expand your data set, and much more. Easily build & test complex self-transforming AI workflows & datasets.

Bring LLM-powered features to production with tools for prompt engineering, semantic search, version control, quantitative testing, and performance monitoring. Compatible across all major LLM providers. Quickly develop an MVP by experimenting with different prompts, parameters, and even LLM providers to quickly arrive at the best configuration for your use case. Vellum acts as a low-latency, highly reliable proxy to LLM providers, allowing you to make version-controlled changes to your prompts – no code changes needed. Vellum collects model inputs, outputs, and user feedback. This data is used to build up valuable testing datasets that can be used to validate future changes before they go live. Dynamically include company-specific context in your prompts without managing your own semantic search infra.

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.

Try different prompts and find what works best. Track the latency and cost of each prompt. Set up your prompts with dynamic variables. Call them via API and insert variables into the prompt. Combine the power of different LLMs within your application. Track the latency and cost of requests to optimize what works best. Test different prompts and save your favorite ones.

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.

Unlock the world of AI and Machine Learning right on your phone with FieldDay. We’ve taken the complexity out of creating machine learning models and turned it into an engaging, hands-on experience that’s as simple as using your camera. FieldDay allows you to create custom AI apps and embed them in your favourite tools, using just your phone. Feed FieldDay examples to learn from, and generate a custom model ready to be embedded in your app/project. A range of projects and apps driven by custom FieldDay machine learning models. Our range of integrations and export options simplifies the process of embedding a machine-learning model into the platform you prefer. With FieldDay, you can collect data directly from your phone’s camera. Our bespoke interface is designed for easy and intuitive annotation during collection, so you can build a custom dataset in no time. FieldDay lets you preview and correct your models in real-time.

The entire process of building machine learning algorithms and predicting outcomes, packed in one single click. Not all data is built to be ready for ML, use the Data Dialog to seamlessly shape your dataset without wrangling your files. Share your prediction reports with your team or make them public. Allow anyone to start making predictions on your model. Bring dynamic ML predictions into your own app using our low-code API. Predict willingness to pay, score leads and much more in real-time. Obviously AI puts the world’s most cutting-edge algorithms in your hands, without compromising on performance. Forecast revenue, optimize supply chain, personalize marketing. You can now know what happens next. Add a CSV file OR integrate with your favorite data sources in minutes. Pick your prediction column from a dropdown, we'll auto build the AI. Beautifully visualize predicted results, top drivers and simulate "what-if" scenarios.

Explore the power of choosing the right LLM for your needs and how to optimize for quality, speed, and cost-efficiency. Access all LLMs across all providers with a single API key and a standard API. Setup your own cost, latency, and output speed constraints. Define a custom quality metric. Personalize your router for your requirements. Systematically send your queries to the fastest provider, based on the very latest benchmark data for your region of the world, refreshed every 10 minutes. Get started with Unify with our dedicated walkthrough. Discover the features you already have access to and our upcoming roadmap. Just create a Unify account to access all models from all supported providers with a single API key. Our router balances output quality, speed, and cost based on user-specific preferences. The quality is predicted ahead of time using a neural scoring function, which predicts how good each model would be at responding to a given prompt.

your team can develop AI applications based on models such as GPT-4 and operate them visually. Whether for internal team use or external release, you can deploy your application in as fast as 5 minutes. Using documents/webpages/Notion content as the context for AI, automatically complete text preprocessing, vectorization and segmentation. You don't have to learn embedding techniques anymore, saving you weeks of development time. Dify provides a smooth experience for model access, context embedding, cost control and data annotation. Whether for internal team use or product development, you can easily create AI applications. Starting from a prompt, but transcending the limitations of the prompt. Dify provides rich functionality for many scenarios, all through graphical user interface operations.

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.

You get dozens of daily e-mails, but only some need your immediate attention, so the e-mail classifier below helps you maintain an organized inbox. For customer complaints, we summarize the main issue and notify #customer-support on Slack. Delayed orders go into #customer-relation. After a customer call with your support agent, you want to stay informed on what happened. Instead of listening to the whole call, create a Waveline flow that summarizes the main points. Many people experience writer's block when writing text. Quickly build an internal tool with Waveline that automatically gathers information about the recipient from LinkedIn and a Google search to generate a highly personalized first draft. Parse unstructured data and repackaged it into a structured format. Waveline uses LLMs to extract information from text, images, and more.

Build better AI with multiple models. AI models are a new kind of building block. Sieve is the easiest way to use these building blocks to understand audio, generate video, and much more at scale. State-of-the-art models in just a few lines of code, and a curated set of production-ready apps for many use cases. Import your favorite models like Python packages. Visualize results with auto-generated interfaces built for your entire team. Deploy custom code with ease. Define your environment compute in code, and deploy with a single command. Fast, scalable infrastructure without the hassle. We built Sieve to automatically scale as your traffic increases with zero extra configuration. Package models with a simple Python decorator and deploy them instantly. A full-featured observability stack so you have full visibility of what’s happening under the hood. Pay only for what you use, by the second. Gain full control over your costs.

Improve the accuracy of your machine learning models with publicly available datasets. Save time on data discovery and preparation by using curated datasets that are ready to use in machine learning workflows and easy to access from Azure services. Account for real-world factors that can impact business outcomes. By incorporating features from curated datasets into your machine learning models, improve the accuracy of predictions and reduce data preparation time. Share datasets with a growing community of data scientists and developers. Deliver insights at hyperscale using Azure Open Datasets with Azure’s machine learning and data analytics solutions. There's no additional charge for using most Open Datasets. Pay only for Azure services consumed while using Open Datasets, such as virtual machine instances, storage, networking resources, and machine learning. Curated open data made easily accessible on Azure.

MakerSuite is a tool that simplifies this workflow. With MakerSuite, you’ll be able to iterate on prompts, augment your dataset with synthetic data, and easily tune custom models. When you’re ready to move to code, MakerSuite will let you export your prompt as code in your favorite languages and frameworks, like Python and Node.js.

Lilac is an open source tool that enables data and AI practitioners to improve their products by improving their data. Understand your data with powerful search and filtering. Collaborate with your team on a single, centralized dataset. Apply best practices for data curation, like removing duplicates and PII to reduce dataset size and lower training cost and time. See how your pipeline impacts your data using our diff viewer. Clustering is a technique that automatically assigns categories to each document by analyzing the text content and putting similar documents in the same category. This reveals the overarching structure of your dataset. Lilac uses state-of-the-art algorithms and LLMs to cluster the dataset and assign informative, descriptive titles. Before we do advanced searching, like concept or semantic search, we can immediately use keyword search by typing a keyword in the search box.

Freeplay gives product teams the power to prototype faster, test with confidence, and optimize features for customers, take control of how you build with LLMs. A better way to build with LLMs. Bridge the gap between domain experts & developers. Prompt engineering, testing & evaluation tools for your whole team.

Intuitively build your RAG system with fewer abstractions compared to solutions like LangChain. Choose from a wide range of hosted and remote providers for vector databases, datasets, Large Language Models (LLMs), application integrations, and more. Use SciPhi to version control your system with Git and deploy from anywhere. The platform provided by SciPhi is used internally to manage and deploy a semantic search engine with over 1 billion embedded passages. The team at SciPhi will assist in embedding and indexing your initial dataset in a vector database. The vector database is then integrated into your SciPhi workspace, along with your selected LLM provider.

Write a prompt, train it on your own knowledge, content, docs and data. Bring together multiple app with a cohesive interface that reflects your brand's style - all accessible via one link. Collect emails, charge users, and upsell to other services and products. Transform interactions with Chipp's custom chat interfaces, trained on your unique datasets, documents, and files. Whether it's customer service or interactive storytelling, our chatbots provide relevant, context-aware dialogues for an engaging user experience that reflects your brand's voice.

We offer a unified set of world class tools and assets for seamless conversion of ideas into production-ready AI solutions. Build and deploy end-to-end custom Generative AI solutions on our unified platform, skipping the hassle of tool fragmentation and platform-switching. Launch your next AI solution through a single API endpoint. Creating, maintaining, and improving AI systems has never been this easy. Discover is aiXplain’s marketplace for models and datasets from various suppliers. Subscribe to models and datasets to use them with aiXplain no-code/low-code tools or through the SDK in your own code.

The open-source ML observability platform. Evaluate, test, and monitor ML models from validation to production. From tabular data to NLP and LLM. Built for data scientists and ML engineers. All you need to reliably run ML systems in production. Start with simple ad hoc checks. Scale to the complete monitoring platform. All within one tool, with consistent API and metrics. Useful, beautiful, and shareable. Get a comprehensive view of data and ML model quality to explore and debug. Takes a minute to start. Test before you ship, validate in production and run checks at every model update. Skip the manual setup by generating test conditions from a reference dataset. Monitor every aspect of your data, models, and test results. Proactively catch and resolve production model issues, ensure optimal performance, and continuously improve it.

Experience JinaChat, a pioneering LLM service tailored for pro users. JinaChat ushers in a new era of multimodal chat capabilities, extending beyond text to incorporate images and more. Delight in our offer of free short interactions under 100 tokens. Our API empowers developers to leverage long conversation histories and eliminate redundant prompts to build complex applications. Dive headfirst into the future of LLM services with JinaChat, where conversations are multimodal, long-memory, and affordable. Modern LLM applications often hinge on lengthy prompts or extensive memory, leading to high costs when similar prompts are repeatedly sent to the server with only minor changes. JinaChat's API solves this problem by letting you carry forward previous conversations without resending the entire prompt. This saves you both time and money, making it the perfect tool for developing complex applications like AutoGPT.

AI today is blocked by lack of labeled data, not models. Unblock AI with the first data-centric AI development platform powered by a programmatic approach. Snorkel AI is leading the shift from model-centric to data-centric AI development with its unique programmatic approach. Save time and costs by replacing manual labeling with rapid, programmatic labeling. Adapt to changing data or business goals by quickly changing code, not manually re-labeling entire datasets. Develop and deploy high-quality AI models via rapid, guided iteration on the part that matters–the training data. Version and audit data like code, leading to more responsive and ethical deployments. Incorporate subject matter experts' knowledge by collaborating around a common interface, the data needed to train models. Reduce risk and meet compliance by labeling programmatically and keeping data in-house, not shipping to external annotators.

NVIDIA Base Command™ Platform is a software service for enterprise-class AI training that enables businesses and their data scientists to accelerate AI development. Part of the NVIDIA DGX™ platform, Base Command Platform provides centralized, hybrid control of AI training projects. It works with NVIDIA DGX Cloud and NVIDIA DGX SuperPOD. Base Command Platform, in combination with NVIDIA-accelerated AI infrastructure, provides a cloud-hosted solution for AI development, so users can avoid the overhead and pitfalls of deploying and running a do-it-yourself platform. Base Command Platform efficiently configures and manages AI workloads, delivers integrated dataset management, and executes them on right-sized resources ranging from a single GPU to large-scale, multi-node clusters in the cloud or on-premises. Because NVIDIA’s own engineers and researchers rely on it every day, the platform receives continuous software enhancements.

We’ve included some common examples, but the list is far from exhaustive. Our solution engineering team will work with you to customize Kolena for your workflows and your business metrics. Aggregate metrics don't tell the full story — unexpected model behavior in production is the norm. Current testing processes are manual, error-prone, and unrepeatable. Models are evaluated on arbitrary statistical metrics that align imperfectly with product objectives. ‍ Tracking model improvement over time as the data evolves is difficult and techniques sufficient in a research environment don't meet the demands of production.

Today's organizations grapple with increasingly complex problems, involving vast and diverse datasets. Solving these challenges demands an innovative approach. Filuta AI uses Composite Artificial Intelligence to present powerful solutions. We're democratizing AI - improving user access to effective automation, while reducing reliance on costly, non-transparent solutions. Our platform makes building & operating intelligent automation accessible to non-developers. Filuta AI's self-supervised learning and automatic symbolic domain synthesis allow you to deploy at ten times the speed of traditional AI. Our platform was designed with a novel approach - to augment human experts by breaking complex, real-world problems into actionable hierarchies with Composite AI.

Whether you're building AI chatbots, virtual assistants, or other intelligent applications, set up your workspace effortlessly by integrating GPT-powered language models with your provider keys for unparalleled performance. Accelerate the creation of your diverse AI applications using LLM Spark's GPT-driven templates or craft unique projects from the ground up. Test & compare multiple models simultaneously for optimal performance across multiple scenarios. Save prompt versions and history effortlessly while streamlining development. Invite members to your workspace and collaborate on projects with ease. Semantic search for powerful search capabilities to find documents based on meaning, not just keywords. Deploy trained prompts effortlessly, making AI applications accessible across platforms.

Wordware enables anyone to develop, iterate, and deploy useful AI agents. Wordware combines the best aspects of software with the power of natural language. Remove constraints of traditional no-code tools and empower every team member to iterate independently. Natural language programming is here to stay. Wordware frees prompt from your codebase by providing both technical and non-technical users with a powerful IDE for AI agent creation. Experience the simplicity and flexibility of our interface. Empower your team to easily collaborate, manage prompts, and streamline workflows with an intuitive design. Loops, branching, structured generation, version control, and type safety help you get the most out of LLMs, while custom code execution allows you to connect to virtually any API. Easily switch between various large language model providers with one click. Optimize your workflows with the best cost-to-latency-to-quality ratios for your application.

Open Agent Studio is not just another co-pilot it's a no-code co-pilot builder that enables solutions that are impossible in all other RPA tools today. We believe these other tools will copy this idea, so our customers have a head start over the next few months to target markets previously untouched by AI with their deep industry insight. Subscribers have access to a free 4-week course, which teaches how to evaluate product ideas and launch a custom agent with an enterprise-grade white label. Easily build agents by simply recording your keyboard and mouse actions, including scraping data and detecting the start node. The agent recorder makes it as easy as possible to build generalized agents as quickly as you can teach how to do it. Record once, then share across your organization to scale up future-proof agents.

No-code, in minutes—inject AI prompts into your own website, your data, your workflow. We support the latest generative models and are always adding more. Use GPT4, ChatGPT, GPT3, DALL-E 2, Stable Diffusion, and more! Train AI to use your PDF, website, or document as context for its responses. Customize Pickaxes and embed them on your website, bring them into Google sheets, or access through our API

A new way to automatically train, evaluate and deploy state-of-the-art Machine Learning models. AutoTrain is an automatic way to train and deploy state-of-the-art Machine Learning models, seamlessly integrated with the Hugging Face ecosystem. Your training data stays on our server, and is private to your account. All data transfers are protected with encryption. Available today: text classification, text scoring, entity recognition, summarization, question answering, translation and tabular. CSV, TSV or JSON files, hosted anywhere. We delete your training data after training is done. Hugging Face also hosts an AI content detection tool.

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