Find and compare the best Artificial Intelligence software for Hugging Face in 2025
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Use the comparison tool below to compare the top Artificial Intelligence software for Hugging Face on the market. You can filter results by user reviews, pricing, features, platform, region, support options, integrations, and more.
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Langfuse is a free and open-source LLM engineering platform that helps teams to debug, analyze, and iterate their LLM Applications. Observability: Incorporate Langfuse into your app to start ingesting traces. Langfuse UI : inspect and debug complex logs, user sessions and user sessions Langfuse Prompts: Manage versions, deploy prompts and manage prompts within Langfuse Analytics: Track metrics such as cost, latency and quality (LLM) to gain insights through dashboards & data exports Evals: Calculate and collect scores for your LLM completions Experiments: Track app behavior and test it before deploying new versions Why Langfuse? - Open source - Models and frameworks are agnostic - Built for production - Incrementally adaptable - Start with a single LLM or integration call, then expand to the full tracing for complex chains/agents - Use GET to create downstream use cases and export the data
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Bolt.diy, an AI-powered agent for web development, allows you to run, edit and deploy full-stack applications directly from the browser. No local setup is required. It integrates cutting edge AI models with a in-browser environment powered by StackBlitz’s WebContainers. This allows you to install and use npm libraries and tools, run Node.js server, interact with third party APIs, and even deploy to production directly from chat. Bolt.diy, unlike traditional development environments, where AI is limited to code generation, gives AI models full control over the environment. This includes the filesystem, the node server, the package manager, the terminal, and the browser console. AI agents can now handle the entire lifecycle of an app, from creation to release. Bolt.diy lets you build full-stack production applications, whether you're a developer, PM, or designer.
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Datasaur
Datasaur
$349/month One tool can manage your entire data labeling workflow. We invite you to discover the best way to manage your labeling staff, improve data quality, work 70% faster, and get organized! -
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Zilliz Cloud
Zilliz
$0Searching and analyzing structured data is easy; however, over 80% of generated data is unstructured, requiring a different approach. Machine learning converts unstructured data into high-dimensional vectors of numerical values, which makes it possible to find patterns or relationships within that data type. Unfortunately, traditional databases were never meant to store vectors or embeddings and can not meet unstructured data's scalability and performance requirements. Zilliz Cloud is a cloud-native vector database that stores, indexes, and searches for billions of embedding vectors to power enterprise-grade similarity search, recommender systems, anomaly detection, and more. Zilliz Cloud, built on the popular open-source vector database Milvus, allows for easy integration with vectorizers from OpenAI, Cohere, HuggingFace, and other popular models. Purpose-built to solve the challenge of managing billions of embeddings, Zilliz Cloud makes it easy to build applications for scale. -
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Union Cloud
Union.ai
Free (Flyte)Union.ai Benefits: - Accelerated Data Processing & ML: Union.ai significantly speeds up data processing and machine learning. - Built on Trusted Open-Source: Leverages the robust open-source project Flyte™, ensuring a reliable and tested foundation for your ML projects. - Kubernetes Efficiency: Harnesses the power and efficiency of Kubernetes along with enhanced observability and enterprise features. - Optimized Infrastructure: Facilitates easier collaboration among Data and ML teams on optimized infrastructures, boosting project velocity. - Breaks Down Silos: Tackles the challenges of distributed tooling and infrastructure by simplifying work-sharing across teams and environments with reusable tasks, versioned workflows, and an extensible plugin system. - Seamless Multi-Cloud Operations: Navigate the complexities of on-prem, hybrid, or multi-cloud setups with ease, ensuring consistent data handling, secure networking, and smooth service integrations. - Cost Optimization: Keeps a tight rein on your compute costs, tracks usage, and optimizes resource allocation even across distributed providers and instances, ensuring cost-effectiveness. -
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Codestral Mamba
Mistral AI
FreeCodestral Mamba is a Mamba2 model that specializes in code generation. It is available under the Apache 2.0 license. Codestral Mamba represents another step in our efforts to study and provide architectures. We hope that it will open up new perspectives in architecture research. Mamba models have the advantage of linear inference of time and the theoretical ability of modeling sequences of unlimited length. Users can interact with the model in a more extensive way with rapid responses, regardless of the input length. This efficiency is particularly relevant for code productivity use-cases. We trained this model with advanced reasoning and code capabilities, enabling the model to perform at par with SOTA Transformer-based models. -
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Flyte
Union.ai
FreeThe workflow automation platform that automates complex, mission-critical data processing and ML processes at large scale. Flyte makes it simple to create machine learning and data processing workflows that are concurrent, scalable, and manageable. Flyte is used for production at Lyft and Spotify, as well as Freenome. Flyte is used at Lyft for production model training and data processing. It has become the de facto platform for pricing, locations, ETA and mapping, as well as autonomous teams. Flyte manages more than 10,000 workflows at Lyft. This includes over 1,000,000 executions per month, 20,000,000 tasks, and 40,000,000 containers. Flyte has been battle-tested by Lyft and Spotify, as well as Freenome. It is completely open-source and has an Apache 2.0 license under Linux Foundation. There is also a cross-industry oversight committee. YAML is a useful tool for configuring machine learning and data workflows. However, it can be complicated and potentially error-prone. -
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TrueFoundry
TrueFoundry
$5 per monthTrueFoundry provides data scientists and ML engineers with the fastest framework to support the post-model pipeline. With the best DevOps practices, we enable instant monitored endpoints to models in just 15 minutes! You can save, version, and monitor ML models and artifacts. With one command, you can create an endpoint for your ML Model. WebApps can be created without any frontend knowledge or exposure to other users as per your choice. Social swag! Our mission is to make machine learning fast and scalable, which will bring positive value! TrueFoundry is enabling this transformation by automating parts of the ML pipeline that are automated and empowering ML Developers with the ability to test and launch models quickly and with as much autonomy possible. Our inspiration comes from the products that Platform teams have created in top tech companies such as Facebook, Google, Netflix, and others. These products allow all teams to move faster and deploy and iterate independently. -
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ZenML
ZenML
FreeSimplify your MLOps pipelines. ZenML allows you to manage, deploy and scale any infrastructure. ZenML is open-source and free. Two simple commands will show you the magic. ZenML can be set up in minutes and you can use all your existing tools. ZenML interfaces ensure your tools work seamlessly together. Scale up your MLOps stack gradually by changing components when your training or deployment needs change. Keep up to date with the latest developments in the MLOps industry and integrate them easily. Define simple, clear ML workflows and save time by avoiding boilerplate code or infrastructure tooling. Write portable ML codes and switch from experiments to production in seconds. ZenML's plug and play integrations allow you to manage all your favorite MLOps software in one place. Prevent vendor lock-in by writing extensible, tooling-agnostic, and infrastructure-agnostic code. -
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Qwen-7B
Alibaba
FreeQwen-7B, also known as Qwen-7B, is the 7B-parameter variant of the large language models series Qwen. Tongyi Qianwen, proposed by Alibaba Cloud. Qwen-7B, a Transformer-based language model, is pretrained using a large volume data, such as web texts, books, code, etc. Qwen-7B is also used to train Qwen-7B Chat, an AI assistant that uses large models and alignment techniques. The Qwen-7B features include: Pre-trained with high quality data. We have pretrained Qwen-7B using a large-scale, high-quality dataset that we constructed ourselves. The dataset contains over 2.2 trillion tokens. The dataset contains plain texts and codes and covers a wide range domains including general domain data as well as professional domain data. Strong performance. We outperform our competitors in a series benchmark datasets that evaluate natural language understanding, mathematics and coding. And more. -
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Flowise
Flowise AI
FreeFlowise is open source and will always be free to use for commercial and private purposes. Build LLMs apps easily with Flowise, an open source UI visual tool to build your customized LLM flow using LangchainJS, written in Node Typescript/Javascript. Open source MIT License, see your LLM applications running live, and manage component integrations. GitHub Q&A using conversational retrieval QA chains. Language translation using LLM chains with a chat model and chat prompt template. Conversational agent for chat model that uses chat-specific prompts. -
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CodeQwen
Alibaba
FreeCodeQwen, developed by the Qwen Team, Alibaba Cloud, is the code version. It is a transformer based decoder only language model that has been pre-trained with a large number of codes. A series of benchmarks shows that the code generation is strong and that it performs well. Supporting long context generation and understanding with a context length of 64K tokens. CodeQwen is a 92-language coding language that provides excellent performance for text-to SQL, bug fixes, and more. CodeQwen chat is as simple as writing a few lines of code using transformers. We build the tokenizer and model using pre-trained methods and use the generate method for chatting. The chat template is provided by the tokenizer. Following our previous practice, we apply the ChatML Template for chat models. The model will complete the code snippets in accordance with the prompts without any additional formatting. -
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Mathstral
Mistral AI
FreeAs a tribute for Archimedes' 2311th birthday, which we celebrate this year, we release our first Mathstral 7B model, designed specifically for math reasoning and scientific discoveries. The model comes with a 32k context-based window that is published under the Apache 2.0 License. Mathstral is a tool we're donating to the science community in order to help solve complex mathematical problems that require multi-step logical reasoning. The Mathstral release was part of a larger effort to support academic project, and it was produced as part of our collaboration with Project Numina. Mathstral, like Isaac Newton at his time, stands on Mistral 7B's shoulders and specializes in STEM. It has the highest level of reasoning in its size category, based on industry-standard benchmarks. It achieves 56.6% in MATH and 63.47% in MMLU. The following table shows the MMLU performance differences between Mathstral and Mistral 7B. -
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ID Privacy AI
ID Privacy AI
$15 per monthID Privacy is shaping the future of AI by focusing on privacy-first solutions. Our mission is to deliver cutting edge AI technologies to empower businesses to innovate, without compromising security and trust. ID Privacy AI provides secure, adaptable AI model built with privacy in mind. We empower businesses in all industries to harness advanced AI. Whether it's optimizing workflows, improving customer AI chat experiences or driving insights while safeguarding data, we empower them. The team at ID Privacy met and developed the plan for AI as a Service solution under the guise of stealth. Launched with the most comprehensive knowledge base of ad technology, including multi-modal and multi-lingual capabilities. ID Privacy AI focuses on privacy-first AI for businesses and enterprise. Businesses can be empowered with a flexible AI Framework that protects data and solves complex challenges in any vertical. -
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Qwen2.5
Alibaba
FreeQwen2.5, an advanced multimodal AI system, is designed to provide highly accurate responses that are context-aware across a variety of applications. It builds on its predecessors' capabilities, integrating cutting edge natural language understanding, enhanced reasoning, creativity and multimodal processing. Qwen2.5 is able to analyze and generate text as well as interpret images and interact with complex data in real-time. It is highly adaptable and excels at personalized assistance, data analytics, creative content creation, and academic research. This makes it a versatile tool that can be used by professionals and everyday users. Its user-centric approach emphasizes transparency, efficiency and alignment with ethical AI. -
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Qwen2.5-Coder
Alibaba
FreeQwen2.5-Coder-32B-Instruct has become the current SOTA open source code model, matching the coding capabilities of GPT-4o. It is a powerful and comprehensive coder, but also has good general and math skills. Qwen2.5 Coder currently covers six popular model sizes in order to meet the requirements of different developers. We explore the practicality and potential applications of Qwen2.5 Coder in two scenarios. These scenarios include code assistants and artifacts. Qwen2.5-Coder-32B-Instruct, as the flagship model of this open source release, has achieved the best performance among open source models on multiple popular code generation benchmarks and has competitive performance with GPT-4o. Coding repair is a key programming skill. Qwen2.5-Coder-32B-Instruct can help users fix errors in their code, making programming more efficient. -
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FauxPilot
FauxPilot
FreeFauxPilot, a self-hosted open-source alternative to GitHub Copilot, is a self-hosted open-source alternative. It uses the SalesForce CodeGen model on NVIDIA’s Triton Inference Server, with the FasterTransformer as the backend. Docker is required, as well as an NVIDIA GPU that has enough VRAM and the ability split the model over multiple GPUs, if necessary. Downloading models from Hugging Face, and converting them to FasterTransformer compatible formats is the setup. -
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Qwen2.5-Max
Alibaba
FreeQwen2.5-Max is an advanced Mixture-of-Experts (MoE) model from the Qwen team, trained on more than 20 trillion tokens and enhanced through Supervised Fine-Tuning (SFT) and Reinforcement Learning from Human Feedback (RLHF). It surpasses models like DeepSeek V3 in key benchmarks, including Arena-Hard, LiveBench, LiveCodeBench, and GPQA-Diamond, while also performing strongly in broader evaluations like MMLU-Pro. Available via API on Alibaba Cloud, Qwen2.5-Max can also be tested interactively through Qwen Chat, offering users a powerful tool for diverse AI-driven applications. -
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Qwen2.5-VL
Alibaba
FreeQwen2.5-VL is an advanced vision-language model in the Qwen series, offering improved visual comprehension and reasoning over its predecessor, Qwen2-VL. It can accurately interpret a wide range of visual elements, including text, charts, icons, and layouts, making it highly effective for complex image and document analysis. Acting as an intelligent visual agent, the model can dynamically interact with tools, analyze extended video content over an hour long, and identify key segments with precision. It also excels in object localization, generating bounding boxes or points with structured JSON outputs for various attributes. Additionally, Qwen2.5-VL supports structured data extraction from documents such as invoices, forms, and tables, benefiting industries like finance and commerce. Available in base and instruct versions across 3B, 7B, and 72B model sizes, it is accessible on platforms like Hugging Face and ModelScope for seamless integration. -
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SmolLM2
Hugging Face
FreeSmolLM2 offers a set of advanced, compact language models tailored for lightweight, on-device applications. With configurations ranging from the large 1.7B model to smaller 360M and 135M versions, SmolLM2 delivers high-quality text generation in real-time. Designed to run efficiently on resource-limited devices, these models support diverse tasks like content generation, coding help, and language understanding, making them ideal for mobile devices, edge computing, and other environments where computational power is constrained. SmolLM2 enables developers to integrate sophisticated AI capabilities into smaller, more accessible devices. -
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Pinecone
Pinecone
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. -
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Stack AI
Stack AI
$199/month 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. -
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Nekton.ai
Nekton.ai
$9 per monthUse plain English when describing your workflow steps. Nekton AI can automate steps if they are automated. You don't have to learn complicated tools to get started. Nekton can automate business and personal workflows. It integrates with thousands services. Automate workflows by collecting input from users. Send a link of your workflow so that others can run it. Sign-up is not required. Nekton can automate highly-customized processes, and you do not need to hire developers or learn complex platforms. Automate as you need. Mix manual and automated steps into a workflow. Automation is done in the cloud so you don't have to maintain or set up any infrastructure. Use services that you can't access from the internet to run automation locally. Process small to medium amounts of data. -
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LastMile AI
LastMile AI
$50 per monthCreate generative AI apps for engineers and not just ML practitioners. Focus on creating instead of configuring. No more switching platforms or wrestling with APIs. Use a familiar interface for AI and to prompt engineers. Workbooks can be easily streamlined into templates by using parameters. Create workflows using model outputs from LLMs and image and audio models. Create groups to manage workbooks between your teammates. Share your workbook with your team or the public, or to specific organizations that you define. Workbooks can be commented on and compared with your team. Create templates for you, your team or the developer community. Get started quickly by using templates to see what others are building. -
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PostgresML
PostgresML
$.60 per hourPostgresML is an entire platform that comes as a PostgreSQL Extension. Build simpler, faster and more scalable model right inside your database. Explore the SDK, and test open-source models in our hosted databases. Automate the entire workflow, from embedding creation to indexing and Querying for the easiest (and fastest) knowledge based chatbot implementation. Use multiple types of machine learning and natural language processing models, such as vector search or personalization with embeddings, to improve search results. Time series forecasting can help you gain key business insights. SQL and dozens regression algorithms allow you to build statistical and predictive models. ML at database layer can detect fraud and return results faster. PostgresML abstracts data management overheads from the ML/AI cycle by allowing users to run ML/LLM on a Postgres Database.