- AI Developers & Engineers: Build multimodal agents—text, image, video, audio—using tested workflows.
- Enterprise Architects: Integrate scalable retrieval-augmented generation (RAG) systems and research assistants.
- Data Teams & Researchers: Ingest and query large multimodal datasets like PDFs or video archives.
- Solution Providers: Assemble customer-service chatbots, security screening pipelines, or voice assistants.
- Startups & Scale-Ups: Launch AI applications faster with enterprise-grade blueprints.
How to Use Build by NVIDIA?
- Browse Blueprints: Choose from options like “Enterprise Research Agent,” “Video Search & Summarization,” or “RAG Pipeline.”
- Install Microservices: Deploy NeMo microservices via NIM—including retrievers, generators, summarizers, audio processors.
- Connect Data Sources: Ingest PDFs, video, audio, or enterprise documents using GPU-accelerated extractors.
- Configure Agent Flow: Use reference code and Helm charts to customize workflows with RAG, chat, audio/video pipelines.
- Deploy & Scale: Launch on any infrastructure—Kubernetes, cloud providers, or on-prem—with GPU acceleration and monitoring tools.
- Enterprise-Ready Blueprints: Pre-built, scalable pipelines for real-world AI use cases.
- Microservices Architecture: Modular NeMo services allow composable, GPU-accelerated AI components.
- Multimodal Support: Handles text, images, audio, and video in unified flows (e.g., PDF-to-podcast, video summarization).
- Cloud-Agnostic & Scalable: Easily deployable across Kubernetes and major clouds with performance tuning.
- Open-Weight Friendly: Works with open models like Llama and Mistral, giving flexibility and data control.
- Turnkey enterprise workflows for rapid prototyping
- GPU-accelerated microservices for fast inference
- Strong multimodal capabilities out of the box
- Modular and easily customizable pipelines
- Open-model compatibility avoids vendor lock-in
- Requires GPU infrastructure and Kubernetes expertise
- May involve non-trivial integration overhead for custom use cases
- Largely enterprise-oriented—individual developers may find it heavyweight
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OpenAI - GPT 4.1
GPT-4.1 is OpenAI’s newest multimodal large language model, designed to deliver highly capable, efficient, and intelligent performance across a broad range of tasks. It builds on the foundation of GPT-4 and GPT-4 Turbo, offering enhanced reasoning, greater factual accuracy, and smoother integration with tools like code interpreters, retrieval systems, and image understanding. With native support for a 128K token context window, function calling, and robust tool usage, GPT-4.1 brings AI closer to behaving like a reliable, adaptive assistant—ready to work, build, and collaborate across tasks with speed and precision.
OpenAI - GPT 4.1
GPT-4.1 is OpenAI’s newest multimodal large language model, designed to deliver highly capable, efficient, and intelligent performance across a broad range of tasks. It builds on the foundation of GPT-4 and GPT-4 Turbo, offering enhanced reasoning, greater factual accuracy, and smoother integration with tools like code interpreters, retrieval systems, and image understanding. With native support for a 128K token context window, function calling, and robust tool usage, GPT-4.1 brings AI closer to behaving like a reliable, adaptive assistant—ready to work, build, and collaborate across tasks with speed and precision.
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Mistral Nemotron
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Mistral Nemotron
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Boundary AI
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Batteries Included
Batteries Included is a self-hosted AI platform designed to provide the necessary infrastructure for building and deploying AI applications. Its primary purpose is to simplify the deployment of large language models (LLMs), vector databases, and Jupyter notebooks, offering enterprise-grade tools similar to those used by hyperscalers, but within a user's self-hosted environment.
Batteries Included
Batteries Included is a self-hosted AI platform designed to provide the necessary infrastructure for building and deploying AI applications. Its primary purpose is to simplify the deployment of large language models (LLMs), vector databases, and Jupyter notebooks, offering enterprise-grade tools similar to those used by hyperscalers, but within a user's self-hosted environment.
Batteries Included
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UsageGuard
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BaseAI
Base AI is a cutting-edge platform designed to simplify and accelerate the development of AI-powered applications. It provides a robust backend infrastructure that handles the complexities of building, deploying, and managing AI models, allowing developers to focus on creating the core functionality of their applications. By abstracting away the technical challenges of AI engineering, Base AI makes it easier and faster to integrate artificial intelligence into products.
BaseAI
Base AI is a cutting-edge platform designed to simplify and accelerate the development of AI-powered applications. It provides a robust backend infrastructure that handles the complexities of building, deploying, and managing AI models, allowing developers to focus on creating the core functionality of their applications. By abstracting away the technical challenges of AI engineering, Base AI makes it easier and faster to integrate artificial intelligence into products.
Stakly
Stakly.dev is an AI-powered full-stack app builder that lets users design, code, and deploy web applications without writing manual boilerplate. You describe the app idea in plain language, set up data models, pages, and UI components through an intuitive interface, and Stakly generates production-ready code (including React front-end, Supabase or equivalent backend) and handles deployment to platforms like Vercel or Netlify. It offers a monthly free token allotment so you can experiment, supports live previews so you can see your app as you build, integrates with GitHub for code versioning, and is functional enough to build dashboards, SaaS tools, admin panels, and e-commerce sites. While not replacing full engineering teams for deeply custom or extremely large scale systems, Stakly lowers the technical barrier significantly: non-technical founders, product managers, solo makers, or small agencies can use Stakly to create usable, polished apps in minutes instead of weeks.
Stakly
Stakly.dev is an AI-powered full-stack app builder that lets users design, code, and deploy web applications without writing manual boilerplate. You describe the app idea in plain language, set up data models, pages, and UI components through an intuitive interface, and Stakly generates production-ready code (including React front-end, Supabase or equivalent backend) and handles deployment to platforms like Vercel or Netlify. It offers a monthly free token allotment so you can experiment, supports live previews so you can see your app as you build, integrates with GitHub for code versioning, and is functional enough to build dashboards, SaaS tools, admin panels, and e-commerce sites. While not replacing full engineering teams for deeply custom or extremely large scale systems, Stakly lowers the technical barrier significantly: non-technical founders, product managers, solo makers, or small agencies can use Stakly to create usable, polished apps in minutes instead of weeks.
Stakly
Stakly.dev is an AI-powered full-stack app builder that lets users design, code, and deploy web applications without writing manual boilerplate. You describe the app idea in plain language, set up data models, pages, and UI components through an intuitive interface, and Stakly generates production-ready code (including React front-end, Supabase or equivalent backend) and handles deployment to platforms like Vercel or Netlify. It offers a monthly free token allotment so you can experiment, supports live previews so you can see your app as you build, integrates with GitHub for code versioning, and is functional enough to build dashboards, SaaS tools, admin panels, and e-commerce sites. While not replacing full engineering teams for deeply custom or extremely large scale systems, Stakly lowers the technical barrier significantly: non-technical founders, product managers, solo makers, or small agencies can use Stakly to create usable, polished apps in minutes instead of weeks.
WebDev Arena
LMArena is an open, crowdsourced platform for evaluating large language models (LLMs) based on human preferences. Rather than relying purely on automated benchmarks, it presents paired responses from different models to users, who vote for which is better. These votes build live leaderboards, revealing which models perform best in real-use scenarios. Key features include prompt-to-leaderboard comparison, transparent evaluation methods, style control for how responses are formatted, and auditability of feedback data. The platform is particularly valuable for researchers, developers, and AI labs that want to understand how their models compare when judged by real people, not just metrics.
WebDev Arena
LMArena is an open, crowdsourced platform for evaluating large language models (LLMs) based on human preferences. Rather than relying purely on automated benchmarks, it presents paired responses from different models to users, who vote for which is better. These votes build live leaderboards, revealing which models perform best in real-use scenarios. Key features include prompt-to-leaderboard comparison, transparent evaluation methods, style control for how responses are formatted, and auditability of feedback data. The platform is particularly valuable for researchers, developers, and AI labs that want to understand how their models compare when judged by real people, not just metrics.
WebDev Arena
LMArena is an open, crowdsourced platform for evaluating large language models (LLMs) based on human preferences. Rather than relying purely on automated benchmarks, it presents paired responses from different models to users, who vote for which is better. These votes build live leaderboards, revealing which models perform best in real-use scenarios. Key features include prompt-to-leaderboard comparison, transparent evaluation methods, style control for how responses are formatted, and auditability of feedback data. The platform is particularly valuable for researchers, developers, and AI labs that want to understand how their models compare when judged by real people, not just metrics.
inception
Inception Labs is an AI research company that develops Mercury, the world's first commercial diffusion-based large language models. Unlike traditional autoregressive LLMs that generate tokens sequentially, Mercury models use diffusion architecture to generate text through parallel refinement passes. This breakthrough approach enables ultra-fast inference speeds of over 1,000 tokens per second while maintaining frontier-level quality. The platform offers Mercury for general-purpose tasks and Mercury Coder for development workflows, both featuring streaming capabilities, tool use, structured output, and 128K context windows. These models serve as drop-in replacements for traditional LLMs through OpenAI-compatible APIs and are available across major cloud providers including AWS Bedrock, Azure Foundry, and various AI platforms for enterprise deployment.
inception
Inception Labs is an AI research company that develops Mercury, the world's first commercial diffusion-based large language models. Unlike traditional autoregressive LLMs that generate tokens sequentially, Mercury models use diffusion architecture to generate text through parallel refinement passes. This breakthrough approach enables ultra-fast inference speeds of over 1,000 tokens per second while maintaining frontier-level quality. The platform offers Mercury for general-purpose tasks and Mercury Coder for development workflows, both featuring streaming capabilities, tool use, structured output, and 128K context windows. These models serve as drop-in replacements for traditional LLMs through OpenAI-compatible APIs and are available across major cloud providers including AWS Bedrock, Azure Foundry, and various AI platforms for enterprise deployment.
inception
Inception Labs is an AI research company that develops Mercury, the world's first commercial diffusion-based large language models. Unlike traditional autoregressive LLMs that generate tokens sequentially, Mercury models use diffusion architecture to generate text through parallel refinement passes. This breakthrough approach enables ultra-fast inference speeds of over 1,000 tokens per second while maintaining frontier-level quality. The platform offers Mercury for general-purpose tasks and Mercury Coder for development workflows, both featuring streaming capabilities, tool use, structured output, and 128K context windows. These models serve as drop-in replacements for traditional LLMs through OpenAI-compatible APIs and are available across major cloud providers including AWS Bedrock, Azure Foundry, and various AI platforms for enterprise deployment.
Editorial Note
This page was researched and written by the ATB Editorial Team. Our team researches each AI tool by reviewing its official website, testing features, exploring real use cases, and considering user feedback. Every page is fact-checked and regularly updated to ensure the information stays accurate, neutral, and useful for our readers.
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