Accelerate your AI projects with a comprehensive toolkit

Leverage cost-effective language models, generative AI, advanced search, and more to streamline workflows, enhance accuracy, and scale AI solutions.

Access popular AI models for free, with playground testing, seamless integration, and enterprise-ready deployment through built-in tools.

Hub for discovering foundation models, curated by Microsoft, NVIDIA, Hugging Face, and Meta, packaged and optimized for Azure AI Studio.

Extension to download, evaluate, adjust, and deploy AI models, including guided walkthroughs for popular SLMS, within applications or to cloud services.

Develop custom GenAI to deliver enterprise-ready models with precise data curation, cutting-edge customization, retrieval-augmented generation (RAG), and accelerated performance.

A set of accelerated inference microservices that allow organizations to run AI models on NVIDIA GPUs anywhere.

Provides access to GPU-accelerated software, including performance-optimized containers, pretrained AI models, and industry-specific SDKs.

Open-source SDK that allows developers to easily combine AI services with conventional programming languages.

Enhance LLM capabilities by integrating an information retrieval system, providing grounding data and allowing control over data used.

Open-source library for developers to build complex and dynamic applications leveraging the capabilities of LLMs, serving as an orchestration tool for prompts.

Simplify orchestration, optimization, and automation of LLM workflows, enabling development of complex LLM applications using customizable and conversable agents.

Experimental library that uses type definitions in your codebase to retrieve structured AI responses that are type-safe, aiming to bridge the gap between natural language, application schemas, and APIs.

World-class models for embedding, reranking, and ingestion to quickly unlock accurate insights from massive volumes of enterprise data.

Libraries that speed up widely adopted operations and algorithms, reducing time to insight as questions evolve.

Bind tools with LangChain to NIM microservices to create structured outputs that bring agent capabilities to your applications.

Optimize and automate IT operations to improve efficiency, reduce costs, and enhance decision-making for more adaptive and intelligent systems management.

Automate the build and deployment process in Visual Studio Code with a single sign-on experience to access Azure resources and services.

Automate, customize, and execute workflows right in your repo. Discover, create, and share actions to perform any job you'd like, including CI/CD.

Manage and track changes to your Kubernetes apps with built-in version control. Run and debug containers directly in any Kubernetes environment.

Scalable data curation microservice that enables developers to sort through trillion-token multilingual datasets for pretraining LLMs.

High-performance, scalable microservice that simplifies fine-tuning and alignment of LLMs for domain-specific use cases.

Evaluate custom LLMs and retrieval-augmented generation (RAG) efficiently and reliably across diverse academic and custom benchmarks with APIs.

Automatically scan your code for vulnerabilities and dependencies to ensure you catch security issues early, before they become risks in production.

Leverage AI-driven threat analysis to quickly identify and respond to emerging security threats, enabling faster, more informed decision-making.

Secure your entire ecosystem—from identities to endpoints—by detecting and mitigating advanced cyberattacks, minimizing potential damage and reducing downtime.

Centralize threat detection and investigation with a scalable, cloud-native SIEM, allowing you to efficiently respond to security incidents across your entire environment.

Automatically filter harmful or inappropriate AI-generated content, ensuring a safe and trustworthy experience for users interacting with your applications.

Safeguards generative AI systems by orchestrating dialog management, ensuring accuracy, appropriateness, and security in smart applications with LLMs.