Blog

As LLM-powered agents become more complex, integrating them with tools, APIs, and private data sources remains a major challenge. Model Context Protocol (MCP) offers a clean, open standard for connecting language models to real-world systems through a modular, plug-and-play interface. In this article, we explore how MCP works.

In this short research blog post, we discuss how RL-finetuned reasoning language models can be used as a better alternative to regression-based critics when performing parallel trajectory search in test time, and how to train such models to prioritize precision over recall.

Large language models (LLMs) rely on fast data processing and distributed computing, making the efficiency of data processing tools a critical factor. Apache Spark streamlines text data preparation, enables parallel processing of massive datasets and simplifies the development of scalable ML workflows. This article explores Spark’s architecture, its advantages for data preparation and solutions to common limitations when working with LLM-scale models.

Moving large datasets between S3 buckets is often slow, unreliable and frustrating — especially across accounts or clouds. In this post, we share a fast, fully open-source workaround using SkyPilot, s5cmd and Nebius AI Cloud.

Audit Logs is a new feature, now available in preview in Nebius AI Cloud. With Audit Logs, our customers can improve their accountability, security and ensure compliance with regulatory requirements.

Pre-trained models let you skip the hassle of training new models by providing already hosted models you can fine-tune to build use-case-specific models. This article explores how this works, some use cases, upsides and downsides (plus countermeasures) of using pre-trained models.

Fine-tuning pre-trained AI models is a challenging task in itself. What strategy should you use and what resources do you require? This article presents a comprehensive overview of the AI model fine-tuning process.

We’ve had a very productive first quarter of 2025 at Nebius AI Studio, shipping major features, adding powerful new models and significantly expanding our infrastructure. This comprehensive roundup covers everything we’ve launched and where you can find us next.

If you work with personal data and need to store it in the cloud, you have to choose a cloud provider that offers appropriate storage. If you’re wondering whether our cloud can store and process personal user data, the answer is yes. Our platform ensures the secure storage of personal data, in full compliance with applicable regulations. But what does that mean in practice? Let’s break it down.

The increasing adoption of AI cloud solutions highlights the need for efficient, vendor-independent orchestration tools. dstack provides an open-source, AI-focused orchestration platform that emphasizes flexibility and ease of use for AI development. We are announcing an integration with dstack to enhance the developer experience for professional ML teams. You can now choose Nebius in dstack and start managing dev environments, executing training jobs and deploying models on our AI infrastructure.

Nebius AI Cloud is collaborating with NVIDIA Inception, a leading program for AI startups with over 22,000 members, to accelerate AI startup innovation. Nebius’ AI Lift program — announced at NVIDIA GTC 2025 — offers eligible NVIDIA Inception members up to $150,000 in cloud credits, access to AI technical expertise and support and a range of other exclusive benefits.

The end of the quarter is a great time to reflect and share a kind of changelog with you, highlighting what’s improved in Nebius AI Cloud over the past three months. This quarter, we launched new regions, released new services for MLOps and enhanced our existing product lineup to minimize your infrastructure concerns even further.