Nebius User Experience: A Field Report

By: Howe Wang, and DJ Rich

Posted: Updated:

Nebius Office Nebius Office.

In the rapidly evolving landscape of enterprise AI and machine learning, specialized cloud platforms are emerging as viable alternatives to traditional hyperscalers like AWS, Azure, and Google Cloud. Nebius—launched in late 2023 by the Nebius Group (formerly part of Yandex)—positions itself as a purpose-built infrastructure provider for AI and generative AI workloads. Real-world feedback highlights both its strengths and growing pains: users praise the self-serve setup, GPU reliability, comprehensive AI stack and flexible pricing, while also pointing to support delays and limited documentation for advanced use cases. For many teams, Nebius fits into a multi-provider strategy, chosen for its cost-performance trade-offs and integration flexibility. This review offers an impartial look at Nebius’s developer experience, compares it with platforms like CoreWeave and AWS, and evaluates its suitability across different team profiles and AI infrastructure needs.

What Makes Nebius Stand Out

Nebius carves out its niche with a feature set tailored to AI/ML practitioners:

  • Access to Advanced GPUs with NVIDIA Preferred Status: Nebius provides access to NVIDIA’s most recent GPUs, such as the H100, H200, and the upcoming GB200 NVL72. Nebius benefits from a close relationship with NVIDIA, ensuring early access to advanced accelerators like the Blackwell Ultra platform and enabling optimized AI infrastructure for training and inference.

  • AI Studio and Managed Kubernetes for Simplified Workflows: Nebius provides AI Studio, a developer-friendly platform that allows teams to deploy their own models or run inference using pre-trained models like Llama 3 and Mistral. With cost-efficient per-token pricing and batch processing support, AI Studio makes it easy to experiment, fine-tune, and scale inference workloads. Complementing this, Nebius’s managed Kubernetes service handles container orchestration, autohealing, and topology-aware scheduling—reducing the operational overhead of scaling and managing infrastructure for AI applications.

  • Pricing Flexibility and Cost Efficiency: Nebius offers GPU reservations starting at just $2/hour for training, with lower trial rates and per-token pricing for inference. This pricing model delivers estimated savings of 30%+ compared to the hyperscalers. Users can also reserve GPUs in advance to access additional discounts, making it a highly cost-effective option for AI and ML workloads.

  • Comprehensive AI Stack: Nebius offers an end-to-end AI platform purpose-built for large-scale ML workloads, developed by an engineering team that previously built Yandex’s internal AI infrastructure. Key components include managed MLflow for experiment tracking, a custom Kubernetes operator for Slurm (Soperator) enabling autoscaling and GPU job orchestration, and AI Studio for fine-tuning and inference on models like Llama 3 and Mistral with per-token pricing and batch support.

  • Data Sensitivity Support with EU Location Benefits: Nebius ensures secure GPU access for enterprises with strict data policies by operating within encrypted virtual private networks (VPNs), meaning sensitive data—like personal information or trade secrets—stays on-premises without needing to migrate to a public cloud; its EU-based data centers in Finland and Paris further enhance compliance with stringent regulations like GDPR, simplify data residency requirements, and offer low-latency performance for regional teams, all while maintaining top-tier privacy standards.

What to Watch For

  • Narrower Ecosystem for General Cloud Workloads: Nebius is purpose-built for AI and machine learning tasks, but its ecosystem lacks the breadth of services found in more mature platforms like AWS or Databricks. While it provides a managed ClickHouse service for analytics, broader offerings such as fully managed databases, business intelligence tools, or integrated ETL pipelines are limited. This often requires teams to supplement with external tools for non-AI workloads, reducing workflow cohesion.

  • Support and Reliability: While Nebius offers core support services, some users have reported slower response times compared to major providers like AWS or CoreWeave. This can introduce friction for teams running time-sensitive or production-critical workloads, especially during peak demand or infrastructure issues.

  • Inference Optimization Gap: Nebius trails Run:ai, a leader in advanced inference optimization, as it lacks Run:ai’s dynamic GPU allocation, model compression, and intelligent orchestration, which ensure minimal latency and high throughput for latency-critical tasks like autonomous driving; despite Nebius AI Studio’s competitive speed and cost-efficiency (up to 50% lower than competitors), its optimization capabilities are less robust for time-sensitive workloads.

  • Scalability and Maturity Risks: With a current GPU footprint of around 20,000 (targeting 35,000–60,000), Nebius is rapidly scaling but remains a newer entrant. This raises valid concerns about its ability to handle peak demand and maintain long-term platform stability.

Who Should Use Nebius?

Nebius aligns well with specific team needs:

  • Compute-Intensive Startups and Scale-ups: Ideal for teams building generative AI applications, training large models, or running high-throughput inference workloads. Nebius offers top-tier NVIDIA GPUs (H100, H200) with InfiniBand connectivity, and caters especially well to startups and scale-ups operating with $2M–$3M budgets by providing flexible pricing, low-cost reservations, and access to cloud credit programs like the AI Discovery Award (up to $100K).

  • Teams Seeking Streamlined AI Tools: Nebius’s AI Studio and managed Kubernetes services simplify workflows, allowing innovation-driven groups to focus on model development and deployment without extensive infrastructure management.

  • Global Teams: With data centers in Finland, Paris, Kansas City Missouri, and upcoming facilities in New Jersey and Iceland, Nebius ensures low-latency access and supports compliance with regional data regulations, benefiting distributed teams operating across different geographies.

  • Enterprises with Sensitive Data: Nebius offers features like VPN support to secure GPU access, aligning with stringent data policies and providing enterprises with the necessary tools to maintain data integrity and confidentiality.

Who Might Find Nebius Challenging to Use?

  • Teams Requiring Broader Cloud Services: Nebius’s focus on AI means limited support for general-purpose infrastructure like managed databases, BI tools, or serverless analytics—often requiring external platforms to fill the gap.

  • Support-Dependent Teams: Users have noted slower response times compared to hyperscalers or CoreWeave, which can be problematic for time-sensitive workflows.

  • Smaller Teams Without DevOps Expertise: While powerful, Nebius relies on tools like Kubernetes and Slurm that assume a baseline of operational knowledge, which may be a barrier for lean or non-specialist teams.

  • Risk-Averse Organizations: As a newer entrant with a smaller global footprint, Nebius may not meet the risk tolerance of enterprises that prioritize long-established providers with extensive compliance and uptime records.

Comparison to CoreWeave and Hyperscalers

Nebius occupies a unique middle ground between CoreWeave’s compute-focused infrastructure and the full-stack ecosystems of hyperscalers like AWS and Google Cloud. It surpasses CoreWeave in ecosystem development by offering integrated tools such as AI Studio and managed Kubernetes, which simplify model development and deployment. However, it still falls short of the seamless, end-to-end platforms offered by hyperscalers—such as SageMaker, which tightly integrates coding, training, and deployment workflows.

Where Nebius stands out is in pricing flexibility: it offers GPU reservations, predictable per-token inference pricing, and trial discounts that significantly undercut hyperscaler pricing. Through its Explorer Tier, Nebius provides access to high-performance GPUs starting at $1.50 per hour for up to 1,000 GPU hours monthly. Longer-term reservations reduce rates even further—H100s start at $2.00/hour and H200s at $2.30/hour with a three-month commitment—making it an attractive option for cost-conscious AI teams. Overall, Nebius’s pricing is up to 30% lower than hyperscalers, with additional discounts of up to 35% for sustained usage. The discount structure is particularly well-suited to workloads where teams can estimate training requirements in advance. If the size of the training data and the type of model are known upfront, teams can estimate total GPU hours required and reserve capacity accordingly to secure lower pricing. This level of cost predictability makes Nebius an efficient choice for training scenarios with clearly defined compute needs.