# Nebius Group N.V.
*Also known as Nebius*

- Website: https://nebius.com
- Location: Amsterdam, Netherlands
- Agent profile: https://directory.haycion.ai/agents/nebius-com

> Nebius Group N.V. is a Nasdaq-listed AI cloud company delivering a unified, end-to-end platform for the AI journey to builders and enterprises worldwide.

Nebius is an AI cloud company delivering a unified end-to-end platform for the AI journey—from data handling and model training to production runtime and deployment. Built on deep in-house expertise, Nebius emphasizes an engineering culture that designs and operates large-scale platforms with global reach. The company serves AI builders and enterprises worldwide across industries including healthcare and life sciences, robotics and physical AI, financial services, media & entertainment, retail, and more. Nebius is listed on Nasdaq and maintains a growing global footprint.

**Mission:** To empower AI builders and enterprises worldwide by providing a reliable, end-to-end AI cloud platform that enables data preparation, model training and tuning, and production deployment.

## Products & Services

### [Managed Inference](https://nebius.com/services/token-factory)
*Platform*
Deliver Reliable, Fast, and Scalable Inference for Open-Source Models.

- **60+ Models** — Access Diverse Models
- **Fully Managed** — Focus on Development
- **Unlimited Scalability** — Scale Seamlessly
- **Batch Inference Pricing** — Optimize Costs
- **OpenAI-Compatible API** — Simplify Integration
- **Transparent Pricing** — Predict Costs
- **Serving Modes** — Choose Optimal Performance

### [Agentic Search](https://nebius.com/solutions/agentic-search)
*Solution*
Empowers AI agents to utilize real-time web data for informed decision-making.

- **Live Web Data Retrieval** — Access Current Information
- **Zero-Retention Privacy** — Maintain Data Privacy
- **High-Volume Web Queries** — Optimize Query Latency
- **Structured Content Extraction** — Reduce Token Waste
- **Comprehensive Research Reports** — Generate Informed Insights

### [Human Validation](https://nebius.com/solutions/tendem)
*Solution*
Enhance AI reliability with human expert validation through a streamlined integration.

- **Expertise Access** — Access Verified Domain Experts
- **Programmable Reliability** — Automate Human Escalation Processes
- **Quality Control Mechanisms** — Ensure High-Quality Outputs
- **Risk Management** — Lower Error Rates

### [AI Orchestration](https://nebius.com/orchestration)
*Platform*
Effortlessly manage and scale GPU workloads with AI Orchestration.

- **Scalable GPU Job Management** — Manage GPU Jobs Seamlessly
- **Fault Tolerance Design** — Ensure Workload Reliability
- **Pre-Validated Performance** — Optimize Performance Instantly
- **Soperator for Managed Slurm Clusters** — Launch Slurm Clusters Quickly
- **Integrations with SkyPilot and Ray** — Connect to Preferred Workflows

### [Serverless AI](https://nebius.com/serverless)
*Platform*
Instantly run AI workloads and reduce infrastructure overhead with Serverless AI.

- **No infrastructure setup** — Eliminate Setup Time
- **On-demand scaling** — Scale Compute Instantly
- **Endpoints for inference** — Deploy Models Instantly
- **Job runtime** — Execute Containerized Workloads
- **Pay-as-you-go** — Cost-Efficient Pricing Model
- **DevPod environments** — Create Interactive Environments

### [DataOps](https://nebius.com/dataops)
*Platform*
Streamline AI data management with a fully managed PostgreSQL database for rapid iteration and low latency.

- **Managed PostgreSQL Database** — Manage Data Effortlessly
- **Integrated AI Workflow** — Reduce Latency
- **Versatile Use Cases** — Support Diverse Applications
- **Human Intelligence Integration** — Incorporate Expert Feedback
- **Flexible Data Processing** — Expand Data Capabilities

### [ModelOps](https://nebius.com/modelops)
*Platform*
Streamline ML lifecycle management with comprehensive tracking and governance.

- **Experiment Tracking with MLflow** — Track Experiments Seamlessly
- **Support for the Full Model Lifecycle** — Manage Models End-to-End
- **Integrated Environment** — Run Experiments in One Place
- **Managed Service** — Simplify Model Management

### [Compute](https://nebius.com/compute)
*Platform*
Flexible and high-performance compute platform for AI workloads.

- **GPU-accelerated instances** — Utilize NVIDIA GPUs
- **Auto-scaling Clusters** — Dynamic Resource Allocation
- **Managed Kubernetes** — Deploy with Ease
- **Data Protection and Compliance** — Ensure Data Security
- **CPU-only instances** — Support CPU Workloads

### [Networking](https://nebius.com/networking)
*Platform*
Provides secure and scalable networking solutions tailored for cloud-based AI workloads.

- **Private By Default** — Ensures Complete Isolation
- **Security Groups** — Manage Traffic Safely
- **Virtual Networks** — Integrates Resource Communication
- **Advanced Topologies** — Customize Network Structure
- **Dynamic IP Addressing and Routing** — Tailor Network Flow

### [AI Storage](https://nebius.com/storage)
*Platform*
Maximize performance and efficiency with scalable, high-speed storage solutions for AI workloads.

- **High-speed dataset streaming** — Accelerate Training Cycles
- **Shared filesystem** — Enable Seamless Collaboration
- **Rapid checkpoints** — Enhance Training Efficiency
- **Intelligent storage tiers** — Optimize Costs Effectively
- **Ready for multi-modality** — Support Multi-Modal Workflows
- **Standard object storage** — Store Large Volumes of Data

## Market Segments

- **ML training infrastructure and orchestration** (market size $12.7B, CAGR 24.5%): Capabilities to schedule, run, and scale GPU-accelerated training jobs, manage clusters and checkpoints, and provide fault tolerance and pre-validated high-performance compute for model development.
- **Managed model inference and serving** (market size $23.4B, CAGR 26.8%): Managed hosting and serving of models with autoscaling endpoints, OpenAI-compatible APIs, batch inference pricing, and configurable serving modes to balance latency and throughput for production deployments.
- **AI data management and low-latency databases** (market size $4.8B, CAGR 22%): Data storage, streaming, and managed database capabilities optimized for AI workflows, including high-speed dataset streaming, tiered storage, rapid checkpoints, and managed PostgreSQL for RAG, agent state, and metadata.
- **Web research automation and evidence-based intelligence** (market size $3.5B, CAGR 16%): Automated web-enabled agents that extract structured data at scale, provide reasoning with citations, and deliver fresh intelligence for market research, competitive analysis, and large-scale data collection.
- **Human-in-the-loop engagement and process monitoring** (market size $3.0B, CAGR 22.5%): Capabilities that enable user interaction, task routing, performance monitoring, and exception handling to maintain process quality and operational oversight.

## Ideal Customer Profiles

### Growth Stage Technology Companies
Global technology and AI builders aiming to scale production-grade AI platforms.
- Industry: Technology and AI-focused enterprises across multiple industries.
- Geography: Global, with emphasis on North America and Europe.
- Pain points: Difficulty scaling AI workloads; complex ML pipelines; governance and compliance overhead; data fragmentation; integration friction.
- Business goals: Scale AI initiatives; accelerate time-to-value; reduce operational complexity; improve model quality.
- Positioning: A comprehensive, end-to-end AI platform that enables rapid experimentation, scalable production deployments, and secure data handling for technology and AI-driven enterprises.

#### Persona: Head Of AI And Data Science
- Needs: Clear visibility into experiments, scalable infrastructure, governance and cost control
- Goals: Deliver AI products, accelerate experimentation, scale production
- Challenges: Managing multiple experiments, reproducibility, data access bottlenecks
- Pain points: Prolonged cycle times, tool fragmentation, compliance concerns
- Solution: Leverage DataOps for data workflows, ModelOps for experiment tracking, and AI Orchestration for scalable pipelines.

#### Persona: ML Platform Architect
- Needs: Robust deployment patterns, reusable pipelines, reliable scheduling
- Goals: Deliver scalable ML platform, optimize resource use
- Challenges: Integrating disparate tools, dealing with GPU provisioning times
- Pain points: Bottlenecks in GPU provisioning, complex cluster management
- Solution: Use AI Orchestration and Compute to manage GPU clusters, Pre-validated performance, and Slurm integration.

#### Persona: ML Engineer
- Needs: Fast model experiments, accessible data, reproducible environments
- Goals: Build and iterate models quickly, reduce cycle time
- Challenges: Data availability, environment reproducibility
- Pain points: Slow data access, inconsistent environments
- Solution: Leverage Compute and DataOps, plus ModelOps for tracking; Serverless AI for on-demand compute.

### Regulated Industry Enterprises
Financial services and healthcare organizations requiring strict data governance and regulatory compliance.
- Industry: Financial Services, Healthcare, Life Sciences
- Geography: Global, with emphasis on North America and Europe
- Pain points: Compliance and risk management, data lineage, auditability, data privacy requirements, fragmented data sources
- Business goals: Strengthen regulatory compliance, reduce governance overhead, accelerate secure analytics
- Positioning: A comprehensive platform enabling secure, compliant data workflows and scalable AI deployment for regulated industries.

#### Persona: Compliance Officer
- Needs: Regulatory guidance, audit trails, data lineage
- Goals: Ensure compliance, minimize penalties
- Challenges: Keeping up with changing regulations
- Pain points: Manual processes, evidence fragmentation
- Solution: Leverage DataOps for auditable data pipelines, ModelOps for governance, and AI Storage with access controls to support compliant analytics.

#### Persona: Data Platform Manager
- Needs: Data lineage, governance, scalable pipelines
- Goals: Streamline data flows, cost control
- Challenges: Siloed data, integration issues
- Pain points: Data quality and provenance
- Solution: DataOps for unified pipelines, Compute for scalable processing, AI Storage for efficient data access, ModelOps for versioning.

#### Persona: Security Architect
- Needs: Secure architecture, encryption, access control
- Goals: Secure data and models, compliance
- Challenges: Implementing robust security across environments
- Pain points: Misconfigurations, audit complexity
- Solution: Networking for isolation, Compute with data protection, AI Storage, Serverless AI for on-demand, and DataOps for governance.

### Robotics And Industrial AI Innovators
Global manufacturers and robotics firms deploying AI at scale for automation.
- Industry: Robotics, Manufacturing, Industrial Automation
- Geography: Global
- Pain points: Managing GPU workloads for simulations; data transfer across devices; integration with control systems; reliability
- Business goals: Automate operations, deploy AI at edge, reduce downtime
- Positioning: A scalable AI platform enables rapid development and deployment of AI across robotics and manufacturing environments.

#### Persona: Robotics Platform Engineer
- Needs: Edge deployment capabilities, robust orchestration
- Goals: Deploy AI models in production across devices
- Challenges: Hardware constraints, latency
- Pain points: Provisioning delays, debugging distributed systems
- Solution: AI Orchestration, Compute, Serverless AI to run at edge; DataOps for edge data supply

#### Persona: Simulation Engineer
- Needs: Accurate simulation data, scalable compute
- Goals: Improve simulation results, faster iterations
- Challenges: Long training times
- Pain points: Data transfer bottlenecks
- Solution: Compute, AI Storage, AI Orchestration, Serverless AI

#### Persona: ML Engineer
- Needs: Fast experimentation, reliable testing
- Goals: Improve models for robotics applications
- Challenges: Hardware constraints, reproducibility
- Pain points: Cluster provisioning delays
- Solution: Compute, AI Orchestration, Serverless AI

### Media And Retail AI Teams
Media and retail brands leveraging AI for content personalization, insights, and automation.
- Industry: Media and Entertainment, Retail
- Geography: Global
- Pain points: Personalization at scale, content recommendations, content generation, data-driven decisions, licensing constraints
- Business goals: Increase engagement and monetization, optimize content workflows
- Positioning: A platform to accelerate personalized content and data-driven decision-making for media and retail brands.

#### Persona: Director Of Content And Personalization
- Needs: Personalized content, performance metrics
- Goals: Boost engagement, optimize ROI
- Challenges: Rights management, data silos
- Pain points: Limited visibility into content performance
- Solution: AI Orchestration, DataOps, Compute, Serverless AI to accelerate personalization and data-driven workflows

#### Persona: Marketing Analytics Lead
- Needs: Campaign metrics, segmentation, insights
- Goals: Increase conversions, optimize spend
- Challenges: Data integration from multiple channels
- Pain points: Slow data processing
- Solution: DataOps, Compute, AI Storage

#### Persona: Data Engineer
- Needs: Reliable data pipelines, data freshness
- Goals: Maintain data pipelines and models
- Challenges: Data quality, pipeline failures
- Pain points: ETL friction, data latency
- Solution: DataOps, Compute, ModelOps