Nebius Group¶

What it is¶

Nebius Group (Nebius AI Cloud) is an AI infrastructure and cloud platform provider delivering high-performance GPU clusters (NVIDIA H100, H200, B200), managed Kubernetes, managed databases, and AI developer services (including Tavily for agentic web search). As of early January 2027, Nebius operates enterprise AI data centers across Europe and North America, supporting frontier model training, fine-tuning, and high-throughput FastMCP 3.1 inference workloads.

What problem it solves¶

Training frontier models and deploying multi-agent applications requires specialized AI cloud infrastructure that traditional hyperscalers often struggle to deliver efficiently: - GPU Cluster Orchestration: Delivers ultra-low latency InfiniBand inter-node connections for distributed model training (PyTorch, DeepSpeed). - Integrated Agent Services: Hosts agentic search capabilities through parent ownership of Tavily. - Cost-Optimized AI Compute: Offers competitive GPU instance rates with flexible commitment terms for AI startups and enterprise AI labs. - FastMCP 3.1 & Model Serving: Managed Slurm, Kubernetes, and inference server deployments optimized for FastMCP 3.1 tool-calling streaming APIs.

Where it fits in the stack¶

Providers / AI Cloud Infrastructure. Nebius sits alongside GPU cloud providers (Lambda Labs, CoreWeave, RunPod, AWS Bedrock) and specialized AI service stacks.

Typical use cases¶

Frontier LLM Training & Fine-Tuning: Running large-scale distributed training jobs using Axolotl, Llama-Factory, or PyTorch FSDP across NVIDIA H100/B200 clusters.
Agentic Search Integration: Deploying agent search workloads via Tavily API hosted on Nebius AI Cloud.
High-Throughput Inference Clusters: Provisioning vLLM or Aphrodite Engine deployments on Nebius Kubernetes with FastMCP 3.1 streaming support.
Private Enterprise Deployment: Hosting air-gapped AI workloads with GDPR and SOC2 compliance.

Strengths¶

Bare-Metal & Managed GPU Compute: Native InfiniBand connectivity with minimal virtualization overhead.
Developer-Friendly API & CLI: Infrastructure provisioning via Terraform, Ansible, and the nebius CLI.
Ecosystem Integration: Native parent-company integration with Tavily AI agent search services.
High Availability: Redundant Tier-3 data centers across major European and American regions.

Limitations¶

Focus on AI Compute: Optimized specifically for AI/ML workloads rather than general web app hosting or legacy enterprise IT services.
Regional Footprint: Geographic availability expands rapidly but is concentrated in key AI cloud hubs.

When to use it¶

When requiring multi-GPU or multi-node clusters for LLM training and fine-tuning.
When seeking a cloud provider with native integration for agentic web search (Tavily).
When looking for cost-effective NVIDIA H100/B200 GPU compute with InfiniBand interconnects.

When not to use it¶

When hosting general web applications, traditional monolithic SQL databases, or legacy non-AI enterprise software.
When requiring local on-premise deployments without public cloud interconnects.

Getting started¶

Create a Nebius AI Cloud account at nebius.com.
Install the Nebius CLI or Terraform provider.
Authenticate with your cloud credentials: nebius auth login.

CLI examples¶

Authenticating via Nebius CLI¶

nebius auth login --iam-token $NEBIUS_IAM_TOKEN

Listing Available GPU Instances via CLI¶

nebius compute instance list --folder-id $NEBIUS_FOLDER_ID

API examples¶

Provisioning a Nebius GPU Instance via Terraform¶

terraform {
  required_providers {
    nebius = {
      source = "nebius/nebius"
      version = ">= 0.4.0"
    }
  }
}

provider "nebius" {
  region = "eu-west-1"
}

resource "nebius_compute_instance" "gpu_node" {
  name        = "llm-training-h100-node"
  zone        = "eu-west-1-a"
  platform_id = "gpu-h100-sxm5"

  resources {
    cores  = 64
    memory = 512
    gpus   = 8
  }

  boot_disk {
    initialize_params {
      image_id = "ubuntu-24-04-cuda-12-8"
      size     = 1000
    }
  }

  network_interface {
    subnet_id = "subnet-ai-cluster-01"
    nat       = true
  }
}

Checking Nebius API Service Health with Python¶

import requests

NEBIUS_API_KEY = "your_nebius_cloud_api_key"
HEADERS = {
    "Authorization": f"Bearer {NEBIUS_API_KEY}",
    "Content-Type": "application/json"
}

# Fetch active GPU compute clusters
response = requests.get("https://api.nebius.cloud/v1/compute/instances", headers=HEADERS)
if response.status_code == 200:
    instances = response.json().get("instances", [])
    print(f"Active Nebius GPU Instances: {len(instances)}")
    for inst in instances:
        print(f" - ID: {inst['id']} | Status: {inst['status']} | GPU Type: {inst.get('gpu_type')}")
else:
    print(f"Failed to query Nebius API: {response.status_code}")

Sources / references¶

Contribution Metadata¶

Last reviewed: 2027-01-07
Confidence: high