Nebius

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Nebius

Nebius Group (NASDAQ: NBIS) is a technology company focused on AI infrastructure. It emerged from the non-Russian operations of Yandex, rebranded in 2024, and now builds cloud, hardware, developer tools, and inference platforms. Its offerings are designed to support the full ML/AI lifecycle.

General
Release / Rebrand date	August 2024 (split from Yandex, renamed Nebius Group)
Author / Founder	Arkady Volozh
Headquarters	Amsterdam, The Netherlands
Type	AI Infrastructure / Cloud / Inference platform / Developer tools

Nebius AI Cloud & GPU Infrastructure

Nebius provides a cloud platform purpose-built for AI/ML workloads and high-performance compute.

AI-native cloud platform launched in October 2024, built from scratch to support full ML lifecycle (data preprocessing, training, fine-tuning, inference) using NVIDIA accelerated compute.
GPU-hardware: users can get access to NVIDIA H100, H200, L40S GPUs, the upcoming GB200 NVL72 platform, and Blackwell Ultra.
Scalable compute: supports large clusters, managed Kubernetes or Slurm orchestration, fast storage, interconnects (InfiniBand) etc.
Security & enterprise readiness: encryption, secure infrastructure, API / CLI / infrastructure-as-code tools, console UI.

Nebius AI Studio (Inference / Model Access)

Tooling for model deployment, inference, and open-source models:

AI Studio is Nebius’s inference service offering low-latency, cost-efficient endpoints. It supports open-source LLMs (such as Llama, Mistral, Nemo, Qwen, etc.).
Performance: claimed up to ~4.5× faster than competitors, with pricing up to ~50% cheaper.
Flexible pricing: premium / high-speed vs cost-efficient endpoints.
Rapid scaling: designed to go from experiment / prototype to large production workload.

Major Highlights & Strategic Moves

In September 2025, Nebius signed a US$17.4 billion multi-year agreement with Microsoft, to provide AI infrastructure over five years; could rise to ~$19.4B depending on capacity / services.
Nebius is raising US$3 billion to accelerate growth: with $2 billion via convertible notes, $1 billion via public offering. These funds will go toward hardware, more data centers, securing land, expanding footprint.
Global expansion: deploying GPU clusters / data centers in UK (Blackwell Ultra GPUs), US (Vineland, New Jersey data center), Europe (Finland, France), among others.
Nebius Homepage
Nebius AI Cloud
Nebius AI Studio / API
Nebius Newsroom / Blog (for platform launches, infrastructure updates)

Additional Notes

Nebius originated from a spinoff / restructuring of Yandex: after divesting the Russian operations under Yandex, the company refocused on international AI infrastructure.
It is publicly traded (Nasdaq: NBIS) and is positioning itself among competitors in AI infrastructure / “neocloud” category.
Competitive differentiation includes: vertical integration (hardware, data centres, software stack), open-source model support, and cost-performance trade-offs in inference.

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Nebius AI technology page Hackathon projects

Discover innovative solutions crafted with Nebius AI technology page, developed by our community members during our engaging hackathons.

LoanFlow AI

LendAI is an intelligent multi-agent loan processing platform built for the AI Multi-Agent Hackathon. It demonstrates how multiple specialized AI agents can collaborate seamlessly to handle complex, real-world financial workflows without human intervention. The system uses three independent agents, each with a single responsibility, working in sequence: **Agent 1 — Docs Collection Agent** Greets applicants warmly and collects all required documents conversationally. Instead of uploading files or filling forms, users simply chat — providing their CNIC, monthly income, employer details, and credit history one step at a time. The agent validates each piece of information before handing off to the next stage. **Agent 2 — Risk Management Agent** Acts as the analytical backbone. It parses submitted documents and computes a comprehensive risk profile using three key financial indicators: Debt-to-Income (DTI) ratio, employment stability score, and existing loan burden. **Agent 3 — Loan Decision Agent** Makes the final credit decision and generates a personalized loan offer based on the risk profile. For LOW RISK applicants, it approves loans up to 10× monthly income at 12% interest. For MEDIUM RISK, it offers conditional approval (6× income at 18%). For HIGH RISK, it provides a transparent decline with actionable next steps. Every approved offer includes three repayment tenure options with exact EMI calculations. **Why This Matters** LendAI showcases how AI agents can handle high-stakes financial decisions with transparency, speed, and accuracy. Users get instant decisions and clear reasoning about their credit profile. Built on **band.ai** for agent orchestration and **Featherless AI** for model inference, LendAI proves that complex multi-agent workflows are not only possible but practical for real business applications.

MEV Shield: Micropayment-Gated Mempool AI

MEV Shield is a real-time pre-trade intelligence layer for DeFi traders and autonomous agents. It captures live Ethereum mempool transactions from multiple sources (PublicNode, Alchemy, Infura), scoring each pending swap using proprietary microstructure signals — Hawkes process order-flow clustering, Kyle's Lambda price impact, and VPIN toxicity — to detect sandwich attacks and frontrunning risk before execution. Each transaction receives a cliff score (VPIN × LOB depth × gas Z-score) that quantifies extraction probability. Pools are monitored continuously with per-pool intelligence cards showing Hawkes intensity, Kyle's Lambda impact, and next-attack timing estimates. Bot fingerprinting clusters known MEV addresses by behavioral signature. Intelligence is delivered as a metered API where consumers pay $0.01 per alert via Nitrolite ERC-7824 off-chain state channels, eliminating per-query gas costs. Payments are cryptographically signed ECDSA state updates — verifiable without touching the chain. When accumulated payments reach a threshold, the channel closes via a Flashbots MEV-protected transaction and settles autonomously as a real USDC transfer on-chain via Circle Arc developer-controlled wallets, confirmed on ETH-Sepolia. Claude, AIsa.one provides pre-trade risk assessments on every fifth alert, combining quantitative microstructure signals with natural language recommendations covering slippage tolerance, private mempool routing, and sandwich guard configuration. AI agents can autonomously consume and pay for real-time financial intelligence. No subscriptions, no API keys per user, no manual settlement — just a self-sustaining micropayment loop. Live mempool data flows in, off-chain ECDSA payments accumulate, on-chain USDC settles when the threshold is reached. DeFi traders lose an estimated $1-3B annually to MEV extraction. MEV Shield is the pre-trade defense layer that makes institutional-grade microstructure analysis accessible at $0.01 per query.

ForgeClaw x Kraken Autonomous Crypto Trading Agent

orgeClaw × Kraken is an autonomous crypto trading agent built on a production-grade stack that goes far beyond a simple trading bot. The agent pipeline executes 7 sequential Temporal activities: connecting to the Kraken CLI MCP server for market data, fetching AI signals from PrismaAPI, computing RSI(14) and VWAP deviation analysis with volume confirmation, gating each trade behind an ERC-8004 USDC micropayment for trustless execution, executing paper trades with 10% position limits and 2% stop loss enforcement, tracking realized PnL with a FINRA-style SQLite audit log, and delivering formatted trade summaries to Slack. ForgeClaw acts as the design-time layer — a BPMN agent composer (forgeclaw-app.vercel.app) that generates Temporal workflows from visual pipelines. VerifyClaw scans every agent skill against 25 SAFE-MCP threat patterns before deployment, with a max risk score of 3/100 on this agent. Redpanda streams all trade events across 5 Kafka-compatible topics in real time. The dashboard is a pixel-accurate Kraken Pro replica with live signal feed, agent workflow panel, executor, trade history, PnL analytics, open orders, Slack log, and ERC-8004 payment ledger — all backed by a FastAPI service proxied through nginx and pulling from SQLite on every cycle. Clicking Run Agent fires a real Temporal workflow end-to-end, not a simulation. Infrastructure: Temporal + PostgreSQL for durable orchestration, Redpanda for event streaming, nginx reverse proxy, Docker Compose for one-command deployment.

Secure-AI

LLMs have recently adopted persistent memory to provide models with better user knowledge and personalisation. However, this introduces a new vector for adversarial manipulation. This report investigates Memory Injection, a threat model where adversaries exploit indirect prompt injection within web content to poison an agent's long-term memory. Employing user manipulation scenarios, we show that memory attacks, should they succeed, are effective at changing model behaviour towards the user, often more so than a direct system prompt. Hence, this highlights the need for more robust evaluations of memory updates in agentic memory systems.