Directory AI & ML PlatformsLLM Infrastructure & APIsNebius (Yandex)

Nebius (Yandex)

About Nebius (Yandex)

Nebius provides a cloud platform designed specifically for AI innovators requiring scalable, high-performance infrastructure to support demanding AI workloads. Their solution integrates NVIDIA GPU accelerators, high-speed InfiniBand networking, and orchestration tools like Kubernetes and Slurm to enable seamless scaling from single GPUs to clusters with thousands of GPUs. This infrastructure supports both training and inference of large language models (LLMs) and other AI applications, delivering optimized efficiency and long-term value for enterprise AI deployments.

Targeted at enterprises and research organizations, Nebius offers fully managed services including deployment of MLflow, PostgreSQL, and Apache Spark, along with infrastructure as code capabilities via Terraform, API, and CLI. Their platform supports advanced AI use cases such as gene-editing automation and open-source LLM inference optimization, providing 24/7 expert support and architect assistance. Nebius also operates AI-optimized sustainable data centers, ensuring reliable and efficient compute resources for AI workloads at scale.

How to evaluate LLM Infrastructure & APIs

This is how the CIOPages Research Team evaluates this category. It is not an assessment of Nebius (Yandex). It comes from our Generative AI & LLM Platforms buyer guide.

25%

Model Capability & Task Fit

Reasoning and instruction-following on your tasks (not public leaderboards), multimodal coverage (text, image, audio, video) where you need it, context-window length for your documents, breadth of the model menu (frontier, mid, small), and measured quality on a held-out set of your real prompts

20%

Deployment, Data Residency & Governance

Where inference physically runs (multi-tenant API, your VPC, on-prem, air-gapped), explicit no-training-on-your-data and retention commitments, regional residency, SOC 2 / ISO 27001 / HIPAA / FedRAMP and EU AI Act alignment, and DLP, content filtering, and full prompt/response audit logging

15%

Build & Customization Tooling

First-class retrieval-augmented generation and grounding, managed fine-tuning and (where relevant) continued pre-training, an integrated evaluation and regression-testing harness, prompt/version management, and native or tightly integrated vector search over your corpora

15%

Agentic & Orchestration Readiness

Reliable tool/function calling, support for open agent protocols (MCP, A2A), a managed agent runtime with memory and state, multi-agent orchestration, and guardrails — permissions, human-in-the-loop checkpoints, and step-level tracing for autonomous workflows

15%

Portability & Lock-in Risk

How model-agnostic the API is, whether multiple model families are reachable through one interface, the real cost of swapping providers (prompt, tool-schema, and eval rework), reliance on open standards, and whether you can take fine-tuned weights or your data with you on exit

10%

Cost, Throughput & Operability

Token economics at your projected volume, batch and provisioned-throughput options, prompt-caching support, rate limits and quota headroom for production, p95 latency under load, regional availability and uptime SLAs, and built-in usage, cost, and quality observability

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