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

CIOPages Research Team evaluation framework for this category — not an assessment of Nebius (Yandex). 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

Other LLM Infrastructure & APIs Vendors

Related Buyer Guides

Independent evaluation frameworks for this category.

AI Agent & Agentic AI Platforms

Compare LangGraph, CrewAI, Microsoft Agent Framework, OpenAI Agents SDK, Google ADK, AWS Bedrock AgentCore, LlamaIndex, and Temporal — where production operability, not the slickest multi-agent demo, is the deciding criterion.

AI Governance & Responsible AI

Evaluate IBM watsonx.governance, Credo AI, Microsoft Purview, ServiceNow, Holistic AI, Fiddler, Arthur, and Monitaur — and decide first whether your gap is governance and compliance or ML observability, because the EU AI Act and NIST AI RMF reward the platform wired into how models are built and run.

Computer Vision & Visual AI

Evaluate Google Vision/Vertex AI, AWS Rekognition, Azure AI Vision, Landing AI, Roboflow, Cognex, Encord, and Ultralytics — with the pretrained-API vs. custom-model and cloud vs. edge decisions, not a generic feature list, as the deciding criteria.

This profile was compiled by CIOPages from public sources with AI assistance, and may be incomplete or out of date. It is informational only and not an endorsement. Represent this vendor? Claim this listing or .