Designing an AI Infrastructure Stack Like Nebius: A Practical Guide for DevOps

Hook: If you’re running AI workloads in production, you’re probably burned by costs, noisy neighbors, and surprise latency spikes. Here’s how a neocloud AI provider like Nebius would design a dependable, cost-effective AI infra stack in 2026 — and how you can implement those patterns today.

Teams shipping models fast need more than GPUs and Kubernetes. They need an architecture that balances orchestration, multi-tenant isolation, cost optimization, and SLO-driven operations. This guide reverse-engineers the practical components and operational practices that power a full-stack neocloud AI provider — actionable for DevOps, SREs, and platform engineers.

Executive summary — what you’ll get

• Concrete architectural components and why they matter (control plane, data plane, model store, serving layer).
• Practical Kubernetes patterns for GPU orchestration and multi-tenant isolation.
• Cost optimization playbook: spot pools, quantization, batching, autoscaling, and cost SLOs.
• Operational model: SLO-driven ops, runbooks, observability, and drift detection.
• 2026 trends to watch: DPUs/SmartNICs, composable GPUs, foundation-model economics, and inference marketplaces.

1. Anatomy of a neocloud AI provider (reverse-engineered)

Think of the stack in two broad planes: the control plane (management, metadata, billing, tenant control) and the data/compute plane (GPU clusters, storage, model serving). Separating these lets you evolve each independently and enforce multi-tenant policies.

Core components

• Orchestration layer: Kubernetes clusters with specialized node pools for GPU types (A100, H100, AMD Instinct) and CPU-only pools for control and light inference.
• Model registry & artifact store: S3-compatible object store + immutable model registry (OCI + content-addressable hashes + provenance).
• Serving layer: Triton / KServe / custom microservices for high-throughput inference; Ray Serve or BentoML for complex pipelines.
• Scheduler & autoscaler: cluster-autoscaler, KEDA, GPU-aware autoscalers, and spot instance managers.
• Security & tenancy: namespace RBAC, network policies, runtime sandboxes (gVisor/Kata), and workload identity.
• Telemetry & SLO control: Prometheus + Thanos/Cortex, OpenTelemetry traces, model observability (drift, data quality).
• Billing & cost engine: per-tenant chargeback (GPU-hour, storage, egress, model token metrics).

2. Kubernetes patterns for GPU orchestration

By 2026, Kubernetes remains the control plane of choice, but the patterns have matured. Below are practical patterns Nebius-like platforms use.

Node pools and specialization

• Create separate node pools for training vs inference vs low-latency realtime inference. Use taints/tolerations and node selectors.
• Label nodes by GPU capability: MIG-friendly, HBM size, PCIe topology. Match workloads to hardware characteristics.

# example: node pool taint for GPU inference
apiVersion: v1
kind: Pod
metadata:
  name: inference-pod
spec:
  nodeSelector:
    accelerator: h100
  tolerations:
  - key: "gpu-inference"
    operator: "Equal"
    value: "true"
    effect: "NoSchedule"
  containers:
  - name: model
    image: myorg/triton:latest
    resources:
      limits:
        nvidia.com/gpu: 1

Device plugins and GPU sharing

Use vendor device plugins (NVIDIA GPU device plugin) and enable MIG or vGPU when supported. In 2026, emerging composition layers allow multiple small inference tasks to share a single larger GPU more safely.

• Enable MIG on H100/A100 and schedule pods to MIG slices when low-latency, cost-sensitive inference is needed.
• Use MPS for throughput-sensitive workloads where fragmentation is acceptable, but prefer MIG or vGPU for stronger isolation.

Autoscaling strategies

• Use HPA for pod-level scaling (latency/queue length metrics). Use Vertical Pod Autoscaler for adaptive resources in inference containers.
• Cluster-autoscaler plus a spot-instance autoscaler: maintain a mixed pool (on-demand + spot) and schedule interruptible training jobs on spot nodes with checkpointing.
• For batch training, leverage job-queue schedulers (Volcano, Ray cluster autoscaler) that are GPU-aware and can preempt low-priority workloads.

3. Multi-tenant isolation — practical approaches

Multitenancy is a spectrum. Nebius-like providers will use layered isolation to match tenant risk and pricing tiers.

Isolation tiers

1. Soft isolation — Namespaces, RBAC, network policies. Good for dev/test and low-privilege workloads.
2. Strong isolation — Runtime sandboxes (Kata/gVisor), dedicated node pools, MIG/vGPU. For higher trust boundaries and regulated data.
3. Physical isolation — Dedicated clusters or physical hosts for highest security; usually premium offering.

Tenant networking

• Use CNI with support for network policies and eBPF filters (Cilium). Enforce per-namespace egress rules and encrypt east-west traffic.
• Use service mesh selectively for secured multi-tenant APIs; inject sidecars only for paying tiers since meshes add latency and CPU overhead.

Data governance & encryption

• Encrypt at rest (object store + disk) and in transit. Use envelope encryption with tenant-specific keys where required.
• Provide ephemeral credential issuance for model pulls and integrate with workload identity (OIDC) for short-lived tokens.

4. Model serving — high-throughput and low-latency patterns

Serving is the business end of the stack. Your design must trade off latency, cost, and accuracy.

Serving topologies

• High-performance inference: Triton on GPU with dynamic batching, model ensemble, and client-side batching.
• Model-as-microservice: containerized model server behind autoscaling Kubernetes service (best for custom pre/post processing).
• Streaming pipelines: Ray Serve or KServe for stateful models, streams, or feature lookups.

Optimizations

• Quantization (INT8/INT4) reduces memory and increases throughput. Apply calibration and measure accuracy loss.
• Mixed precision and tensor cores for NVIDIA GPUs reduce cost per token on transformer models.
• Dynamic batching and request coalescing at the server or gateway lower per-inference overhead.

Dynamic instance pools

Maintain a hot pool of GPUs for latency-sensitive tenants and a cold/spot pool for background or batch inferences. Promote/demote via a fast provisioning layer (pre-warmed container images, golden snapshots).

5. Cost optimization playbook

Cost is often the single largest pain point for teams running models. Nebius-like operators optimize across hardware, software, and operations.

Hardware & procurement

• Buy a balanced mix: large GPUs for high-throughput training, smaller MIG-sliced GPUs for inference.
• Negotiate spot-like capacity (partner exchanges) and contract DPUs/SmartNICs to offload networking and security for dense packing.

Scheduling & pricing strategies

• Use reclaimable (spot) pools for non-urgent workloads and automatic checkpointing for training state.
• Chargeback per inference and per-token for customers; expose cost APIs so tenants can budget and set limits.

Software-level savings

• Model compression, pruning, and quantization to run smaller instances on cheaper hardware.
• Adaptive inference: route simple requests to CPU-based tiny models, heavy requests to GPU full models.
• Autoscaling with thresholds tuned to the business metric (cost-per-1000-inferences, latency SLOs).

Monitoring cost

Track GPU-hour, active GPUs, egress, and model token usage. Implement a cost SLO — e.g., 95% of requests cost less than $X per 1k inferences.

6. SLO-driven operations: telemetry, SRE, and playbooks

A Nebius-like provider runs ops by SLOs, not by ad-hoc alerts. Here’s how to implement that model.

Define SLIs and SLOs

• Key SLIs: p95 latency, p99 latency, error rate (5xx), availability, model accuracy drift, cost per inference.
• Set SLOs per workload class (production-critical vs best-effort). Use error budgets to decide on feature rollout and workload preemption.

Observability stack

• Metrics: Prometheus + Thanos/Cortex for long-term storage and per-tenant metrics aggregation.
• Tracing: OpenTelemetry integrated across model pipelines to trace pre/post-processing latencies.
• Model observability: drift detection (WhyLabs, Evidently, Arize) integrated with telemetry: monitor input distribution shifts and prediction quality.

Operational playbooks

• Create playbooks tied to SLO breaches: P95 latency spike, GPU OOM, model drift alert.
• Automate remediation where safe: scale-up/inject additional pods, promote replicas from cold pool, failover to CPU fallback model.
• Use runbooks with clear rollback and customer communication templates for incidents impacting tenants.

Run your platform by SLOs: treat the error budget like your most important currency. If you spend it, you must pay it back with stability work.

7. Model governance and reproducibility

AI providers must prove lineage and reproducibility for models. Use immutable artifacts and content-addressable storage.

• Record model inputs, preprocessing code, training data hash, and hyperparameters with each registered model.
• Use CI for model training like normal code: tests, unit tests for data transformations, canary deployments for model rollouts.

Canary & progressive rollout

Implement traffic shadowing and canary routing and measure both performance and business metrics (e.g., conversion uplift). If a new model consumes more GPU without a commensurate improvement, roll back automatically.

8. 2026 trends and what to plan for

Design with the near future in mind. Here are the trends shaping AI infra in 2026:

• DPUs / SmartNICs: offload networking and security; expect lower CPU overhead and denser packing.
• Composable GPUs: finer-grained GPU sharing and software-defined fabric make MIG-like sharing commonplace.
• Inference marketplaces: dynamic spot inference capacity and model marketplaces increase price pressure.
• Standardized telemetry: OpenTelemetry is universal; model-centric observability standards are maturing.
• Regulatory requirements: data residency and model explainability will be operational requirements for platform offerings.

9. Implementation checklist — apply Nebius patterns today

Use this checklist to convert the ideas above into an actionable roadmap.

1. Partition your clusters into specialized node pools (training, inference, low-cost batch).
2. Deploy a model registry and store artifacts immutably with provenance metadata.
3. Enable MIG/vGPU and configure device plugins for deterministic scheduling.
4. Implement mixed autoscaling (HPA + GPU-aware cluster autoscaler + spot pool manager).
5. Define SLIs and SLOs that include cost metrics and set alert thresholds on error budgets.
6. Integrate model observability for drift and data quality; run scheduled drift tests for production models.
7. Provide tenant isolation tiers: soft (namespaces), strong (runtime sandboxes), physical (dedicated clusters).
8. Establish a cost-engine for per-tenant billing and expose cost APIs to customers.

10. Example: Minimal Kubernetes resources for safe GPU inferencing

Use this starter manifest for a safe inference pod with MIG slice scheduling and pod priority.

apiVersion: v1
kind: Pod
metadata:
  name: safe-infer
  labels:
    app: safe-infer
spec:
  priorityClassName: high-priority
  nodeSelector:
    accelerator-type: h100-mig
  tolerations:
  - key: "gpu-inference"
    operator: "Equal"
    value: "true"
    effect: "NoSchedule"
  containers:
  - name: triton
    image: nvcr.io/nvidia/tritonserver:latest
    env:
    - name: MODEL_REPO
      value: "/models"
    resources:
      limits:
        nvidia.com/gpu: 1

Wrap-up: Why this architecture works

Reverse-engineering a Nebius-style stack shows recurring priorities in successful neocloud AI providers: strict separation of control and compute planes, hardware-aware scheduling, layered tenancy, SLO-first operations, and continuous cost pressure optimization. These patterns help teams scale AI offerings predictably while keeping costs and risks manageable.

Actionable takeaways

• Start small: add a specialized GPU node pool and a model registry before rearchitecting everything.
• Measure cost as an SLO: track cost-per-inference and add it to your error budget conversation.
• Automate remediation: tie autoscaling and failover to SLO breaches, not just alerts.
• Plan for 2026 hardware trends: evaluate DPUs and composable GPU offerings in procurement cycles.

Next steps (call-to-action)

Want a ready-to-deploy template for a Nebius-inspired AI infra? Get the 12-step Terraform + Kubernetes blueprint we use to launch a multi-tenant GPU cluster with cost SLOs and model registry integration. Download the blueprint, or book a 30-minute walkthrough with our platform engineers to adapt it to your environment.

Related Reading

• How AI Personal Trainers Are Changing Coaching: Ethics, Data, and Practical Use (2026)
• Best Executor Builds After the Nightreign Buff (Beginner to Speedrunner)
• 1031 Exchanges for Manufactured Homes and Small‑Scale Developers: What You Need to Know
• From Viral Singles to Tour Plans: How Artists Use Eight-Year Gaps to Stage Comebacks
• Guided Self-Compassion Practice for Fans Processing a Beloved Artist’s Troubling News