architectureinfrastrategy

Neocloud Architectures: How Companies Like Nebius Build Full-Stack AI Infrastructure

ssmart labs

2026-02-05

8 min read

Surveying 2026 neocloud approaches: what to build, what to outsource, and how Nebius-style platforms combine Rubin access, ClickHouse analytics, and managed MLOps.

Neocloud Architectures: How Companies Like Nebius Build Full-Stack AI Infrastructure (2026)

Hook: If your team still spends weeks assembling GPU fleets, chasing reproducibility, and stitching together siloed monitoring and model registries, you’re not alone. In 2026, engineering teams need reproducible, secure, and cost-efficient AI platforms that let developers ship models — not infrastructure.

The promise and the pain

Neoclouds — next-generation cloud providers that package compute, specialized hardware, data services, and developer tooling into opinionated stacks — aim to solve that pain. Companies like Nebius have become focal points because they deliver full-stack AI platforms: ephemeral dev labs, integrated MLOps pipelines, model registries, and access to the newest accelerators like NVIDIA’s Rubin lineup. But the real question for engineering leaders is: what should you build in-house, and what should you outsource to a neocloud?

Snapshot: 2026 trends shaping neocloud design

Accelerator scarcity and geography: access to Rubin-class GPUs is uneven — firms are renting compute across Southeast Asia and the Middle East to secure capacity. This affects latency, data residency, and cost.
Specialized data stacks: OLAP systems like ClickHouse are now mainstream for telemetry and experiment analytics after major funding and product maturity in 2025–26; teams are pairing ClickHouse with serverless ingestion patterns described in Serverless Data Mesh for Edge Microhubs.
Managed-first expectations: teams expect managed services (databases, object stores, model serving) but still demand customizability for core training pipelines.
Security and compliance automation: integrated policies, fine-grained IAM, and reproducible environment snapshots are table stakes — pair IAM best-practices with enterprise password hygiene frameworks like Password Hygiene at Scale.

How to choose: adopt, outsource, or build?

To balance speed, cost, and control, frame decisions with three lenses: core differentiator, risk & compliance, and operational overhead.

Adopt (use managed services)

Use managed services for components that do not provide sustained competitive advantage and that are expensive to run reliably.

Data storage and object stores: managed object storage and hosted ClickHouse or OLAP clusters minimize ops. ClickHouse Cloud or Nebius-managed ClickHouse are usually cheaper than DIY at scale.
Model serving and inference: managed LLM inference platforms are cost-effective for variable traffic. Outsource low-latency global inference to keep SLOs without buying global colo.
Authentication, auditing, and secrets: managed IAM, KMS, and SIEM integrations reduce compliance risk — integrate them with strong site-reliability practices; see Evolution of Site Reliability in 2026 for modern SRE responsibilities beyond uptime.
CI/CD and MLOps primitives: hosted pipelines, artifact registries, and model registries enable teams to move faster.

Outsource strategically (neocloud-managed but customizable)

Use a neocloud partner like Nebius where you need strong integration across layers but still want control.

Ephemeral developer labs: let the provider manage workspace orchestration, container images, and GPU quotas. You keep image definitions and policy as code.
Training clusters: use provider-managed GPU pools with configurable instance types, spot/commit plans, and priority scheduling for Rubins. Nebius-style neoclouds expose fine-grained controls while abstracting capacity management.
Experiment tracking and analytics: provided as managed services (ClickHouse for OLAP, integrated dashboards) with export hooks to your observability stack; pair that with a serverless ingestion plan from Serverless Data Mesh patterns for real-time telemetry.

Build in-house (when to keep custom hardware or software)

Keep components internal when they deliver clear differentiation or strict constraints justify the cost.

Proprietary model IP and specialized accelerators: if you run extremely large, private foundation models or need ultra-low-latency on-prem inference due to data residency, buy or colocate hardware.
Custom scheduler/runtime: if your workloads require novel scheduling policies (sparse training, huge model parallelism), invest in a tailored stack.
Regulatory data handling: for regulated datasets that cannot leave your network, you’ll need on-prem or private cloud builds with tight access controls.

Architectural patterns for full-stack AI (practical)

The following patterns reflect real deployments in 2026 and are actionable for teams evaluating neocloud providers.

1. Hybrid training pipeline (cloud + custom racks)

Use a hybrid approach: burst training to Nebius or Rubin-capable regions for scale; keep low-latency eval and sensitive datasets on-prem.

Control plane runs in managed neocloud: job orchestration, logging, model registry.
Data plane spans private S3-compatible storage and neocloud object storage with replication rules.
Use a reproducible environment system (container images + nixpkgs or reproducible build artifacts) so jobs are portable — pair this with offline-first sandboxes and component trialability approaches from Component Trialability in 2026.

2. Ephemeral dev-to-prod path

Implement ephemeral environments that mirror production but are cheap to spin up.

Developers launch ephemeral workspaces via Nebius self-service UI or API.
Workspaces mount read-only datasets and a small training quota in Rubin regions.
Push to staged managed pipelines that run larger experiments on pooled GPUs; support developer collaboration with edge-assisted live collaboration patterns for low-latency co-editing and iteration.

3. Observability-first experiment tracking

Ship all metrics to an OLAP store (ClickHouse) and use that as the single source for analytics, cost attribution, and drift detection.

INSERT INTO experiments.metrics (run_id, timestamp, step, loss, gpu_hours)
VALUES ('run-123', now(), 100, 0.12, 3.6);

This lets you run fast analytical queries for MLOps dashboards and retroactive audits without hitting primary storage.

Integration checklist: Nebius-style neocloud + your stack

The following checklist maps integration points and recommended controls.

Identity & Access: federate SSO, map groups to GPU quotas, require MFA for admin operations. Lock this into an org-wide credential hygiene program like Password Hygiene at Scale.
Network: private peering for sensitive datasets, TLS everywhere, egress filtering for model download policies.
CI/CD: enable pipeline triggers on model registry events; sign artifacts and enforce provenance.
Data governance: automated lineage, dataset versioning, and policy enforcement (masking, PII checks) at ingest — tie ingestion to a serverless data mesh where possible (see guide).
Billing & cost control: tag every run, enforce daily budgets, and turn on preemptible or committed discounts for Rubin pools.

Concrete code and orchestration examples

Below are compact examples to illustrate common integrations. Replace placeholders with your provider’s API keys and endpoints.

Terraform snippet: request a managed training job

resource 'neb_provider_job' 'train' {
  name = 'resnet-train'
  gpu_type = 'rubin-a100'   # hypothetical value
  gpu_count = 8
  image = 'registry.company.com/ml:2026-01'
  entrypoint = ['python','train.py']
  env = {
    STORAGE_BUCKET = 'gs://my-bucket'
  }
}

ClickHouse: schema for experiment telemetry

CREATE TABLE experiments.metrics (
  run_id String,
  ts DateTime,
  step UInt32,
  loss Float32,
  accuracy Float32,
  gpu_hours Float32
) ENGINE = MergeTree() ORDER BY (run_id, ts);

Cost, compliance, and supply-chain realities in 2026

Suppliers and compute availability now influence architecture decisions more than ever. Two trends matter:

Geopolitical and supply constraints: Rubin-class accelerators are prioritized to certain vendors and geographies. Many companies route training to specialized regions or rent capacity in third-party data centers — watch vendor financials and market movements like the OrionCloud IPO to understand supplier shifts.
DB and analytics consolidation: ClickHouse’s 2025–26 growth makes it a go-to for analytics — expect neocloud offerings to include managed ClickHouse or compatible OLAP backends.

Practical cost controls

Use committed or reserved capacity for predictable training loads.
Enable preemptible instances for experiments and short jobs.
Track GPU-hours per project in ClickHouse and enforce budgets programmatically.

Security & trust: reproducible, auditable pipelines

Trust is the differentiator for neoclouds. Nebius-like providers earn trust by combining:

Immutable environment artifacts: reproducible container images and signed manifests. Consider immutable artifact signing and offline attestation patterns discussed in broader site reliability guidance like Evolution of Site Reliability in 2026.
Provenance: model lineage, training dataset snapshots, and hashed inputs stored in an immutable ledger — for teams experimenting with ledger-backed provenance, reading practical security field guides on ledger handling is useful (see Practical Bitcoin Security for Cloud Teams for operational notes on ledger protection and key custody).
Fine-grained IAM: per-model and per-dataset ACLs with temporal grants for external contractors — integrate IAM with credential hygiene tooling (see best practices).

"If you can't reproduce a model, you don't own the model." — engineering principle for 2026 AI platforms

Case study: 'Acme Labs' adopts a Nebius-style neocloud

Acme Labs needed a reproducible environment to move from R&D to product. They evaluated three options: build their own Rubin-capable racks, use public cloud GPUs, or adopt a Nebius-like neocloud. They chose the neocloud model for these reasons:

Immediate access to Rubin accelerators without capital expense.
Managed ClickHouse for experiment analytics reduced ops headcount.
Native ephemeral dev workspaces improved developer productivity by 4x in their pilot.

Implementation highlights:

All training jobs were launched via the neocloud API, which enforced quotas and billing tags.
Experiment telemetry flowed into ClickHouse for retroactive analysis and cost attribution.
Sensitive datasets remained on-prem with data mirrored into isolated, encrypted buckets for specific training runs.

Outcome: time-to-model deployment dropped from months to weeks, with predictable costs and an auditable pipeline for compliance teams.

When not to trust a neocloud

Neoclouds are not a silver bullet. Consider building when:

Your models contain ultra-sensitive IP and cannot risk multi-tenant exposures.
You require experimental hardware or co-designed accelerators that vendors don’t offer.
You need on-site low-latency inference with strict data residency that neoclouds can’t support.

Checklist: readiness questions before choosing Nebius or similar

Do you have predictable workloads that benefit from reserved capacity?
Are you constrained by data residency or export-control rules?
Do you need fine-grained control of scheduler behavior for model-parallel training?
Does your team want to reduce ops overhead to focus on ML model innovation?

Actionable roadmap: 90-day plan to adopt a neocloud

Week 1–2: Audit workloads, identify IP and compliance constraints, rank training jobs by GPU-hours and latency sensitivity.
Week 3–4: Pilot a Nebius-managed training job, ship telemetry to ClickHouse, measure cost and mean time-to-train.
Week 5–8: Integrate IAM, set budgets, and enable ephemeral dev workspaces for one team.
Week 9–12: Migrate staging pipelines, validate provenance and reproducibility, and finalize go/no-go for additional teams.

Final recommendations

In 2026, the most effective approach is pragmatic hybridism: outsource commoditized layers to neoclouds like Nebius to get speed and scale, but retain ownership of the few layers that provide defensible advantage. Use managed ClickHouse for analytics, negotiate access to Rubin pools where needed, and enforce reproducible environments and policy-as-code across the stack.

Key takeaways:

Adopt managed services for storage, OLAP, and inference unless you have a business-critical reason not to.
Use neocloud-managed training pools for access to scarce accelerators like Rubin.
Keep IP-sensitive inference and custom accelerator topologies in-house.
Instrument everything: ClickHouse + telemetry = faster debugging, cost control, and compliance reporting.

Call to action

Ready to reduce time-to-model and experiment securely? Start with a 30-day Nebius-style pilot: deploy one training pipeline, stream telemetry into ClickHouse, and measure GPU-hours, cost, and reproducibility. If you want a starter checklist and Terraform templates tailored to your environment, contact our team for a hands-on review and pilot plan. For additional operational playbooks on auditability and edge decision planes, see Edge Auditability & Decision Planes.

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