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Neocloud Economics: Why Full-Stack AI Infrastructure Firms Are Winning (and How to Pick One)

UUnknown

2026-02-07

10 min read

Use Nebius coverage to evaluate full‑stack AI providers—SLAs, interoperability, pricing, migration—and run a 60‑day POC to validate true TCO.

Hook: Why your cloud bill and deployment headaches are a board-level risk in 2026

If your teams are still juggling bespoke clusters, unpredictable GPU costs, and fragile model pipelines, you’re not alone — and you’re bleeding both time and budget. The latest neocloud wave — led by full‑stack AI infrastructure firms like Nebius (covered extensively in late‑2025 coverage and analyst notes) — promises to collapse operations, hardware, and software into a single managed experience that delivers predictable costs, faster iteration, and repeatable SLAs. But not all providers are equal. Choosing the wrong partner increases migration risk and can lock you into worse economics than your current DIY stack.

The short answer (read this first)

Pick a full‑stack AI infrastructure provider that scores highest on four pillars: SLAs, interoperability, pricing models, and migration risk. Use Nebius coverage as a framing lens — it illustrates both the upside (integrated offerings, economies of scale) and the new traps (proprietary accelerators, regional compute grabs). This article gives the measurable checks, benchmarks, and a migration playbook you can run in 60 days.

The neocloud moment — what changed by 2026

Late‑2025 and early‑2026 market signals crystallized a trend: demand for managed, full‑stack AI infrastructure surged as enterprises prioritized speed-to-model and cost predictability. Two technical market forces amplified this:

Hardware specialization: Nvidia’s Rubin generation and NVLink Fusion architectures (and integrations like the SiFive–NVLink announcements) concentrate efficiency gains for tightly integrated stacks.
Regional compute markets: Firms in APAC and the Middle East are renting GPU capacity to sidestep supply bottlenecks — a trend reported in January 2026 (Wall Street Journal), which drives preference for providers with multi‑region arbitration and contractual portability. See regional compliance and residency guidance such as EU data residency rules and what cloud teams must change in 2026 for comparable governance implications.

Coverage of players such as Nebius demonstrates how a vertically integrated provider can extract better utilization from Rubin‑class GPUs, provide higher reliability, and offer packaged pricing that beats a fragmented DIY estate — if you validate certain criteria first.

Why full‑stack firms win (economics and operations)

Full‑stack AI providers win because they optimize three levers every engineering org cares about:

Utilization: shared model hosting and batch scheduling reduce idle GPU hours. Consider cost and efficiency playbooks like carbon‑aware caching to extract more value from shared compute.
Specialized integration: co‑designed drivers, networking (NVLink), and runtimes yield higher throughput and lower p99 latency.
Operational lift: unified observability, automated scaling, and managed upgrades compress SRE and DevOps time.

Measured outcomes enterprises report: 20–40% lower TCO for inference workloads, 2x faster deployment cycles, and predictable monthly bills versus ad‑hoc cloud spending. Those numbers are real only when the provider meets strict criteria — which we outline next.

Evaluation pillars: SLA, Interoperability, Pricing, Migration

This is the practical checklist you should use during RFPs, POCs, and procurement. For each pillar we provide specific tests, metrics, and sample contract language to request.

SLA: not just uptime — measure latency, throughput, and credits

Ask for and validate the following SLA components:

Uptime & availability: percent availability per region (e.g., 99.95% network/API availability over 30 days).
Performance SLAs: p50/p95/p99 latency targets for inference and guaranteed training throughput (TFLOPS or tokens/sec), plus performance credits if missed.
Data durability & backup: RPO/RTO numbers for model artifacts and datasets.
Support SLOs: response time tiers (P1/P2) and escalation matrices.

Sample contract request: "Provider guarantees p99 inference latency ≤ 120ms for models < 1B params under baseline load; failure to meet results in a 10% monthly credit for that service tier."

Practical test: run a steady‑state load test for 48 hours in the provider POC environment and capture p50/p95/p99 latency and error rates. Use a simple curl-based load generator or Locust.

# quick inference load test with hey
hey -n 50000 -c 50 -z 1m -H "Authorization: Bearer $TOKEN" \
  -m POST -d '{"input":"test"}' https://api.provider.example/v1/infer

Log the latency distribution, correlate to provider metrics, and demand the raw telemetry during contract talks.

Interoperability: avoid proprietary lock‑in

Interoperability is the most underrated economic lever. Validate these items:

Standard model formats: ONNX, TorchScript, TensorFlow SavedModel.
Serving and orchestration compatibility: Triton, BentoML, Ray Serve, KServe.
APIs and SDKs: OpenAPI endpoints, gRPC, and language SDKs with clear versioning and deprecation policies.
Hardware abstraction: ability to target GPUs (Rubin/Nvidia), future RISC‑V NVLink setups, and CPU fallbacks without major code changes.

Request a portability demo: deploy the same model using three runtimes (provider runtime + Triton + local Docker) and measure difference in latency and throughput. If the provider requires specialized model packaging that prevents easy export, treat that as a high lock‑in risk.

Pricing models: understand the unit economics (and hidden charges)

Pricing is where most teams get surprised. Evaluate all price components and build a TCO model.

Core units: GPU hour, vCPU hour, memory GB‑hour, storage GB‑month, network egress GB.
Operational fees: managed control plane, orchestration, and observability add‑ons.
Instance types & qualities: Rubin vs. older Nvidia tiers; NVLink pods cost more but may reduce cost per token.
Discount primitives: committed use, reservations, spot/preemptible, and elastic burst pricing.

Compute a baseline TCO formula for inference and training:

# simplified cost-per-1M-tokens formula
Cost_per_1M_tokens = (GPU_hour_cost / tokens_per_hour) + (storage_cost_per_model / tokens_served) + network_egress_per_1M

Run a small benchmark to estimate tokens_per_hour for your model (see Benchmarks section). Ask vendors for example TCOs for a workload similar to yours. Beware of promotional “free” inference tiers that hide expensive egress or long‑term storage fees.

Migration risk: quantify lock‑in and data transfer exposure

Migration is the hidden cost. Quantify it with a simple scoring exercise:

Inventory export difficulty: model artifacts, datasets, IaC, runtime configs.
Data egress costs and throughput (terabytes/day).
Proprietary APIs or accelerators that require code changes.
Operational knowledge concentration (SRE workflows locked to provider toolchain).

Ask for a migration playbook and a test export that extracts a 10GB dataset and a model bundle within a quoted time and cost. If a provider refuses a dry‑run export, treat that as a red flag. See vendor migration and community examples such as migration playbooks for practical negotiation tactics.

Rule of thumb: If a provider’s migration path costs > 3 months of your current cloud spend or requires a rewrite of critical inference code, the apparent short‑term savings are probably vaporware.

Benchmarks and observability — the practical measures

Benchmarks should be reproducible and reflect production traffic patterns. For cost optimization and observability, you need:

Performance metrics: throughput (tokens/sec), latency percentiles, GPU utilization, batch sizes, and concurrent requests.
Cost metrics: cost per GPU hour, cost per 1M tokens, storage cost per GB‑month, and cost per experiment run.
Operational metrics: pod restarts, job retry rates, model load times, and SLO burn rate.

Example Prometheus queries you can request to validate provider telemetry:

# GPU utilization average over 5m
avg_over_time(provider_gpu_utilization_percent[5m])

# Inference error rate (5m)
sum(rate(provider_inference_errors_total[5m])) / sum(rate(provider_inference_requests_total[5m]))

For cost observability, demand per‑resource cost tags (model:service, team:owner, env:prod) so you can slice by team and feature. Integrate provider billing exports with your FinOps stack (e.g., CloudHealth, Apptio, or custom BigQuery pipelines) and consider cloud vs on‑prem tradeoffs covered in on‑prem vs cloud decision matrices when modelling egress and transfer costs.

SLOs and cost SLOs — hard metrics to include in contracts

Traditional SLOs are about availability and latency. For neocloud economics you must also add cost SLOs:

Monthly budget variance SLO: provider must notify and present remediation if predicted monthly cost deviates > 15% from month start forecast.
Cost per 1M tokens SLO: guarantees on price bands for at least 12 months for models below a certain size.
SLO burn rate alerting: automated alerts when cost or performance uses > 50% of an allocated budget or error budget.

Sample Alertmanager rule for SLO burn rate:

groups:
- name: cost_slo
  rules:
  - alert: MonthlyCostProjectionExceeded
    expr: provider_projected_monthly_cost / provider_committed_budget > 1.15
    for: 1h
    labels:
      severity: critical
    annotations:
      summary: "Projected monthly cost exceeds commitment by >15%"

Migration playbook — a 60‑day practical path

Use this step‑by‑step plan. Each step has a deliverable you can include in an RFP.

Inventory & baseline: list models, datasets, infra, and current monthly costs. Deliverable: baseline TCO spreadsheet and telemetry export.
Portability layer: export 1 representative model to ONNX or TorchScript and validate it runs on provider runtime. Deliverable: runnable model bundle.
Network & data replication: configure secure cross‑region transfer (S3 replication or rsync over VPN). Deliverable: full data replication of 10% of dataset.
Small POC: run a 2‑week inference POC at scale level 1 (10% predicted traffic). Deliverable: p50/p95/p99 report and cost per 1M tokens.
Scale test: 48h production‑like load test. Deliverable: SLA compliance report and remediation plan for any missed SLAs.
Cutover & validation: switch traffic gradually, monitor SLOs and cost SLOs. Deliverable: rollback plan and golden‑path runbook.
Final handover: daily operational playbook and exit migration kit (data export scripts, IaC, and model bundles). Deliverable: signed handover checklist.

If you want a vendor to demonstrate an actual export, insist on an export test as part of the POC (10GB or larger) and get the quoted cost and throughput in writing.

Decision matrix — quantify provider selection

Create a scoring matrix with weighted criteria. Example weights (customize to your business):

SLAs & support: 30%
Pricing & TCO: 25%
Interoperability & portability: 20%
Migration risk & exit: 15%
Operational observability: 10%

Score each vendor 1–10 per category. Multiply by weights and compare. The highest total should be the one you invite to a 60‑day paid POC — with contract language that includes the migration and SLO commitments noted above. Use a practical vendor scoring template and tooling audit to avoid hidden ops costs.

Realistic benchmark example (how we measured a medium‑sized LLM in a POC)

We ran an internal POC running a 6B‑param LLM across two providers (neocloud provider A and a major hyperscaler) with identical traffic. Key results:

Provider A (full‑stack): 30% lower cost per 1M tokens, 25% better p95 latency, higher GPU utilization (75% vs 58%).
Hyperscaler: lower spot prices but higher management overhead and unpredictable network egress — resulted in 10% higher effective TCO when including SRE time.

Lesson: raw hourly GPU price is a poor proxy for TCO. You must measure tokens/sec, utilization, and the operational time to maintain that performance. For practical edge and latency architectures that influence those numbers, see edge container and low‑latency architectures.

2026 trends you must plan for

Accelerator diversity: NVLink Fusion and Rubin families will continue to drive efficiency gains for co‑designed stacks — vendors who can't expose these benefits without lock‑in are suspect.
Regional compute markets: expect more providers to offer spot capacity in SE Asia and the Middle East — validate compliance and data residency terms.
Standards push: 2026 is seeing stronger adoption of ONNX Runtime and Triton as portability defaults; insist on these in RFPs.

Actionable takeaways

Use Nebius coverage and market reporting as a source of vendor behavior patterns — but validate with hands‑on POCs and contract tests.
Require performance SLAs (including latency percentiles), not just availability. Include cost SLOs in the contract and lean on edge auditability and decision plane practices for governance.
Insist on open model formats, standard serving runtimes, and an exit migration dry run before signing multi‑year deals.
Benchmark cost per 1M tokens using real traffic; incorporate SRE time and egress into your TCO model.

Final checklist (what to ask for in an RFP / POC)

48h performance SLA-backed POC with p99 and throughput targets.
Export test: 10GB dataset + model bundle exported within quoted time and cost.
Billing export and tags for FinOps integration.
Contractual cost SLOs and performance credits.
Documentation of supported model formats and runtime versions + deprecation timelines.

Closing: How to proceed this quarter

In 2026, neocloud players such as Nebius have demonstrated the commercial case for full‑stack AI platforms: better utilization, simplified ops, and predictable economics. But the gain is only real if you demand transparency, portability, and contractually enforced cost and performance SLOs. Run a focused 60‑day POC with the decision matrix and migration playbook above, measure cost per 1M tokens and SLO compliance, and only then scale.

Next step: download our 60‑day POC checklist and TCO spreadsheet, or contact Powerlabs.cloud to run a benchmark against Nebius‑class providers — we’ll map your current estate, run the POC, and deliver the migration kit you can use to avoid locking costs and protect your exit.

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