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Vendor Lock-In Risk Assessment: Choosing Between Neocloud Providers and On-prem AI Infrastructure

wworkflowapp

2026-01-31

10 min read

A 2026 framework for assessing vendor lock‑in: compare Nebius‑style neoclouds and on‑prem AI with procurement questions, exit costs, and migration playbooks.

Stop Guessing: A Practical Framework to Measure Vendor Lock‑In When Choosing Neocloud AI vs On‑Prem

Hook: If your team's productivity drains into context switching, manual integrations, and constant firefighting every time a provider changes pricing or deprecates an API, you’re feeling vendor lock‑in. In 2026, with neocloud vendors such as Nebius offering full‑stack AI platforms and on‑prem AI stacks maturing rapidly, procurement teams must move from binary debates (“cloud or on‑prem”) to a structured risk, cost, and migration assessment that produces an executable exit plan.

Executive summary — what you need to know first

Most organizations evaluating AI infrastructure in 2026 face three interlinked risks: data gravity (your data keeps you tethered to a provider), proprietary workflows (vendor‑specific APIs and model formats), and hidden exit costs (egress, rework, and staff time). Neocloud vendors like Nebius deliver speed of adoption, managed scaling, and optimized Nvidia Rubin/accelerator access—advantages amplified in late 2025 and early 2026 by constrained GPU supply chains. But those advantages can amplify lock‑in unless procurement and engineering teams insist on portability primitives, escrow agreements, and migration playbooks.

2026 trends shaping the decision

Accelerator access and regional compute scarcity: Global competition for Rubin‑class GPUs and private silicon continues to concentrate capacity with neocloud providers who secure bulk agreements and regional capacity. (See practical hardware benchmarking and accelerator notes in community field tests like Benchmarking the AI HAT+ 2 for a sense of diverse hardware tradeoffs.)
Regulatory pressure: The EU AI Act enforcement and tightened data residency rules (post‑2024) have forced vendors to offer stronger data sovereignty options—critical when evaluating multi‑tenant neoclouds vs dedicated on‑prem instances.
Standards & portability: ONNX, TorchScript, and model registry standards matured in 2025, but many vendors still add value through proprietary wrappers—buyers must verify true portability.
Hybrid-first deployments: In 2026, best practice is hybrid: run sensitive workloads on‑prem or in dedicated tenancy while using neocloud for burst capacity and model fine‑tuning.

Framework Overview: Evaluate along four axes

Use this practical framework as your procurement checklist. Score each vendor (neocloud or on‑prem) on these axes: Portability & Exit Risk, Security & Compliance, Operational TCO, and Scaling & Performance. Weight each axis by organizational priorities (e.g., compliance‑heavy orgs weight Security higher).

1) Portability & Exit Risk

Data export formats: Does the vendor provide raw data backups (S3, Parquet) and model exports in open formats (ONNX/TorchScript/TF SavedModel)?
Workflow portability: Can you export pipelines (CI/CD, preprocessing, feature stores) as Terraform/Argo workflows or are they locked into managed orchestration?
APIs and SDKs: Prefer vendors that support open standards (OpenTelemetry, OCI artifacts) and well‑documented REST/gRPC interfaces without proprietary gates.

2) Security & Compliance (multi‑tenant, SSO, backups)

Isolation options: multi‑tenant isolation, dedicated hardware or VPC‑isolated tenancy, private endpoints, and physical host tenancy.
Identity integration: SAML/SSO, OIDC, and SCIM for provisioning; does the vendor support BYO IdP and enforce MFA and conditional access? For operational playbooks around edge identity signals see Edge Identity Signals: Operational Playbook.
Key management: BYOK/KMS integration and HSM support to maintain control over encryption keys.
Certifications: SOC2, ISO27001, FedRAMP, HIPAA—map required certifications to your compliance regime.
Backups & RTO/RPO: Automated backups, immutable snapshots, retention, and restore time guarantees.

3) Operational TCO

Base costs vs burst pricing: Compare committed vs on‑demand vs spot pricing and hidden charges (monitoring, logging egress).
Staffing: Engineering hours to integrate, maintain, and extract vs vendor managed ops. Account for training & onboarding time.
Exit costs: Data egress charges, compute to re‑train or convert models, re‑engineering connectors, and potential downtime.

4) Scaling & Performance

Horizontal scaling (multi‑tenant throughput) and vertical scaling (access to latest accelerators like Rubin variants).
Network locality: Options for colocated storage & compute to reduce latency.
Service SLAs: Uptime, throughput, and predictable performance guarantees for production batches.

Procurement questionnaire — ask these 25+ practical questions

Embed this into RFPs. Use binary, descriptive, and time‑boxed answers where appropriate.

Who owns customer data? Are there any rights the vendor retains over hosted models or derived data?
Provide the exact export formats for datasets and models. Can you export models as ONNX/TorchScript/TensorFlow SavedModel?
What is the maximum monthly data egress throughput and the pricing model (per GB, tiered, or negotiated)?
Do you provide a written, tested exit plan and migration assistance? Include time estimates and cost model.
What encryption is used in transit and at rest? Support for BYOK/HSM?
Which identity providers are supported (SAML, OIDC)? Is SCIM supported for provisioning?
How do you isolate tenant workloads in a multi‑tenant environment? Describe noisy neighbor protections.
What is your audit log retention period and format (W3C, JSON)? Can logs be streamed to our SIEM?
List certifications and third‑party penetration tests in the last 12 months.
What is your documented RTO and RPO for production environments?
Is hardware dedicated (single‑tenant) or shared? Any option for bare‑metal tenancy?
Describe how feature stores and pipelines can be exported and rehydrated on‑prem.
Provide sample SLA credits and real incident timelines from 2025–2026.
Do you support private networking (VPC peering, PrivateLink) and on‑prem connectors? If networking observability is a concern, consider practices in Proxy Management Tools for Small Teams.
What are the supported model versioning and registry formats; can we transfer registry metadata programmatically?
Do you offer escrow for model weights and platform code? Under what conditions is escrow released?
How do you handle dependency management for runtime environments (CUDA, cuDNN, Python packages)?
Detail your pricing for GPU bursts and preemptible instances—how is job interruption handled?
Are there any non‑standard terms that limit our ability to self‑host or migrate?
Is there an SLA for onboarding and migration assistance?
Provide a sample contract clause for portability and exit costs.
How are secrets and credentials managed and rotated in the platform?
Are there any vendor‑provided SDKs that are required for production that lock workflows?
What telemetry is collected and is it anonymized or PII filtered by default?
How do you support compliance audits and evidence collection for my auditors?

Quantifying exit costs: categories and sample calculation model

Exit costs are often underestimated. Break them into direct and indirect categories:

Direct costs: Data egress fees, vendor migration service charges, escrow fees.
Indirect costs: Engineering time to rework connectors, QA and validation, lost productivity during cutover, retraining models on new hardware.

Sample quick model (replace with real numbers):

# Example exit cost estimation
data_size_gb = 50_000  # 50 TB
egress_per_gb = 0.12   # $0.12/GB
egress_cost = data_size_gb * egress_per_gb  # $6,000

# Rework and revalidation
eng_hours = 400
avg_hourly_rate = 120
eng_cost = eng_hours * avg_hourly_rate  # $48,000

# Re-training compute
retrain_hours = 2000
gpu_hourly = 3.5
retrain_cost = retrain_hours * gpu_hourly  # $7,000

total_exit = egress_cost + eng_cost + retrain_cost
print(total_exit)  # ~$61,000

Adjust for higher egress (regional pricing), longer QA cycles, or legal costs. For enterprise scale, exit costs can be in the hundreds of thousands to millions.

Migration playbook — step‑by‑step runbook (technical)

This playbook assumes you’re moving from a neocloud provider to on‑prem or another vendor. Keep each step as an automated job in your CI/CD and follow an incremental migration strategy.

Phase 0 — Preparation & discovery (2–4 weeks)

Inventory: catalog datasets, model registries, pipelines, connectors, infra templates, and external integrations.
Risk scoring: prioritize assets by business criticality, sensitivity, and coupling to vendor APIs.
Export dry‑run: request sample exports and test restores in a sandbox to validate formats.

Phase 1 — Data and model extraction (1–6 weeks)

Export datasets in open formats (Parquet/CSV) to a staging S3 bucket or direct transfer to your on‑prem object store. Confirm checksums.
Export models in ONNX/TorchScript/SavedModel. If vendor only provides proprietary format, request a conversion tool or run inference‑based extraction tests.
Sample generic export API call:

curl -X POST https://api.neocloud.example/v1/exports \
  -H "Authorization: Bearer $TOKEN" \
  -d '{"resource":"model:prod/my-model","format":"onnx","destination":"s3://mycompany-migration"}'

Phase 2 — Rebuild pipelines & runtimes (2–8 weeks)

Containerize runtimes with explicit dependency versions. Example Dockerfile for PyTorch inference:

FROM pytorch/pytorch:2.2-cuda12
COPY ./model /opt/model
COPY requirements.txt /opt/requirements.txt
RUN pip install -r /opt/requirements.txt
CMD ["python", "/opt/inference_server.py"]

Lift CI pipelines: create Argo/Concourse pipelines that run model validation, perf tests, and canary rollouts.
Feature store migration: export feature snapshots and rebuild or adapt to Feast/your chosen feature store. Consider supply‑chain risk and red‑teaming guidance from case studies like Red Teaming Supervised Pipelines.

Phase 3 — Networking, Security, and Integration (1–4 weeks)

Establish private connectivity (VPN, VPC peering) and enforce firewall rules.
Integrate SSO/SCIM for user provisioning and rotate keys to your KMS.
Replay audit logs and ensure parity in retention and format.

Phase 4 — Testing & Cutover (2–6 weeks)

Validation: functional parity, performance, and security scans. Run model drift checks and compare metrics (latency, throughput, AUC).
Staged cutover: move noncritical traffic first, then progressively shift critical traffic with canary percentages.
Rollback plan: rapid switch to neocloud via DNS or traffic routing if thresholds are exceeded.

Phase 5 — Post‑migration (ongoing)

Retain a frozen environment on the old vendor for 30–90 days for audit and quick rollback.
Complete cost reconciliation and update TCO model with real run rates.
Document lessons learned into a migration playbook to reduce future exit costs.

Sample contract clauses & negotiation levers

Insist on the following clauses or equivalent:

Data Portability Clause: Vendor must provide bulk exports (data+models+metadata) in open formats within 30 days of request at no additional charge.
Exit Assistance: Define hours of migration support (e.g., 200 hours) and a fixed price or discounted rate for additional hours.
Escrow for Critical Artifacts: Weights and platform code held by a neutral escrow agent, releasable on insolvency or contract breach.
Cap on Egress Fees: Maximum egress per GB or capped monthly egress charges during an exit window.
Audit Rights: Right to perform annual security and portability audits with reasonable lead time. See broader IT consolidation and contract playbooks in IT playbook: consolidating martech.

When on‑prem is the right choice in 2026

Choose on‑prem when: you need strict data residency and low‑latency inference, regulatory regimes demand complete control, or your model training requires persistent access to proprietary accelerators you can cost‑effectively maintain. Hybrid models are most common: sensitive inference on‑prem, burst training in neocloud during peak demand, with robust portability guarantees.

When neocloud (e.g., Nebius) is the right choice

Neoclouds shine when speed matters—rapid prototyping, access to the latest Rubin‑class accelerators, and reduced ops overhead. In late 2025 and early 2026, many vendors also offer advanced managed MLOps features (automatic model tuning, managed feature stores). But only proceed if the vendor commits to clear export paths, BYOK, and a defined exit playbook.

Checklist: Quick red flags that indicate high lock‑in risk

No documented export format for models or only proprietary encrypted weights.
Proprietary SDKs required for core workflows with no equivalent open API. If you’re evaluating workflow automation, compare independent reviews like PRTech Platform X — workflow automation review.
High, unlimited egress fees or only manual export windows.
Absence of BYOK/KMS or refusal to allow tenant key control.
No contractual exit assistance or escrow options.

Actionable takeaways — what to do this quarter

Run the vendor questionnaire with any neocloud vendors under consideration; score and weight answers.
Budget for an early portability audit and a sandbox export test as a nonnegotiable procurement milestone.
Require an exit assistance clause and capped egress fees before signing multi‑year commitments.
Adopt containerized runtimes, standard model formats, and IaC for pipelines to reduce rework later.
Plan a hybrid architecture that isolates sensitive workloads but leverages neocloud for burst compute—document the cutover and rollback steps now.

Closing: make lock‑in a managed business decision, not a surprise

Vendor lock‑in is not binary. In 2026, with constrained accelerator supply and powerful neocloud offerings like Nebius, you can achieve both speed and control—if you demand portability, escrow, and a tested migration playbook during procurement. Treat exit readiness as a first‑class procurement requirement; do a dry‑run export in the sandbox, quantify exit costs into your TCO, and keep the migration runbook runnable and automated. Doing so converts vendor lock‑in from a risk into a predictable business decision.

“Buy fast, migrate safely.” — Practical procurement maxim for AI infrastructure in 2026.

Call to action

Ready to evaluate Nebius or another neocloud with a defensible exit strategy? Download our free procurement RFP template, exit cost calculator, and automated migration playbook (includes scripts and Terraform examples) to run a portability dry‑run in your environment. Schedule a 30‑minute consultation with our engineers to assess your current stack and get a prioritized migration cost estimate.

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