migrationcloudinfrastructure

Migration Checklist: Moving On-Prem AI Workloads to Neocloud Full-Stack Providers

UUnknown

2026-02-17

9 min read

A practical, technical migration checklist for moving on-prem AI workloads to neocloud providers—networking, data egress, model registry and compliance.

Hook: Why your next migration must treat networking, egress and the model registry as first-class citizens

Enterprises moving AI workloads from on-prem to a neocloud full-stack provider (examples: Nebius-style platforms) in 2026 face three predictable failure modes: unexpected networking friction, runaway data egress costs, and broken model traceability. If your migration plan treats those as afterthoughts, you’ll hit prolonged downtime, compliance blockers and a surprise cloud bill. This checklist gives a technical, step-by-step migration plan tailored for senior engineers, platform teams and IT leads who need to get production AI running fast and safely.

Executive summary: What you must accomplish before cutover

Most successful migrations in late 2025–early 2026 converged on a short set of priorities. Start here and expand into the detailed checklist below:

Network topology and private connectivity—establish private links, peering or ExpressRoute equivalents before moving data.
Data egress strategy—measure, model and cap egress; prefer delta and compression; colocate storage when possible.
Model registry and reproducibility—portable artifacts, metadata and CI/CD hooks from day one.
Compliance and data residency—contractually validate provider certifications (SOC2, ISO27001, FedRAMP where applicable) and implement BYOK/KMS.
Ops: observability, canary rollouts, rollback plans—build automated SLO checks and drift detection before traffic cutover.

1. Pre-migration assessment (2–4 weeks)

Inventory & classification

Map every model, dataset and pipeline you plan to move. For each asset capture:

Model name, version, framework, size (GB), GPU requirement
Dataset sensitivity, residency needs, throughput (MB/s) and access patterns
Inference QPS, latency targets, and peak vs baseline traffic
Upstream/downstream dependencies (feature stores, message queues)

Prioritize assets for migration in phases: bulk-batch first (non-latency-critical), then latency-sensitive APIs, then internal tooling.

SLA & cost targets

Define measurable goals: 99.9% availability, P95 latency, and monthly egress budget. Model egress using real traffic traces — 2026 providers usually offer egress-aware pricing calculators; use them to simulate monthly spend with your expected QPS.

2. Networking: private, high-throughput connectivity

Networks are migration make-or-break. In 2026, neocloud providers standardized on private peering, direct connect and carrier-grade interconnects. Your checklist:

Design the topology

Choose private peering / direct connect over public endpoints for production model traffic to reduce latency and egress charges.
Segment networks by function: management, training, inference, and telemetry.
Use VLANs and subnets with strict security groups for ingress/egress control.

Practical steps

Request a provider-side private link (private endpoint) and validate BGP prefixes and MTU.
Run latency and path MTU tests: iperf3 and tracepath between on-prem and provider POPs.
Ensure DNS and cert provisioning automation (ACME/private CA) supports private zones.

Example: simple Terraform to request a private peering (pseudocode)

# provider-specific resource names will vary — adapt for Nebius-style API
resource "neocloud_private_connection" "peering" {
  name       = "corp-to-neocloud-peering"
  project_id = var.project_id
  location   = var.location
  bandwidth  = "10Gbps"
  interface  = "etl-vlan"
}

output "peering_endpoint" {
  value = neocloud_private_connection.peering.endpoint
}

3. Data egress: measure, reduce, and cap costs

Data egress is a major cost lever when migrating from on-prem to cloud. In 2026, providers and finance teams are focused on predictable billing: you must do the same.

Audit and model egress

Capture historical outbound bytes per pipeline over 90 days.
Classify traffic: model downloads, dataset transfers, batch outputs, telemetry.
Simulate cost scenarios using provider egress tiers and expected growth.

Techniques to reduce egress

Co-locate data and models: store large datasets in provider object stores in the same region as compute.
Delta sync: send only diffs for model updates and feature exports.
Compression and serialization: use efficient formats (Parquet, Apache Arrow) and compressed checkpoints.
Inference near data: move inference to the cloud provider and send only aggregated results back on-prem.
Cache inference: use Redis/edge caches for repeated queries to cut redundant outbound bytes.
Egress caps: request billing alerts and hard caps for initial months to protect budgets.

Operational guardrails

Set daily egress thresholds and automated throttles at the network edge.
Track per-model egress in your model registry metadata.
Implement chargeback tags so teams see their monthly egress contribution.

4. Model registry: portability, metadata and CI/CD

A robust model registry preserves reproducibility across on-prem and neocloud. In 2026, registries are expected to be cloud-agnostic and metadata-rich.

Registry requirements

Artifact storage (containerized model images or tarballs) with immutable versioning
Provenance metadata: training dataset hashes, code commit, hyperparameters, and evaluation metrics
Signatures and SBOM for model artifacts
APIs for CI/CD to promote models across environments

Tooling choices

Use industry standards and proven tools: MLflow, BentoML, TFX or provider-native registries with open APIs. Export model artifacts from on-prem registry into the neocloud registry as part of a reproducible pipeline.

Example: push a model artifact to a registry (bash/MLflow)

# package and push model to MLflow-tracking / registry
mlflow models build -m /artifacts/my_model -n my_model:1 --flavor pytorch
mlflow artifacts upload -r neocloud://registry/project/models/my_model/1 \
  --src ./dist/my_model_v1.tar.gz

5. Compliance & security: encryption, access and audit trails

Compliance remains a top migration blocker. In 2026, expect stricter enforcement of data residency for regulated industries and more providers offering FedRAMP/IL4-like options. The checklist below helps you close gaps.

Contract and certification checklist

Confirm provider certifications (SOC2, ISO27001). For government workloads, confirm FedRAMP or equivalent.
Review data residency and subprocessor lists.
Verify SLAs for incident response and access to audit logs.

Technical controls

BYOK / Customer-managed KMS—ensure keys are under your control where required.
Encrypt data at rest and in transit (TLS 1.3 and modern ciphers).
Use zero-trust network controls and least-privilege IAM for service accounts.
Enable object-level immutability (WORM) for provenance-critical artifacts.

Auditing and evidence

Export provider logs into your SIEM (Syslog/S3 integration) and retain per regulatory retention policies.
Maintain reproducible audit trails from dataset snapshot to deployed model (store hashes and SBOMs in registry).

6. Deployment patterns: hosting, scaling and latency

Decide hosting patterns per workload — there’s no single best approach. Evaluate these patterns against cost and latency targets.

Patterns

Serverful GPU clusters for large models and training.
Serverless inference for bursty, stateless APIs (pay-for-use).
Stateful model pods with local NVMe caches for large, fast models (useful for LLMs).
Edge/nearby inference for ultra-low latency — use the provider’s edge POPs when available.

Autoscaling & cost controls

Use adaptive batching and dynamic concurrency for GPU endpoints to improve throughput and reduce cost.
Prefer heterogeneous autoscaling: baseline small instances + burstable accelerators (spot/ephemeral) for noncritical load.
Reserve capacity for steady-state high-QPS endpoints and use spot instances for batch workloads.

Kubernetes example: GPU node selector (snippet)

apiVersion: apps/v1
kind: Deployment
metadata:
  name: inference-deployment
spec:
  replicas: 2
  template:
    spec:
      nodeSelector:
        accelerator: nvidia-tesla-a100
      containers:
      - name: model-server
        image: registry.neocloud/my-model:1.0
        resources:
          limits:
            nvidia.com/gpu: 1

7. Observability, testing, and rollout

You cannot migrate models without testing drift, observability and safe rollouts.

Testing

Run synthetic and shadow traffic to validate latency and correctness before switching production traffic.
Implement end-to-end golden tests: fixed inputs with stable expected outputs to catch infra regressions.

Rollout strategy

Blue-green or canary releases for API endpoints.
Stepwise traffic ramp with SLO gates: do not exceed error budget or P95 thresholds during ramp.
Automated rollback triggers based on anomaly detection (error rate, latency, output drift metrics).

Observability stack

Telemetry: Prometheus + OpenTelemetry traces and metrics.
Model observability: capture input features, prediction distributions, confidence metrics and label feedback loops.
Set up automated drift alerts and periodic model re-evaluation pipelines.

8. Cost optimization playbook (post-migration)

After cutover, do iterative cost optimization. Priorities in 2026 include quantization-aware deployment, caching, and intelligent instance selection.

Quick wins

Quantize models (FP16/INT8) when accuracy tolerances allow — can cut GPU cost 2–4x.
Batching & concurrency — increase GPU utilization with dynamic batching.
Inference caching — cache high-hit responses at the edge.
Use provider spot/preemptible GPUs for batch retraining and hyperparameter tuning.

Measure & iterate

Track cost-per-inference and model-level egress monthly.
Set team-level budgets and automated alerts on surges.

9. Migration runbook & rollback

Create a concise runbook with owner, prechecks, cutover steps and rollback triggers. Example checklist:

Prechecks: network established, registry artifacts validated, tests green.
Start data sync: initial full dataset transfer (off-peak), monitor throughput and egress.
Deploy model in shadow mode — capture telemetry for a minimum burn-in period.
Cut 10% traffic -> 50% -> 100% with SLO gates at each step.
Rollback plan: DNS TTL-based switchback and provider API to route traffic back to on-prem endpoints.

10. People and governance

Operationalize the migration with clear roles: Cloud Network Owner, Model Owner, Security/Audit Owner and Finance Owner. Include runbooks, knowledge transfer sessions, and permanent playbooks for incident response.

Compact migration checklist (copyable)

Inventory models/datasets + categorize by sensitivity and latency
Establish private peering and validate BGP/MTU
Estimate egress, set caps and alerts
Export and import model artifacts into cloud-native registry with full metadata
Implement BYOK/KMS and enable audit log export
Shadow deploy models and run golden tests
Canary rollout with automated SLO gates
Optimize (quantize, batch, cache) and monitor cost-per-inference
Document rollback procedures and ownership

Trends & expectations for 2026 (what to watch)

Neocloud specialization: providers like Nebius are offering vertically-optimized stacks (financial, healthcare) with continuous compliance attestation.
Provider-neutral registries: industry momentum toward standardized model metadata and signed artifacts has increased portability.
Edge + cloud hybrid inference: low-latency applications will adopt split-inference and model distillation to reduce egress.
More predictable egress models: some providers now offer committed egress pools and pass-through pricing to reduce bill shock.

Treat egress and the model registry as operationally critical—skipping them costs you weeks, if not months.

Final checklist: migration KPIs to validate within 30 days

All production models have registry entries and reproducible build IDs
Network latency within SLO for 95% of requests
Egress spending within 10% of forecasted budget
Automated monitoring captures drift and triggers re-training pipelines
Compliance evidence uploaded to internal GRC and provider attestation available

Call to action

If you’re planning an on-prem to neocloud migration, start with a short technical discovery sprint: 2 weeks to inventory and a pilot to validate private peering, egress modeling and a model registry push. Our team at aicode.cloud helps engineering teams build these pilot plans and run targeted drills that eliminate the three biggest risks—networking, egress and model traceability—before full cutover. Contact us to get a tailored migration sprint and a reproducible runbook for your Nebius-style provider migration.

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