Migrating AI from Cloud to On-Premise
Typical situation: you trained a model on SageMaker, but the customer requires deployment on their own premises due to GDPR GDPR or corporate policy. Or GPU rental in the cloud hits the budget for 24/7 inference. We help move AI solutions from cloud platforms (AWS, GCP, Azure) to your own servers without performance loss. We handle the entire process from audit to deployment with a guaranteed result.
Let's break down when a transition is justified economically and performance-wise.
When on-premise is cheaper than the cloud
Renting 8x A100 80GB on AWS (p4d.24xlarge) costs ~$32/hour or ~$280,000/year at 100% utilization. Owning a DGX A100 80GB server costs ~$200,000 plus ~$20,000/year operational expenses. At >60% utilization, the server pays for itself in 18-24 months. Savings can reach 40% under high load. In one project, we migrated a recommendation engine for e-commerce from AWS to on-premise. Load was 10K RPS, latency dropped from 50ms to 12ms — a 4x improvement. Annual savings were $150,000.
Why on-premise is more secure than the cloud
On-premise gives you full control over data and infrastructure. Cloud providers can change terms or pricing, while your own servers remain under your management. Moreover, when working with sensitive data (personal data, medical information), on-premise simplifies regulatory compliance. You define access policies and encryption yourself.
On-premise ML platform architecture
On-Premise Infrastructure: ├── GPU Cluster (training) │ ├── Training nodes: 4x DGX A100 (32 GPU) │ └── InfiniBand network 200Gbps ├── Inference Cluster (inference) │ ├── Inference nodes: 4x A100/H100 │ └── 100GbE network ├── Storage │ ├── NVMe SSD (hot data): 200TB │ ├── HDD NAS (warm data): 2PB │ └── Tape (cold archive) ├── Platform (Kubernetes) │ ├── NVIDIA GPU Operator │ ├── Kubeflow Pipelines │ └── MLflow Tracking Server └── Networking ├── Load Balancer (HAProxy/MetalLB) └── Service Mesh (Istio) Replacing cloud-managed services
| Cloud Service | On-Premise Alternative |
|---|---|
| S3 | MinIO (S3-compatible) |
| SageMaker | Kubeflow + MLflow |
| RDS | PostgreSQL on bare metal |
| ElastiCache | Redis cluster |
| CloudWatch | Prometheus + Grafana |
| ECR | Harbor (container registry) |
| Secrets Manager | HashiCorp Vault |
| Lambda | Knative / OpenFaaS |
MinIO as an S3 replacement: No code changes needed — MinIO is fully S3-compatible. Example:
import boto3 s3 = boto3.client( 's3', endpoint_url='https://minio.internal.company.com', aws_access_key_id='minioadmin', aws_secret_access_key='minioadmin' ) s3.create_bucket(Bucket='ml-models') s3.upload_file('model.pkl', 'ml-models', 'v1/model.pkl') How ML pipeline migration works
The process includes several stages. First, we audit the current pipelines and their dependencies. Then we deploy a Kubernetes infrastructure with NVIDIA GPU Operator. Next, we port the code to Kubeflow Pipelines, ensuring compatibility with MLflow for experiment tracking. After load testing, we perform the final deployment.
Typical migration mistakes
- Underestimating DevOps workload: on-premise requires a team to support infrastructure that the cloud provider handles.
- Forgetting monitoring: without Prometheus and Grafana you'll be blind.
- Choosing the wrong storage: hot data needs NVMe, not HDD.
- Ignoring security: default network policies and no encryption lead to leaks.
How to ensure security for on-premise ML
On-premise does not automatically mean security. You need: network segmentation (GPU cluster in an isolated VLAN), mTLS between services, data at rest encryption (LUKS for disks), role-based access control via LDAP/AD integration, and audit logging of all model and data actions. We guarantee your infrastructure meets GDPR and SOC2 requirements.
Hybrid approach
Full migration to on-premise is not always optimal. A hybrid architecture: training and data on-premise, peak inference scaling through the cloud (burst capacity), disaster recovery in the cloud. This reduces CAPEX while keeping control over sensitive data.
What's included in our work
- Audit of your current cloud infrastructure and model
- Design of on-premise architecture (GPU cluster, storage, network)
- Kubernetes setup with NVIDIA GPU Operator
- Migration of ML pipelines (Kubeflow, MLflow)
- Deployment of MinIO, PostgreSQL, Redis, Prometheus/Grafana
- Integration with corporate authentication (LDAP/AD)
- Operations documentation and team training
- Support during the warranty period
Migration stages
| Stage | Duration | Result |
|---|---|---|
| Audit and design | 1-2 weeks | Architecture document |
| Infrastructure deployment | 2-3 weeks | Working GPU cluster |
| Pipeline migration | 4-6 weeks | All ML pipelines running on-premise |
| Testing and optimization | 1-2 weeks | Performance not worse than cloud |
| Documentation and training | 1 week | Team ready for operations |
Timelines and complexity
Initial hardware and base platform setup: 4-6 weeks. Migration of existing ML pipelines: 8-12 weeks. Full operational maturity (monitoring, DR, automation): 4-6 months.
Why choose us
- 5+ years of MLOps experience
- 50+ successful migrations
- Certified engineers (NVIDIA, Kubernetes)
- Guaranteed result: we refund if latency increases by more than 10%
Contact us for a preliminary assessment. Request a consultation — we'll prepare the architecture in 2 days.