Migrating an AI solution from the cloud to on-premise
Migrating AI from the cloud to on-premise infrastructure is an inversion of the standard approach. Reasons include security and compliance concerns (prohibiting data transfer to the cloud), economics under high, constant load (owned GPUs are cheaper than rented ones with 70%+ utilization), latency requirements (edge deployment), and corporate policy.
Economic Analysis: When is On-Premise More Profitable?
The cost of renting 8x A100 80GB on AWS (p4d.24xlarge): ~$32/hour or ~$280,000/year at 100% utilization. The cost of owning a DGX A100 80GB server: ~$200,000 + $20,000/year operational expenses. With >60% utilization, owning a server pays for itself in 18-24 months.
On-premise ML platform architecture
On-Premise Infrastructure:
├── GPU Cluster (обучение)
│ ├── Training nodes: 4x DGX A100 (32 GPU)
│ └── InfiniBand network 200Gbps
├── Inference Cluster (инференс)
│ ├── 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 a replacement for S3:
import boto3
# Код не меняется — MinIO S3-совместим
s3 = boto3.client(
's3',
endpoint_url='https://minio.internal.company.com',
aws_access_key_id='minioadmin',
aws_secret_access_key='minioadmin'
)
# Создание bucket и загрузка — идентично S3 API
s3.create_bucket(Bucket='ml-models')
s3.upload_file('model.pkl', 'ml-models', 'v1/model.pkl')
Security of on-premise ML infrastructure
On-premise doesn't automatically guarantee security. Required features include network segmentation (GPU cluster in an isolated VLAN), mTLS between services, data encryption at rest (LUKS for disks), role-based access control via LDAP/AD integration, and audit logging of all actions with models and data.
Hybrid approach
A complete transition to on-premise isn't always optimal. A hybrid architecture: on-premise training and data, peak inference scaling through the cloud (burst capacity), and disaster recovery in the cloud. This reduces capex while maintaining control over the data.
Timeframe 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. The key risk is underestimating the DevOps workload: on-premise requires a team to support the infrastructure provided by the cloud provider.