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Cloud vs. On-Premise

Purpose

Decision framework for choosing between cloud, on-premise or hybrid infrastructure for your AI system.

Decision framework for choosing between cloud deployment, on-premise infrastructure or a hybrid approach. Use this during the Discovery & Strategy phase before architectural choices are locked in.


1. Decision Matrix

Score each criterion based on your situation: C = advantage for Cloud, O = advantage for On-Premise, = = neutral.

Criterion Weight Your situation Direction
Data sovereignty — data must remain in NL/EU High C / O
Scalability — volumes vary significantly or are unknown High C / O
Time-to-market — quick prototype or pilot needed High C / O
Cost certainty — predictable monthly costs required High C / O
Compliance — sector regulation requires full control High C / O
Latency — real-time processing with \< 100 ms required Medium C / O
Existing infrastructure — significant on-prem investment present Medium C / O
Maintenance capacity — internal team for infrastructure management available Medium C / O

Interpretation

  • Predominantly C: cloud-first approach recommended
  • Predominantly O: on-premise or private cloud recommended
  • Mixed: consider hybrid architecture

2. Decision Tree (5 questions)

1. Does the system process special categories of personal data (health, biometrics)?
   YES → On-premise or private cloud strongly recommended
   NO  → go to 2

2. Is the expected load unpredictable or seasonal (10× variation)?
   YES → Cloud recommended (elastic scalability)
   NO  → go to 3

3. Does the organisation have < 2 FTE available for infrastructure management?
   YES → Cloud recommended (managed services)
   NO  → go to 4

4. Does the sector require full audit control over hardware and data location?
   YES → On-premise or private cloud required
   NO  → go to 5

5. Is time-to-market < 3 months for a working system?
   YES → Cloud recommended
   NO  → both options comparable; base on TCO

3. Cloud Deployment

Providers — Comparison

Aspect AWS Azure GCP
LLM/AI services Bedrock (Claude, Llama) Azure OpenAI, Copilot Vertex AI (Gemini)
EU data residency Frankfurt, Ireland West/North Europe Belgium, Netherlands
Compliance ISO 27001, SOC 2 ISO 27001, SOC 2 ISO 27001, SOC 2
Min. costs (dev) Pay-per-use Pay-per-use Pay-per-use
MLOps platform SageMaker Azure ML Vertex AI

Cloud Cost Management

Primary cost drivers in cloud AI deployments:

  • Inference APIs — cost per token/request (largest variable cost for LLM applications)
  • Compute (GPU/CPU hours) — for training and fine-tuning
  • Storage — model artefacts, training data, vector databases
  • Network — data transfer and egress costs

See Cost Optimisation for reduction techniques (caching, model tiering, batch processing).

Cloud Security Checklist

  • Data residency configured to EU region
  • Encryption at rest and in transit configured
  • IAM with least-privilege configured
  • VPC/private endpoint for sensitive services
  • Secrets management (no credentials in code)
  • Logging and audit trail active
  • Budget alerts configured

4. On-Premise Deployment

Infrastructure Requirements

Component Minimum (pilot) Production
CPU 16 cores 32+ cores
RAM 64 GB 256 GB+
GPU Optional (CPU inference) NVIDIA A100 / H100 for large models
Storage 2 TB NVMe 20+ TB RAID
Network 1 Gbps 10 Gbps
OS Ubuntu 22.04 LTS Ubuntu 22.04 LTS / RHEL

Software Stack (open source options)

Layer Option Licence
Model serving Ollama, vLLM, TGI MIT / Apache 2.0
Orchestration Kubernetes (k3s for small) Apache 2.0
MLOps MLflow, DVC Apache 2.0
Monitoring Prometheus + Grafana Apache 2.0
Vector store Qdrant, Weaviate, pgvector Apache 2.0 / BSD

TCO Calculation (simplified)

CapEx (one-off):
  Hardware:            €_______
  Installation/setup:  €_______

OpEx (annual):
  Energy:              €_______ /year
  Maintenance/admin:   €_______ /year  (1–2 FTE × rate)
  Licences:            €_______ /year

Compare with Cloud:
  Expected cloud costs:  €_______ /year
  Break-even point:      _______ years

5. Hybrid Architecture

The most common hybrid patterns:

Pattern Description When
Dev cloud / Prod on-prem Develop in cloud (flexible), run in production on-prem (control) Strict production requirements, flexible R&D
Data on-prem / Inference cloud Raw data stays on-prem; anonymised/processed to cloud for inference Data sovereignty + scalability
Multi-cloud Critical workloads on two providers Avoid vendor lock-in, high availability
Edge + cloud Real-time inference on-device; heavy processing in cloud IoT, low latency, limited connectivity

6. Recommendations by Organisation Profile

Profile Recommendation
Explorer (first pilot) Cloud-first: managed LLM API + SaaS tooling. Minimal infrastructure investment.
Builder (production systems) Hybrid: cloud for dev/test, on-prem or private cloud for production data.
Visionary (portfolio) Multi-cloud + on-prem for critical systems. Own Platform Enablement team.