"Where should I run my AI systems?" This question confronts CIOs daily. The answer seems simple: cloud is modern, scalable, and cost-effective. However, AI as the fourth layer of IT architecture brings new requirements that turn traditional hosting decisions upside down.

Why AI Hosting is Different

With traditional software, the hosting decision is usually a matter of cost: cloud for flexibility, on-premises for control. With AI systems, new dimensions are added:

Data residency becomes critical: AI models require continuous access to training data. Where this data may be stored often determines the hosting option.

Compliance becomes complex: An AI system that makes decisions about credit approval or medical diagnoses is subject to different regulatory requirements than a CRM system.

Performance is unpredictable: AI workloads can spontaneously increase tenfold when complex queries are made. Traditional capacity planning fails.

The Three Hosting Options in Detail

Cloud AI: Fast, but with caveats

Advantages:

• Instant access to GPU capacity

• Managed services for ML pipelines

• Automatic scaling

• No hardware investments
  
  

Disadvantages:

• Data leaves the company

• Vendor lock-in with specialized services

• Unpredictable costs with intensive usage

• Latency with large data volumes
  
  

Suitable for: Startups, non-critical applications, prototyping

On-Premises: Control at Any Cost

Advantages:

• Complete data control

• Compliance security

• Predictable costs

• No dependency on internet connections
  
  

Disadvantages:

• High initial investments

• Complex hardware management

• Limited scalability

• Expertise development required
  
  

Suitable for: Banks, healthcare, government agencies, companies with sensitive data

Hybrid: The Best of Both Worlds?

Advantages:

• Sensitive data remains internal

• Cloud for peak loads

• Gradual migration possible

• Flexibility for different applications
  
  

Disadvantages:

• Complex Architecture

• Data Flow Management Required

• Dual Expertise Necessary

• Potential Security Vulnerabilities at Interfaces

Suitable for: Large companies with mixed requirements

The Decision Matrix

Regulatory Requirements

Health data (GDPR Art. 9): On-premises or certified EU cloud
Financial data (MaRisk, Solvency II): On-premises with documented exceptions
Personnel data: Hybrid possible, but with strict separation
Trade secrets: Dependent on company policy

Technical Requirements

Real-time decisions: On-premises for minimal latency
Batch processing: Cloud for cost efficiency
Continuous learning: Hybrid for flexibility
Experimental projects: Cloud for rapid iteration

Organizational Factors

Existing expertise: Cloud when lacking AI know-how
Investment budget: Cloud for low initial costs
Long-term strategy: On-premises for strategic AI systems
Vendor preferences: Dependent on existing partnerships

Practical Decision-Making Aids

Questions you should ask yourself

1. Data classification: Which of your data may leave the company?

2. Compliance requirements: Which auditors do you need to satisfy?

3. Performance requirements: How critical are response times?

4. Scaling expectations: How much will AI usage grow?

5. Expertise availability: Do you have AI operations know-how?

Typical Scenarios

Scenario 1: Medium-sized Bank

• Requirement: Credit risk assessment

• Decision: On-premises

• Reason: BaFin compliance, sensitive financial data

Scenario 2: E-Commerce Company

• Requirement: Product recommendations

• Decision: Cloud

• Reason: Scalability, non-critical data

Scenario 3: Pharmaceutical Corporation

• Requirement: Research Data Analysis

• Decision: Hybrid

• Reason: Sensitive research data internal, computing power from cloud

The Hidden Costs

Cloud-Fallen

Data egress fees: Transferring large datasets can become expensive
Specialized services: GPU instances quickly cost five-figure amounts monthly
Vendor lock-in: Migration between cloud providers is complex and expensive

On-Premises Realities

Hardware refresh: GPU technology evolves rapidly
Expertise costs: AI operations specialists are expensive and scarce
Underutilization: Hardware often sits unused

Hybrid complexity

Dual infrastructure: Costs for both environments
Integration: APIs and data flows between environments
Security: Protection of hybrid interfaces

What really works

Start with clear data classification

Before you decide on hosting, categorize your data:

• Public: Cloud without restrictions

• Internal: Cloud with encryption

• Confidential: Hybrid with strict separation

• Secret: On-premises only

Start with pilot projects

Cloud-First for Experiments: Test new AI applications in the cloud first
On-Premises for Production: Operate proven systems internally
Hybrid for Scaling: Offload peak loads to the cloud

Plan exit strategies

Avoid vendor lock-in: Use standardized APIs and data formats
Prepare for migration: Documentation and processes for transitions
Multi-cloud strategy: Reduce dependencies on individual providers

The strategic dimension

AI hosting is not just a technical, but a strategic decision. It determines:

• Which AI applications you can actually implement

• How quickly you can respond to new requirements

• How dependent you become on external providers
  
  

Today's hosting decision shapes your AI strategy for years to come. Don't just choose for today, but for the AI-native organization you want to become.

The question isn't "Cloud or on-premises?" but rather "Which hosting strategy enables our AI vision?" The answer to that is individual – but it should be made deliberately.

This is the fourth part of our series on AI integration beyond pilot projects. Next week: Which AI governance policies you really need and what must be included in them.