On-Premise AI is worthwhile for companies of all sizes, from small teams with 5 users to large enterprises with hundreds of users. Economics depend less on company size than on usage intensity and data protection and compliance requirements.
Small companies (5–20 employees)
When is On-Premise AI worthwhile?
Ideal for:
- Companies with high data protection requirements
- Regulated industries (banks, law firms, healthcare)
- Companies with sensitive data
- Companies with growing needs
Hardware options:
- SMALL platform – affordable entry
- Full data sovereignty
- Scalable with growth
Break-even:
- Typical: 12–18 months
- With high usage: 6–12 months
Advantages:
- Low entry costs
- Full data sovereignty
- No usage dependency
- Scalable with growth
When is cloud more sensible?
Cloud can be more sensible for:
- Very small teams (<5 users)
- Unpredictable workloads
- Pilot projects
- Non-critical applications
Mid-size companies (20–100 employees)
When is On-Premise AI worthwhile?
Ideal for:
- Companies with predictable, high-volume needs
- Companies with compliance requirements
- Companies with multiple business units
- Companies with growing teams
Hardware options:
- MEDIUM or LARGE platform
- Cluster configurations for larger setups
- Kubernetes for scalability
Break-even:
- Typical: 6–12 months
- With high usage: 3–6 months
Advantages:
- Fast amortisation
- Predictable costs
- Scalable architecture
- Multi-tenant capability
Typical scenarios
Software startup:
- High token usage from developers
- On-Premise significantly more cost-effective
Law firm:
- 25 lawyers, document analysis
- High data protection requirements
- On-Premise required for compliance
Medical practice:
- 15 employees, patient data
- HIPAA compliance required
- On-Premise required for compliance
Large companies (100+ employees)
When is On-Premise AI worthwhile?
Ideal for:
- Companies with multiple sites
- Companies with strict compliance requirements
- Companies with proprietary data
- Companies with high token usage
Hardware options:
- Multi-server clusters: Kubernetes-based
- Geographic distribution: multi-site deployment
- High-availability setups: P99/P100 SLA
Break-even:
- Typical: 3–6 months
- With very high usage: 1–3 months
Advantages:
- Very fast amortisation
- Maximum control
- Enterprise features
- Scalable architecture
Typical scenarios
Financial services:
- 200+ employees, trading algorithms
- FINMA compliance required
- On-Premise required for compliance
Large law firm:
- 150+ lawyers, contract analysis
- High data protection requirements
- On-Premise required for compliance
IT service provider:
- 300+ developers, code analysis
- Very high token usage
- On-Premise significantly more cost-effective
Decision factors
1. Usage intensity
High usage (On-Premise sensible):
- Developers with agentic coding tools
- Lawyers with contract analysis
- Analysts with complex analysis
Low usage (cloud can be more sensible):
- Occasional email summaries
- Sporadic usage
- Pilot projects
2. Data protection requirements
High requirements (On-Premise required):
- Regulated industries
- Sensitive business data
- Compliance requirements (GDPR, HIPAA, FINMA)
Low requirements (cloud possible):
- Public data
- Non-critical applications
3. Cost structure
Predictable costs (On-Premise):
- Fixed monthly costs
- No surprises
- Long-term planning
Variable costs (cloud):
- Costs rise with usage
- Unpredictable invoices
- Difficult budget planning
Next steps
Would you like to find out if On-Premise AI is worthwhile for your company?
- Contact us – Get advice for your company size
Sources and further information:
- How much does it cost to run AI yourself? – Costs and scenarios
- On-Premise AI for SMEs – Practical guide for various company sizes