Local AI vs Cloud: The TCO Comparison
Basics · 8 min
Cloud AI looks cheap — no hardware to buy, no maintenance, just start. But the hidden costs add up. Here's our honest comparison based on real usage.
Note: This is our comparison for the use case "continuous AI agent for business automation". For one-off analyses or prototypes, cloud can be cheaper.
The Scenarios
Cloud Usage
100 daily API calls to OpenAI/Gemini/Claude for workflow automation, support chatbot, and content generation.
Local Stack
Ollama on your own hardware (RTX 3090), n8n for automation, self-hosted monitoring. Everything runs 24/7.
Cost Comparison (per month)
| Cost Item | Cloud | Local | Difference |
|---|---|---|---|
| API costs | €150-300 | €0 | -€150-300 |
| Hardware/Amortization | €0 | €25-50 | +€25-50 |
| Electricity (estimated) | €0 | €20-40 | +€20-40 |
| Hosting/Server | €0 | €10-20 | +€10-20 |
| Monitoring/Tools | €20-50 | €0* | -€20-50 |
| GDPR compliance | €50-200 | €0 | -€50-200 |
| Total/Month | €220-550 | €55-110 | -€165-440 |
*Grafana + Prometheus are open source, free
The Hidden Cloud Costs
- API costs escalate — The more workflows you automate, the more calls. Often 2-3x higher than initially planned.
- GDPR risk — Data goes to the US. Art. 44 ff. GDPR requires additional measures (SCCs, TIAs). Legal counsel: €1,000+.
- Vendor lock-in — Your prompts, workflows, data are with the provider. Switching is expensive and time-consuming.
- Rate limits — Cloud providers throttle with heavy usage. Business plans cost extra again.
- Data incidents — Every data leak is your problem. Local systems = less risk.
When Cloud is Cheaper
| Use Case | Recommendation |
|---|---|
| Prototype (few calls/month) | Cloud — no setup needed |
| One-off analyses | Cloud — pay-as-you-go |
| No budget for hardware | Start cloud, switch later |
| Few internal tools | Cloud — overscale for little use |
| Continuous automation (our use case) | Local — cheaper after 6 months |
Break-Even Analysis
When does switching to local make sense?
Assumptions: - RTX 3090 used: €600 (amortized over 24 months = €25/month) - Electricity: €30/month - Other costs (hosting, maintenance): €20/month - Total local: ~€75/month Break-even with cloud (estimated €200/month): → After 3 months: €600 (cloud) vs €225 (local) = €375 saved → After 12 months: €2,400 (cloud) vs €900 (local) = €1,500 saved → After 24 months: €4,800 (cloud) vs €1,800 (local) = €3,000 saved
Hardware Recommendations
| GPU | VRAM | Price (used) | Models |
|---|---|---|---|
| RTX 3060 | 12GB | €200-250 | Llama 3.2 7B, Mistral 7B |
| RTX 4070 | 12GB | €400-500 | Llama 3.1 8B, Qwen 14B |
| RTX 3090 | 24GB | €500-700 | Llama 3.1 70B (Quantized) |
| RTX 4090 | 24GB | €1,200-1,500 | Llama 3.1 70B, Qwen 72B |
Our Recommendation
Hybrid Approach (our setup)
- Local: Ollama for regular tasks, n8n workflows, monitoring
- Cloud: GPT-4o for complex reasoning tasks (few calls/month)
- Result: Best of both worlds — cost-efficient and powerful
Conclusion
At ~100 API calls per month, local becomes cheaper. Plus you get GDPR benefits (no third-country transfer) and independence from cloud providers. Our recommendation: Start with cloud (prototype), then switch to local (production).
Next step: move from knowledge to implementation
If you want more than theory: setups, workflows and templates from real operations for teams that want local, documented AI systems.
- Local and self-hosted by default
- Documented and auditable
- Built from our own runtime
- Made in Austria