Cloud vs on-prem AI: break-even can arrive in 9 months
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Damien · LocalIAAn honest comparison between OpenAI / Anthropic APIs and a local AI rig, with three concrete TCO scenarios over 24 months.
The old cloud promise was simple: pay only for what you use and avoid upfront investment. In 2026, that promise breaks down for many SME workflows because token volume rises quickly once AI becomes operational.
Why the cloud is no longer automatically cheaper
If a team sends 50 manual prompts per day, APIs are cheap. But real production usage is RAG, agents, classification pipelines and tools calling the model repeatedly.
At that point the unit is no longer thousands of tokens per day, but millions of tokens per month.
Three representative curves
| Legal firm RAG | 22M tokens/month | Break-even around month 10-12. |
| Creative agency agents | 75M tokens/month | Break-even around month 4. |
| Industrial batch classification | 150M tokens/month | Break-even around month 12 with a larger rig. |
Hidden cloud costs
- Input and output are both billed, so long RAG contexts are paid on every call.
- Retries after timeouts or schema errors are billed again.
- Zero Data Retention and enterprise contracts can add large minimum commitments.
- Model deprecations force retesting, reprompting and sometimes application changes.
- Data transfers to US providers can create GDPR work that does not appear in the token price.
When cloud still wins
- You are still exploring and do not know the final model.
- You need proprietary capabilities that open models do not match yet.
- Your traffic has rare huge spikes and low daily usage.
- Your usage is below roughly 10M tokens per month and unlikely to grow.
The practical pattern is cloud to explore, on-prem to industrialize. Once usage is stable, local hardware becomes a production asset.
Open the calculator / request a quote with your target model, users and constraints.
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