The Real Cost of Running LLMs On-Premise (It's Not What You Think)

Sarah, an engineering director at a mid-size logistics firm, decided to purchase a local GPU after her cloud inference bill reached $8,000 in a single month. After a few weeks of setup, she had her first pipeline running. The power cost for running a 24/7 local GPU, serving inference from various automation workflows, was modest compared to the cloud API spend for the same volume.

After accounting for hardware amortization, monthly power cost amounted to $58–73 per month, while the equivalent cloud API cost was $180–240 per month. The real cost of running LLMs on-premise is not just the power bill; it includes the time spent debugging and learning about inference engine memory allocation, Docker networking, and system RAM constraints.

While a local inference call saves money, it costs time, which adds up. The breakeven point for on-premise inference is roughly 3–4 months for a $500–600 GPU versus $180–240 per month with a cloud API. However, below 100 calls per day, the cloud API becomes cheaper when considering the time spent. The decision to go with on-premise inference should consider whether the time spent debugging and learning about the system is worth the cost savings, especially when dealing with high-volume repetitive inference tasks and privacy-sensitive data.