When we started building Debono, the obvious choice was to call OpenAI’s API. Most startups do. But after running the numbers, we chose a different path: self-hosted open-source models on our own GPUs.
The Cost Math
A single pharmacy student generating flashcards from a 200-slide lecture deck might trigger 50-100 API calls. At GPT-4 pricing, that’s $2-5 per study session. Multiply by thousands of students and the margins evaporate.
Running Qwen3-VL-8B on our own RTX 3090s costs us effectively nothing per inference after the hardware investment. That’s how we offer a $15/month plan that would be unprofitable at $150/month on OpenAI’s API.
Speed
Self-hosted models on local GPUs respond in 50-200ms. No network round-trip to a cloud API. No queue. No rate limits. When a student is grinding through flashcards, that latency difference is the difference between flow state and frustration.
Privacy
Pharmacy students study real drug interactions, dosing protocols, and clinical scenarios. That data shouldn’t leave the building. With self-hosted models, it doesn’t. No terms of service to worry about. No data training opt-outs to configure. The data never leaves our server.
The Tradeoff
Self-hosting means we maintain the infrastructure ourselves. GPU drivers, model updates, memory management, failover — it’s real work. But for our use case, the math is clear: better margins, faster responses, stronger privacy guarantees.
Not every product needs self-hosted AI. But if you’re building something where cost-per-inference matters and data privacy is non-negotiable, it’s worth doing the math yourself.