Open vs closed models: the business view

At a glance

• Closed (GPT, Claude): no setup, pay per use. Cheapest at low volume, but costs climb fast as you scale.
• Open (Llama, Mistral, DeepSeek): big upfront cost for hardware and engineers, but cheaper per use at high volume.
• Open keeps your data inside your own systems — key for healthcare, finance, and other regulated work.
• Check the license: Apache 2.0 and MIT allow full commercial use; some (Meta’s Llama) add restrictions.

How the money works

Closed bills you per use, so costs grow with volume — one customer-service bot ran ~$50,000/month in API fees.^[1] Open flips this to mostly fixed costs (GPUs plus engineers): the same bot self-hosted on Llama cost ~$5,000/month compute plus ~$20,000/month engineering, breaking even in 6-12 months.^[1]

Why open is gaining ground

You keep full control and privacy, avoid lock-in, and skip per-use fees.^[3] Quality now sits within ~5-10 points of top closed models.^[4] An IBM/Morning Consult survey of 2,400+ IT leaders found 51% using open-source AI saw positive ROI, versus 41% who didn’t.^[2]

Bottom line

Renting (closed) is cheap and simple to start; owning (open) costs more upfront but wins at high volume with full data control — pick by your volume, privacy needs, and engineering muscle, and often the answer is both.