How do open-source LLMs compare in cost to proprietary APIs?
The trade-off: tokens vs. infrastructure
Proprietary APIs charge per token, so your cost scales directly with usage. There's no upfront hardware cost, and you get reliability, updates, and support from the provider. For small projects or spiky traffic, this is usually the cheapest and simplest option.
Open-source models are free to download, but running them yourself means paying for GPUs, electricity, cooling, and engineering time. If you use a hosted open-source provider, you still pay per token, often at lower rates than top proprietary models, but quality and latency vary.
When self-hosting makes sense
Self-hosting can be cost-effective when you have steady, high-volume usage that keeps your hardware busy. The break-even point depends on GPU rental or purchase prices, model size, and how efficiently you serve requests. Typical ranges vary widely: some teams break even at a few million tokens per day, others need much more.
You also gain control over data privacy and customization. But you take on operational burden: scaling, monitoring, and updating the model. Many teams start with APIs and move to self-hosting only when usage and predictability justify it.
- APIs: pay per token, no hardware, quick to start.
- Self-hosted open-source: high fixed cost, low marginal cost.
- Hosted open-source: middle ground, per-token pricing.
- Break-even depends on utilization and model size.
Common mistakes
- Comparing only the model license cost and ignoring hardware and ops expenses.
- Assuming open-source always means cheaper, even at low volume.
- Forgetting that hosted open-source APIs may have different quality and rate limits.
