abeeba
Most teams start with a public AI API because it is the fastest way to ship. That is the right call early on. The question is when it stops being the right call, and the answer depends on three numbers you probably already have.
1. How many tokens you process each month
Add up input and output tokens across every product and internal tool. Output tokens usually cost more, so track them separately. If you do not know this number, that is the first thing to fix: put a gateway in front of every model call and log usage per team.
2. What the same workload costs on your own GPUs
A private deployment has a mostly fixed monthly cost: GPU servers (rented or owned), power and hosting, plus the engineering time to run them. The key variable is throughput per GPU. With modern serving software such as vLLM, continuous batching and quantization, a single GPU can serve far more requests than most teams expect, as long as it stays busy.
3. How steady your traffic is
Private capacity is cheapest when it is used. Spiky workloads with long idle periods favour APIs or autoscaled cloud GPUs; steady, high-volume workloads favour dedicated capacity.
Rule of thumb: once a single workload keeps one or more GPUs busy most of the day, self-hosting is usually worth a serious look.
Cost is not the only factor
- Data control: for regulated data, a private model may be the only acceptable option at any price.
- Latency: a model in your own region and network can respond faster and more predictably.
- Model choice: a smaller fine-tuned model often beats a larger general model on narrow tasks, at a fraction of the cost.
A sensible path
Keep the API for experiments and low-volume features. Move your top one or two high-volume workloads to a private deployment, measure, and expand from there. If you would like help building the model with your own numbers, book a consultation.