Careeroza
Open vs Closed Models
Two Ways to Use a Model
| Closed (API) models | Open-weight models | |
|---|---|---|
| Examples | Claude, GPT, Gemini | Llama, Mistral, Qwen, Gemma, DeepSeek |
| How you use them | HTTP API, pay per token | Download weights, run on your own GPU/CPU |
| Quality | Usually the strongest | Good and improving; best ones need big GPUs |
| Data control | Data goes to the provider (check their data policy) | Data stays on your servers |
| Ops work | None | You manage GPUs, scaling, updates |
| Cost shape | Pay per use, zero idle cost | Fixed server cost, cheap at very high volume |
Break-even Calculator
Example
When does renting a GPU server for an open model become cheaper than paying per token? A rough comparison (numbers are assumptions; plug in your own):
api_price_in, api_price_out = 2.00, 10.00 # USD per 1M tokens (mid-tier API model)
gpu_server_per_month = 1_800 # USD, one GPU server running 24x7
tokens_in, tokens_out = 1_200, 200 # per request
per_request_api = tokens_in * api_price_in / 1e6 + tokens_out * api_price_out / 1e6
break_even = gpu_server_per_month / per_request_api
print(f"API cost per request: USD {per_request_api:.4f}")
print(f"Break-even: {break_even:,.0f} requests/month (~{break_even / 30:,.0f}/day)")
for daily in (1_000, 20_000, 100_000):
api = per_request_api * daily * 30
winner = "API" if api < gpu_server_per_month else "self-host"
print(f"{daily:>7,}/day -> API USD {api:>8,.0f}/month vs GPU USD {gpu_server_per_month:,} -> {winner}")Output
API cost per request: USD 0.0044 Break-even: 409,091 requests/month (~13,636/day) 1,000/day -> API USD 132/month vs GPU USD 1,800 -> API 20,000/day -> API USD 2,640/month vs GPU USD 1,800 -> self-host 100,000/day -> API USD 13,200/month vs GPU USD 1,800 -> self-host
Tip: The calculation ignores engineering time, quality differences and traffic peaks. Most teams start with an API and only self-host for strict data rules or very high, steady volume.