- Take N companies with different ATS
- Calculate the lowest, average, median, and highest amount of text we need to aggregate those vacancies to become searchable (basically, to generate tags and skills based on that)
- Min., Average, Median and Max. amount of text as input
- Min., Average, Median and Max. amount of tokens as input
- Daily, weekly, monthly refresh cost with an average update for ~3 times / month
| Filter | Num. Companies | Num. Jobs | Min. | Max. | Avg. | Median |
|---|---|---|---|---|---|---|
| Characters | 5 | 361 | 1391 | 6956 | 3463 | 3567 |
| Tokens | 5 | 361 | 283 | 1339 | 681 | 659 |
Converting to OpenAI GPT-3.5 input tokens pricing (0.0010$ / 1000 tokens):
| Category | Tokens | Price | Price per 100 vacancies | Refresh cost (3x / month) | Refresh cost (100 vacancies / month) |
|---|---|---|---|---|---|
| Min. | 283 | 0.000283$ | 0.0283$ | 0.000849$ | 0.0849$ |
| Max. | 1339 | 0.001339$ | 0.1339$ | 0.004017$ | 0.4017$ |
| Avg. | 681 | 0.000681$ | 0.0681$ | 0.002043$ | 0.2043$ |
| Median | 659 | 0.000659$ | 0.0659$ | 0.001977$ | 0.1977$ |
Meaning that, with a budget of 100$ / month, we can approximately hold:
- Median estimate: ~50600 vacancies in the database in up-to-date state.
- Highest estimate: ~25000 vacancies in the database in up-to-date state.