What is a credit?
Credits determine your usage limits on Browse AI. Each plan provides you with a specific number of monthly or yearly credits that define how much data you can extract, how many screenshots you can capture, and how frequently you can monitor websites for changes.
The amount of credits charged varies if you're extracting data from a standard site, or a premium site. The credit system is designed to align with the resources required to run your tasks.
What is a task?
A task is a single execution of your robot when it visits a website to extract data or capture screenshots. Each time you click Run on a robot, you're initiating a task. Similarly, when a monitor automatically runs on schedule, it counts as a task. Tasks are the basic unit of work in Browse AI and are what consume your credits.
Different types of tasks include:
Extracting data from a list page.
Capturing data from a detail page.
Taking screenshots of a webpage.
Checking for changes during a monitoring run.
Running a robot as part of a workflow.
How are credits calculated?
Minimum cost per task: 1 credit on standard sites, and 2-10 credits on Premium sites
Cost for extracting 10 rows of data on a standard site: 1 credit
All single captured texts in a task form a single row together, so they cost 0.1 credit (unlimited number)
Cost for capturing a screenshot on a standard site: 1 credit
If you capture screenshots AND list items, each screenshot costs 0.1 credits
Cost for capturing 10 rows of data on a premium site: 2 to 10 credits
What are premium sites, and why do you charge more?
A small number of sites require premium IP addresses, solving captchas, or a large volume of files to load. This means that they cost more on our side to process. These sites are marked as premium.
Do my credits roll over?
No. Credits reflect your usage limit which depending on your plan could be either a monthly or an annual limit.
One of the benefits of an annual plan is you get you get all credits upfront, meaning that you have an annual limit of usage vs. having to manage usage monthly.
Credit usage examples
Example 1: Extracting a list of products
If you extract a list of 50 products from a single page:
Credit calculation: 50 rows × 0.1 credit per row = 5 credits.
You'll use 5 credits for this task.
Example 2: Extracting a small list
If you extract a list of 8 products:
Credit calculation: 8 rows × 0.1 credit per row = 0.8 credits.
Since this is below the minimum, you'll use 1 credit.
Example 3: Extracting from detail pages
If you need data from 20 individual product pages:
Each page requires a separate task.
Credit calculation: 20 tasks × 1 credit minimum = 20 credits.
You'll use 20 credits for this extraction.
Example 4: Deep scraping (lists + details)
If you extract a list of 100 product links and then visit each product page:
List extraction: 100 rows × 0.1 credit = 10 credits.
Detail page extraction: 100 pages × 1 credit = 100 credits.
Total: 110 credits.
Example 5: Premium site extraction
If you extract 45 items from a Premium site (with a minimum cost of 5 credits):
Credit calculation: 45 rows × 0.1 credit = 4.5 credits.
Since the Premium minimum is higher, you'll use 5 credits.
Example 6: Monitoring websites
If you monitor 10 product detail pages for changes every 3 days:
Each check runs as a separate task: 10 pages × 1 credit = 10 credits per check.
Monthly usage: 10 credits × (30 days ÷ 3 days) = 100 credits per month.
Why you might see a varying number of list items per credit
When looking at a single task in your History tab, you'll see something like this:
These examples use non-Premium websites (learn about Premium sites).
Scraping only list items (as shown above):
1 credit per task (includes 10 list items) + 0.1 credit per additional list item
Breakdown: Since there are no other text elements or screenshots being extracted, 1 credit will get you 10 list items. This is the most straightforward example.
Scraping a list of items, one screenshot, and 5 text elements:
1 credit per task (includes 8 list items) + 0.1 credit per additional list item
Breakdown: Since we are extracting a screenshot (0.1 credit) and text elements (0.1 credit for unlimited text), that means that 1 credit now gets us 8 list items (0.1 credits per list item) instead of 10.
How can I reduce my credit usage?
Extract lists instead of detail pages: extracting from a page with multiple items (like a category page) uses 10× fewer credits than visiting individual detail pages.
Optimize monitoring frequency: adjust your monitoring schedule to check only as often as necessary.
Use deep scraping efficiently: collect as many URLs as possible in a single list extraction before visiting detail pages.