Summary: Request counts cannot be converted directly into residential proxy data requirements. Page resources, clients, failed requests, retries, and workflows all affect actual usage. When choosing a Gleam plan, measure usage through representative tests, then consider the operating period and package validity to determine how much data to purchase.
“If I visit 10,000 pages a day, is 100GB enough?”

That number alone is not enough for a reliable answer. Retrieving a text page and opening a full browser can consume very different amounts of data. Even for the same product page, loading images, scripts, and videos changes the total.

Start by finding out how much data a task actually uses.

Why Can the Same Request Volume Produce Very Different Budgets?

Suppose two projects each run 10,000 tasks a day for 30 days a month, with a 15% allowance added to baseline usage.

The first project retrieves only the HTML it needs, consuming an average of 500KB per task. Using decimal units for a rough estimate:

500KB × 10,000 tasks/day × 30 days × 1.15
≈ 172.5GB

The second project loads pages in a browser, consuming an average of 3MB per task:

3MB × 10,000 tasks/day × 30 days × 1.15
≈ 1,035GB

The task counts are the same, but estimated monthly usage rises from about 172.5GB to roughly 1TB. These are illustrative calculations only; actual billing depends on the provider's data measurement rules.

If an application moves through several pages, follows repeated redirects, or retries failed requests, one request cannot simply be treated as one complete task. Define the unit of work consistently before estimating usage to avoid omissions.

Keep Small Tests Representative of Production

A representative test should use the production client and the complete workflow wherever possible.

If you plan to use Playwright, test with a browser. If production tasks involve searching and then filtering, include those steps. If the workflow covers several countries, include those locations in the sample.

Record the total number of tasks, valid results, failures, retries, total data usage, and runtime. Alongside the average, check whether a small number of resource-heavy pages account for a disproportionate share of usage.

For example, suppose a test runs 1,000 tasks and uses 800MB, with 800 tasks producing valid results. Average usage is 0.8MB per executed task, but 1MB per usable record.

If the business goal is to obtain a fixed number of valid records, the second metric is more useful for budgeting. Avoid double counting: If the average already includes normal retries, do not add the same retry allowance again.

Convert Test Results into a Data Purchase Estimate

Use the following calculation:

Estimated data usage = Average data per valid task
                     × Required valid tasks per day
                     × Planned operating days
                     × Additional variability factor

The additional variability factor covers changes that the sample may not fully represent, such as more page resources, seasonal content changes, or higher task volumes. Base the allowance on observed behavior rather than automatically adding 10% or 20% to every project.

During the early stages of operation, compare estimates with actual usage regularly. If the gap keeps growing, investigate page resources, retries, and task scope before deciding whether to purchase more data.

Gleam Plan Reference: Compare Unit Prices and Validity Periods

The following reference table shows Gleam's package tiers. Actual prices, validity periods, and renewal policies are subject to the dashboard information at the time of purchase.

Gleam uses account top-ups followed by data package purchases, without automatic subscription charges. Projects still undergoing validation can start with a small allowance, complete connection and location testing, and expand later.

Validity periods affect cost as well. A larger package with a lower unit price may not offer better value if it cannot be used before expiry. Purchasing again within the validity period can update the expiry of unused data under the applicable rules; confirm the conditions and expiration date before purchasing.

Reduce Usage by Addressing Unproductive Work First

Data optimization should look beyond page size and identify requests that fail to produce usable results.

If you only need a few public text fields, evaluate whether direct HTTP requests can meet the requirement. If a browser is necessary, check whether videos, large images, fonts, and other resources contribute to the task, then test whether their loading can be reduced.

Blocking resources should not break page behavior. Some pages rely on scripts and API responses for content. Reducing resource loading may lower usage while also making results incomplete.

Set clear retry limits and stopping conditions. Authentication failures, page structure changes, and incorrect URLs usually cannot be fixed through endless requests. When failures persist, pausing the task and retaining error records is more useful than continuing to consume data.

Frequently Asked Questions

How many pages can I visit with 1GB?

There is no universal number. Measure the average usage of representative tasks, then estimate capacity from the available allowance.

Do browser tasks always require a large plan?

No. Total usage also depends on task volume and duration. Even resource-heavy pages may require only a small allowance for short, infrequent tasks.

How can I tell whether a package offers good value?

Consider actual usage within the validity period, the cost per valid result, and total spending together. Do not compare the advertised price per GB alone.

Should I purchase more data immediately if usage suddenly rises?

First check for additional tasks, more resource loading, or abnormal retries. Once the cause is clear, decide whether to optimize the application or increase the allowance.