What is batch web scraping?

Batch scraping means handing over a list of URLs as one job and collecting the results when it finishes, rather than looping over the list yourself and waiting. The job runs asynchronously: you submit, you get an identifier, you check on it or receive a webhook when it completes.

The difference from a loop is not throughput. It is who owns the failures.

What a loop makes you build

Write a synchronous loop over fifty thousand URLs and you will end up writing all of this, in roughly this order, each time prompted by something going wrong:

• Retry with backoff, once transient failures start losing pages.
• Concurrency control, once sequential turns out to be too slow and unlimited turns out to get you blocked.
• Progress tracking, once a run dies at page thirty thousand and you cannot tell which ones completed.
• Incremental writes, once holding results in memory stops being viable.
• A dead-letter list, once you need to know which URLs failed permanently.
• Resumability, once restarting from the beginning becomes unacceptable.

None of it is hard. All of it is orchestration code that has nothing to do with the data you wanted, and it has to be maintained afterwards.

When batch is the right shape

Batch fits when you have the URL list already and do not need results immediately: nightly refreshes, catalogue snapshots, RAG ingestion passes, backfills.

It fits badly when a person is waiting. If a user clicks a button and expects data, a synchronous request is the right call even if it is only one page. Asynchronous jobs trade latency for reliability, which is a good trade for scheduled work and a bad one for interactive work.

The other case where batch is wrong is when request N depends on request N minus one. A crawl that discovers URLs as it goes needs a loop, or a sequence of batches with a discovery step between them.

Knowing the cost before you spend it

A useful property of submitting work as a job is that the job can be priced before it runs. On a loop you discover the cost afterwards, which is how a crawl that rendered every page turns into a surprise. Being able to check first is what makes the decision about rendering a decision rather than a postmortem.

Where Zenrows fits

Batch takes a URL list, runs it, and collects results, with retries, pacing and orchestration inside the job. It reports progress while running, supports webhooks and scheduling, and lets you check a job's cost before it spends anything.

Per-task parameters are supported, so one job can render the URLs that need a browser and fetch the rest as standard requests, which matters when rendering costs five credits against one. The Batch FAQ covers limits, scheduling and how it relates to Fetch.