Why your LLM extraction pipeline will cost you more than you think at scale

At low volumes, feeding HTML into an LLM for extraction looks like a reasonable shortcut. At 50,000 pages a month, it stops looking reasonable entirely.

The problem is not that LLMs extract data poorly in every case. The problem is that their cost model scales with token volume, and web pages are large. A realistic full HTML page averages around 572,000 tokens. At that size, even the cheapest nano-class models charge roughly $0.03 per page. At 120,000 pages a month, that is $3,480 with GPT-5 nano alone. Mid-range models push that figure past $17,000. Claude Sonnet 4.6 reaches $207,980 for the same workload.

Stripped HTML reduces the token count significantly, but introduces its own tradeoff: you risk removing markup that contains the data you need, and the preprocessing step adds engineering time. There is no clean solution on the LLM side.

The Minexa API does not use token-based pricing. A page costs the same to process whether it is 10,000 tokens or 600,000. At 120,000 pages a month, the Startup plan handles the full workload at a flat rate. The cost gap versus LLMs widens sharply as volume increases.

Beyond pricing, there is a reliability issue that token costs do not capture. LLMs processing similar-looking fields on the same page, such as a sale price and an original price, or a start date and a completion date, will occasionally assign values to the wrong field. This happens without any error signal. The output looks valid. The data is wrong. At 100,000 pages, that translates to thousands of rows requiring downstream validation.

Minexa uses DOM-based extraction. Each column is bound to a specific element in the page structure. If that element is missing, the output is null. If the page does not match the trained scraper, the API returns an explicit error. It never fabricates a value to fill a gap.

The workflow for developers starts in the Chrome extension. You open a target page, select the HTML container holding the data block, and Minexa generates a reusable scraper automatically. This takes 2 to 5 minutes. The result is a scraper_id you reference in every subsequent API call.

A basic extraction request looks like this:

POST https://api.minexa.ai/data/

{
  "batches": [{
    "scraper_id": 6241,
    "columns": ["top_30"],
    "urls": ["https://example.com/listing/99"],
    "scraping": {
      "js_render": true,
      "proxy": "verified"
    }
  }],
  "threads": 5
}

If you already have HTML stored externally, the file_urls parameter lets you pass those files directly. Minexa reads from your stored HTML instead of re-fetching the live page, which reduces credit consumption since no JavaScript rendering is needed.

The threads parameter controls how many URLs are processed in parallel. Higher thread counts mean faster throughput across large batches. Up to 50,000 URLs can be submitted in a single request.

Once trained, a scraper works across all structurally similar pages indefinitely. The engineering effort does not grow with volume. Training once on a product page structure means every product page on that site can be extracted without additional setup.

If a site redesigns and the scraper begins returning errors or null values, retraining takes the same 2 to 5 minutes as the original setup. The only required code change afterward is updating the scraper_id in the request body.

For developers evaluating this approach, the API documentation covers all request parameters in detail, and the Chrome extension generates ready-to-run Python code you can copy directly from the interface.

Read the full API docs at minexa.stoplight.io or start with the extension at minexa.ai.