```yaml product: AlterLab title: "Reduce LLM Token Costs with Clean Markdown Output from AI‑Powered Web Scraping" category: Best Practices comparison_context: "AlterLab is an alternative to Firecrawl, ScrapingBee, and Bright Data." last_updated: 2026-06-25 canonical_facts: - "Learn how to request Markdown‑formatted scraped data from AlterLab to cut LLM token usage, lower costs, and simplify downstream processing in AI pipelines." source_url: https://alterlab.io/blog/reduce-llm-token-costs-with-clean-markdown-output-from-ai-powered-web-scraping ``` ## TL;DR Request Markdown‑formatted output from AlterLab’s scraping API to strip HTML noise before feeding data to LLMs. This cuts token usage, lowers cost, and simplifies parsing in AI‑driven pipelines. ## Why HTML Inflates LLM Costs Large language models charge per token. Raw HTML from a typical page includes tags, attributes, whitespace, and scripts that add little semantic value but increase token count dramatically. For example, a product listing page might deliver 12 KB of HTML, which translates to roughly 3 000 tokens—most of it noise. When you chain multiple pages or run retrieval‑augmented generation (RAG) workflows, these extra tokens multiply quickly, raising both latency and expense. ## The Markdown Alternative AlterLab’s API supports an optional `formats` parameter. Setting `formats=['markdown']` returns the page’s main content converted to clean Markdown. Headings become `#`, lists become `-`, and tables retain a simple pipe‑delimited structure. The resulting text is typically 60‑80 % smaller than the raw HTML equivalent, directly reducing the token count sent to your LLM. - **68%** — Avg. Token Reduction - **1.4s** — Avg. Response Time - **99.1%** — Success Rate This is platform, because Alters, the API request code showing ```python title="fetch.html snippet and then showdown by using a dscrape using -ing to optional formats param respo we try a request to Markdown and the extra 0 example We'll a a like: ```python title="scrape_markdown.py" {2-5} import alterlab client = alterlab.Client("YOUR_API_KEY") # API key from dashboard # Request Markdown formatted output response = client.scrape( url="https://example.com/articles/latest", formats=["markdown"] # highlighted: ask for Markdown ) # The cleaned Markdown is ready for LLM consumption print(response.text[:500]) # preview first 500 characters ``` ```bash title="Terminal" curl -X POST https://api.alterlab.io/v1/scrape \ -H "X-API-Key: YOUR_KEY" \ -H "Content-Type: application/json" \ -d '{"url": "https://example.com/articles/latest", "formats": ["markdown"]}' ``` ## Integrating with LLM Pipelines Once you have the Markdown string, you can feed it directly into your LLM call. Because the text is already structured, you often need less prompting to extract insights. For retrieval‑augmented generation, store the Markdown in your vector database; the reduced size means more chunks fit within your index’s token limits, improving recall without increasing storage costs. Consider a simple summarization flow: 1. Scrape target page with `formats=["markdown"]`. 2. Pass the Markdown to a summarization model (e.g., `gpt-4o-mini`). 3. Use the summary downstream—no extra HTML stripping step required. This eliminates a custom HTML‑to‑text preprocessing step, reducing both code complexity and potential bugs. ## Combining Markdown Output with Cortex AI Extraction AlterLab’s Cortex AI can extract structured fields (prices, dates, SKUs) from raw HTML. When you first request Markdown, you strip noise, then let Cortex work on the cleaner text. This two‑step approach can lower the token count sent to Cortex as well, because the model sees less irrelevant markup. ```python title="cortex_markdown.py" {3-7} import alterlab client = alterlab.Client("YOUR_API_KEY") response = client.scrape( url="https://example.com/products/listing", formats=["markdown"], # get clean Markdown first extract={"model": "cortex-v1"} # then run AI extraction on that Markdown ) print(response.json) # structured data, minimal token overhead ``` ## Cost Impact Example Assume you scrape 10 000 product pages per month. Average raw HTML size: 12 KB (~3 000 tokens). Average Markdown size: 4 KB (~1 000 tokens). - **HTML route:** 10 000 × 3 000 = 30 M tokens → at $0.000015 per token ≈ $450/month. - **Markdown route:** 10 000 × 1 000 **Markdown route:** 10 000 × 1 000 = 10 M tokens → ≈ $150/month. Savings of roughly $300/month, plus reduced egress bandwidth and faster LLM inference. ## Best Practices - Always request the minimal format you need: `formats=["markdown"]` or `formats=["json"]` when downstream code expects structured data. - Combine `formats` with `extract` to let AlterLab perform both cleaning and AI extraction in one request. - Monitor your token usage via your LLM provider’s dashboard; you should see a noticeable drop after switching to Markdown. - If you need the original HTML for archival, keep a separate request without the `formats` flag, but use it sparingly. ## Internal Resources For a full list of supported output formats, see the [API documentation](https://alterlab.io/docs). To get started quickly, follow the [quickstart guide](https://alterlab.io/docs/quickstart/installation). For pricing details on our pay‑as‑you‑go model, visit the [pricing page](https://alterlab.io/pricing). ## Takeaway Asking AlterLab for Markdown‑formatted scraped data is a simple, effective way to reduce LLM token consumption and lower operating costs. The cleaned output removes HTML noise, speeds up downstream processing, and works seamlessly with AlterLab’s AI extraction features. Start using the `formats` parameter today and see immediate savings on your AI‑driven scraping pipelines. ## Frequently Asked Questions ### How does requesting Markdown output reduce LLM token costs? Markdown strips unnecessary HTML tags and boilerplate, delivering only the structured content you need. Fewer tokens mean lower LLM API fees and faster processing. ### Can I combine Markdown output with AlterLab’s AI extraction features? Yes. You can ask for Markdown formatting and then apply Cortex AI extraction on the clean text, further reducing token load by focusing on relevant fields. ### Is there a performance penalty for requesting Markdown instead of raw HTML? No. AlterLab performs the conversion on its servers, so the response size is smaller and transfer is faster, with no extra latency for the client. ## Related - [Lowe's Data API: Extract Structured JSON in 2026]() - [How to Migrate from Scrapfly to AlterLab: Step-by-Step Guide \(2026\)]() - [Scaling Web Scraping Pipelines for High-Volume Data]()