AgentQL Alternatives — Better AI Web Data Extraction

AgentQL takes a unique approach to web data extraction: instead of CSS selectors or XPath, you describe what you want in natural language and it uses AI to find and extract the data. It's built for web agents and AI workflows. The concept is solid, but at $99/month for the Professional plan with $0.015-0.02 per additional API call, costs escalate quickly for high-volume extraction.

If AgentQL's pricing or approach doesn't fit your pipeline, here are 7 alternatives worth evaluating.

Key Takeaways

• AgentQL's Professional plan is $99/month for 10,000 calls ($0.015/call beyond that)
• Natural language queries are convenient but slower and less reliable than direct selectors at scale
• Several alternatives offer higher throughput and lower per-request costs
• SearchHive ScrapeForge provides LLM-optimized extraction at $0.001/page — 15x cheaper per page
• The best alternative depends on whether you need natural language queries or just clean data extraction

1. SearchHive ScrapeForge

Best for: High-volume data extraction with clean markdown output at low cost.

AgentQL's natural language query model is novel, but if you know the pages you're scraping, direct extraction is faster, cheaper, and more reliable. SearchHive ScrapeForge extracts content, strips boilerplate, and returns markdown optimized for LLM pipelines.

Pricing: $0.001/page. At AgentQL's Professional tier volume (10,000 pages), SearchHive costs $10. AgentQL costs $99.

import requests

API_KEY = "your-searchhive-key"

# Extract product data from an e-commerce page
result = requests.post(
    "https://api.searchhive.dev/v1/scrape",
    headers={"Authorization": f"Bearer {API_KEY}"},
    json={
        "url": "https://store.example.com/product/12345",
        "format": "markdown",
        "remove_boilerplate": True,
        "wait_for": ".price-block"  # Wait for dynamic pricing
    }
)

data = result.json()
# Clean product markdown — title, price, description, specs
print(data["content"])

For structured extraction, pair ScrapeForge with SearchHive's DeepDive API to extract specific fields from the markdown:

response = requests.post(
    "https://api.searchhive.dev/v1/deepdive",
    headers={"Authorization": f"Bearer {API_KEY}"},
    json={
        "content": data["content"],
        "extract": ["product_name", "price", "rating", "availability", "features"]
    }
)

structured = response.json()
print(structured["product_name"])  # "Widget Pro X500"
print(structured["price"])         # "$129.99"

Two API calls, $0.002 total, and you get structured data from any page.

/blog/searchhive-scrapeforge-api-guide

2. Firecrawl Extract

Best for: Structured data extraction using LLMs with a managed API.

Firecrawl's /extract endpoint uses LLMs to extract structured data from web pages. You define the schema in JSON, Firecrawl returns matching data.

Pricing: 5 credits per extract request. Standard plan: $83/month for 100K credits (20,000 extracts). Growth: $333/month for 500K credits.

The LLM-based extraction is similar in concept to AgentQL but with explicit schema definition rather than natural language queries. More predictable output format. Credit system is less flexible than per-page pricing.

from firecrawl import FirecrawlApp

app = FirecrawlApp(api_key="your-firecrawl-key")

result = app.extract(
    urls=["https://store.example.com/product/12345"],
    prompt={
        "prompt": "Extract product name, price, and rating",
        "schema": {
            "type": "object",
            "properties": {
                "name": {"type": "string"},
                "price": {"type": "number"},
                "rating": {"type": "number"}
            }
        }
    }
)

3. Tavily Extract

Best for: AI agent workflows combining search with extraction.

Tavily's Extract endpoint converts URLs to clean text. Combined with their search API, it handles the find → extract pipeline.

Pricing: Free: 1,000 requests/month. Pro: $60/month for 40K extracts. Enterprise: custom.

Lower cost than AgentQL for basic extraction. But Tavily doesn't do structured data extraction — it returns markdown. For field-level extraction, you need to process the output yourself.

4. Browserbase + Playwright

Best for: Teams needing full browser control with custom extraction logic.

Browserbase provides managed headless browsers. Write extraction logic in Playwright or Puppeteer.

Pricing: Free: 1,000 sessions. Developer: $39/month. Professional: $149/month.

Full browser control means you can implement any extraction strategy — CSS selectors, XPath, custom JavaScript. The trade-off is engineering time. You're building everything yourself.

5. Diffbot

Best for: Automatic page classification and structured extraction without queries.

Diffbot uses computer vision to classify pages (article, product, discussion, etc.) and extract structured fields accordingly. No query or prompt needed — it figures out the page type.

Pricing: Free: 500 requests/month. Startup: $99/month for 10K requests. Growth: $299/month for 50K requests.

Diffbot's automatic classification is its differentiator. Send any URL and get structured data back without defining what to extract. But pricing is similar to AgentQL Professional ($99/month base), and accuracy varies for non-standard page layouts.

6. Import.io

Best for: Non-technical users who want point-and-click data extraction with scheduling.

Import.io provides a visual interface for selecting data from web pages. Handles pagination and scheduling.

Pricing: Starter: $199/month for 10,000 queries. Professional: $499/month for 50,000 queries.

Significantly more expensive than both AgentQL and SearchHive. The visual interface is accessible for non-developers, but the pricing makes it hard to justify for anything beyond small-scale extraction.

7. ScrapFly + BeautifulSoup

Best for: Python developers wanting structured extraction with proxy management.

ScrapFly provides the infrastructure (rendering, proxies, anti-detection). BeautifulSoup handles the parsing and extraction.

Pricing: From $25/month for 100,000 API credits.

You get full control over extraction logic with BeautifulSoup's CSS selector and traversal capabilities. ScrapFly handles the infrastructure. Combined cost is lower than AgentQL at most volumes, but you're writing and maintaining extraction code for each target site.

Comparison Table

Recommendation

AgentQL's natural language query model is genuinely useful for exploratory extraction and web agent prototyping. If you're building an agent that navigates unknown websites and needs to adapt its extraction strategy dynamically, AgentQL's approach has merit.

For most production use cases, you know what pages you're scraping and what data you need. Direct extraction is faster, cheaper, and more reliable. SearchHive ScrapeForge + DeepDive gives you the same structured output at $0.002 per page versus AgentQL's $0.015-0.02 — that's a 10x cost difference at scale.

Start with SearchHive's free tier to test on your target sites. If you need the natural language flexibility, AgentQL is worth the premium for prototyping. But for batch extraction, SearchHive delivers the same data at a fraction of the cost.

/compare/agentql

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Last updated: April 2026. Pricing verified from competitor websites.