> ## Documentation Index > Fetch the complete documentation index at: https://docs.meter.sh/llms.txt > Use this file to discover all available pages before exploring further. # Core Concepts > Understand the fundamental building blocks of Meter # Core Concepts Meter is built around a few key abstractions that make web scraping and monitoring simple and cost-effective. Understanding these concepts will help you get the most out of the platform. ## The big picture A reusable extraction plan generated by AI that defines **how** to scrape a website. Created once, used many times. A single execution of a scrape using a strategy. Jobs run asynchronously and return extracted data. Automated recurring jobs that run at specified intervals or cron times. Perfect for monitoring websites. Chain strategies into DAG-based pipelines where the output of one scraper feeds into the next. For multi-step scraping like index → detail pages. Intelligent diffing that compares jobs to detect meaningful content changes, filtering out noise. ## Key concepts Learn how AI-generated extraction strategies work and when to use them Organize strategies for bulk scheduling and shared output schemas Define the exact JSON structure for extraction results Filter extraction results with conditions and operators Understand job execution, status checking, and result retrieval Set up automated monitoring with intervals or cron expressions Chain strategies into multi-step scraping pipelines Discover how Meter detects meaningful content changes ## How it all fits together ```mermaid theme={null} graph TD A[Generate Strategy] --> B{Simple or Pipeline?} B -->|Simple| C[Create Schedule] B -->|Pipeline| W[Create Workflow] W --> C C --> D[Jobs Run Automatically] D --> E[Compare with Previous Job] E --> F{Changes Detected?} F -->|Yes| G[Webhook or Pull API] F -->|No| H[Skip - No Action] G --> I[Update Vector DB] ``` ### Example workflow 1. **Generate a strategy** for extracting product data from an e-commerce site 2. **Create a schedule** to scrape the site every hour 3. **Jobs run automatically**, extracting current product data 4. **Changes are detected** by comparing content hashes and structural signatures 5. **You get notified** via webhook or pull from the changes API 6. **Update your database** only with changed content ## Cost model Understanding Meter's cost structure helps you optimize usage: | Action | Cost | Frequency | | ----------------------- | ------------- | --------------------- | | **Strategy generation** | \~\$0.02-0.06 | Once per site/pattern | | **Job execution** | Included | Unlimited | | **Change detection** | Included | Automatic | | **API calls** | Included | Unlimited | ### Why strategy-based is cheaper Traditional LLM scraping costs scale with usage: * **Traditional**: Pay per scrape (\$0.02-0.10 each) * **Meter**: Pay once for strategy (\$0.02-0.06), then scrape unlimited times at no extra cost For a site scraped 100 times: * Traditional LLM scraping: \$2-10 * Meter: \$0.02-0.06 (97-99% savings) ## Data model Understanding the data model helps you work with the API: ``` User ├── Strategy Groups │ └── Strategies (grouped for bulk management) ├── Strategies │ ├── Preview Data (sample extraction) │ ├── Output Schema (optional) │ ├── Filter Config (optional) │ ├── Jobs │ │ ├── Results (extracted data) │ │ ├── Content Hash │ │ └── Structural Signature │ └── Schedules │ ├── Interval or Cron │ ├── Webhook URL (optional) │ └── Associated Jobs └── Workflows ├── Nodes (strategy + input config) ├── Edges (data flow + filters) ├── Runs (execution history) └── Schedules (interval or cron) ``` ## Best practices If multiple pages have the same structure (e.g., product pages, blog posts), you can reuse the same strategy with different URLs: ```python theme={null} # Generate once for pattern strategy = client.generate_strategy( url="https://shop.com/product/123", description="Extract name, price, description" ) # Reuse for different products job1 = client.create_job(strategy_id, "https://shop.com/product/123") job2 = client.create_job(strategy_id, "https://shop.com/product/456") ``` Instead of webhooks for every change, poll the changes API periodically: ```python theme={null} # Check once per hour for all changes changes = client.get_schedule_changes(schedule_id) if changes['count'] > 0: batch_process(changes['changes']) ``` This reduces webhook traffic and allows batching updates. **Faster intervals** (15-30 min): * Stock prices, sports scores, breaking news * High-priority monitoring **Moderate intervals** (1-6 hours): * E-commerce products, job listings * Most monitoring use cases **Slow intervals** (daily): * Documentation, blog posts, policies * Low-frequency content Jobs can fail if sites are down or block requests: ```python theme={null} job = client.get_job(job_id) if job['status'] == 'failed': print(f"Job failed: {job['error']}") # Retry logic, alerts, etc. ``` ## Next steps Learn how AI generates extraction strategies Master job execution and result handling Set up automated monitoring Build multi-step scraping pipelines ## Need help? Email me at [mckinnon@meter.sh](mailto:mckinnon@meter.sh)