Clearbit Data API: Extract Structured JSON in 2026

Disclaimer: This guide covers extracting publicly accessible data. Always review a site's robots.txt and Terms of Service before scraping.

TL;DR

To get structured Clearbit data via API, use the AlterLab Extract API to send a target URL and a JSON schema. AlterLab handles the request and returns a validated, typed JSON object containing the specified fields, removing the need for manual HTML parsing or CSS selector maintenance.

Why use Clearbit data?

Clearbit is a primary source for B2B intelligence. Extracting this data into a structured format allows engineers to build automated pipelines for several high-value use cases:

– AI Training and RAG: Feed structured company metrics into Large Language Models (LLMs) to provide grounded context for B2B AI agents. – Competitive Intelligence: Monitor public data changes across competitors to track growth trends or product shifts in real-time. – Market Analytics: Aggregate public data points into a centralized dashboard to analyze industry-wide benchmarks.

What data can you extract?

When building a data pipeline for Clearbit, you should focus on publicly listed information. The goal is to transform unstructured HTML into a machine-readable format. Typical fields include:

– metric_name: The specific identifier of the data point (e.g., "Annual Growth"). – value: The numerical or text value associated with the metric. – date: The timestamp or period the data refers to. – source: The origin of the data point. – category: The classification of the metric (e.g., "Financial", "Demographic").

The extraction approach

Traditional web scraping relies on raw HTTP requests and HTML parsing (BeautifulSoup, Cheerio). This approach is fragile because any small change in the website's DOM breaks your parser, leading to pipeline downtime.

A data API approach shifts the responsibility of parsing from the developer to the API. Instead of writing code to find a div with a specific class, you define the shape of the data you want. AlterLab uses AI-powered extraction to locate the requested information regardless of layout changes, returning a clean JSON response.

For those new to the platform, our Getting started guide provides a walkthrough on setting up your environment.

Quick start with AlterLab Extract API

The Extract API allows you to convert any public Clearbit page into a JSON object. You provide the URL and the desired schema; the API handles the proxy rotation and browser rendering.

See the Extract API docs for a full list of parameters.

Python Implementation

Using the official SDK is the most efficient way to integrate this into a data pipeline.

import alterlab

client = alterlab.Client("YOUR_API_KEY")

schema = {
  "type": "object",
  "properties": {
    "metric_name": {
      "type": "string",
      "description": "The metric name field"
    },
    "value": {
      "type": "string",
      "description": "The value field"
    },
    "date": {
      "type": "string",
      "description": "The date field"
    },
    "source": {
      "type": "string",
      "description": "The source field"
    },
    "category": {
      "type": "string",
      "description": "The category field"
    }
  }
}

result = client.extract(
    url="https://clearbit.com/example-page",
    schema=schema,
)
print(result.data)

cURL Implementation

For lightweight integrations or shell scripts, use the REST endpoint.

curl -X POST https://api.alterlab.io/v1/extract \
  -H "X-API-Key: YOUR_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "url": "https://clearbit.com/example-page",
    "schema": {"properties": {"metric_name": {"type": "string"}, "value": {"type": "string"}, "date": {"type": "string"}}}
  }'

Define your schema

The power of a data API lies in the schema. AlterLab uses JSON Schema to validate the output. If the AI cannot find a field, it returns null rather than guessing, ensuring your downstream database remains clean.

Example Structured Output:

{
  "metric_name": "Market Penetration",
  "value": "24%",
  "date": "2025-Q4",
  "source": "Public Report",
  "category": "Growth"
}

By providing a description within the schema, you give the extraction engine a hint about what to look for, which significantly increases accuracy for ambiguous data points.

Handle pagination and scale

When extracting data at scale, synchronous requests are inefficient. For high-volume Clearbit data extraction, use asynchronous jobs. This allows you to submit thousands of URLs and poll for the results once they are processed.

import alterlab

client = alterlab.Client("YOUR_API_KEY")

urls = ["https://clearbit.com/p1", "https://clearbit.com/p2", "https://clearbit.com/p3"]
schema = {"properties": {"metric_name": {"type": "string"}, "value": {"type": "string"}}}

# Submit as a batch job
job = client.extract_async(
    urls=urls,
    schema=schema
)

# Poll for completion
while job.status == "processing":
    job = client.get_job(job.id)

print(job.results)

Cost and Optimization

To keep costs predictable, use the cost estimation endpoint before committing to a large batch. Costs are based on the complexity of the page and the LLM orchestration fee. You can view detailed AlterLab pricing to calculate your monthly budget.

For sites that require heavy JavaScript rendering, we recommend setting min_tier=3 to skip basic curl attempts and go straight to headless browser rendering.

Key takeaways

– Stop parsing HTML: Use a schema-based data API to avoid fragile CSS selectors. – Ensure type safety: Define your output as a JSON schema to guarantee consistent data types. – Scale asynchronously: Use async jobs for bulk extraction to avoid timeout issues. – Stay compliant: Only extract public data and respect robots.txt.

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