```yaml
product: AlterLab
title: PitchBook Data API: Extract Structured JSON in 2026
category: Tutorials
comparison_context: "AlterLab is an alternative to Firecrawl, ScrapingBee, and Bright Data."
last_updated: 2026-08-02
canonical_facts:
- "Learn how to extract structured JSON from PitchBook pages using AlterLab's Extract API with schema validation, Python examples, and cost estimates."
source_url: https://alterlab.io/blog/pitchbook-data-api-extract-structured-json-in-2026
```
# PitchBook Data API: Extract Structured JSON in 2026
This guide covers extracting publicly accessible data. Always review a site's robots.txt and Terms of Service before scraping.
## TL;DR
Use AlterLab's Extract API to turn a PitchBook URL into typed JSON. Define a JSON schema for the fields you need (ticker, price, change_percent, volume, market_cap), POST the URL and schema, and receive validated data—no HTML parsing required.
## Why use PitchBook data?
PitchBook pages contain structured finance information that powers several workflows:
- **AI training**: Historical pricing and valuation data improve models for market prediction.
- **Analytics**: Combine ticker and market cap trends with internal datasets for portfolio analysis.
- **Competitive intelligence**: Monitor funding rounds, valuations, and ownership changes across private companies.
## What data can you extract?
Publicly visible PitchBook pages typically display:
- **ticker**: Stock symbol (e.g., `AAPL`).
- **price**: Current share price.
- **change_percent**: Percentage change from previous close.
- **volume**: Number of shares traded.
- **market_cap**: Total market valuation.
These fields appear in consistent HTML structures, making them ideal for schema-based extraction.
## The extraction approach
Attempting to parse PitchBook pages with raw HTTP requests and regex or BeautifulSoup is fragile:
- Frequent UI changes break selectors.
- JavaScript‑rendered content requires a headless browser.
- Anti‑bot mechanisms (CAPTCHAs, rate limits) demand rotating proxies and retry logic.
AlterLab's Extract API abstracts these challenges. It renders pages with a managed browser, applies anti‑bot bypass, and returns data that matches a JSON schema you provide. The result is typed, ready‑to‑consume JSON—no post‑processing needed.
## Quick start with AlterLab Extract API
First, install the AlterLab Python client:
```bash title="Terminal"
pip install alterlab
```
Then run a basic extraction. The example below requests ticker, price, and change_percent from a sample PitchBook page.
```python title="extract_pitchbook-com.py" {5-12}
import alterlab
client = alterlab.Client("YOUR_API_KEY")
schema = {
"type": "object",
"properties": {
"ticker": {
"type": "string",
"description": "The ticker field"
},
"price": {
"type": "string",
"description": "The price field"
},
"change_percent": {
"type": "string",
"description": "The change percent field"
}
}
}
result = client.extract(
url="https://pitchbook.com/example-page",
schema=schema,
)
print(result.data)
```
The call returns a JSON object like:
```json
{
"ticker": "MSFT",
"price": "420.15",
"change_percent": "1.23"
}
```
For users who prefer cURL, the equivalent request is:
```bash title="Terminal"
curl -X POST https://api.alterlab.io/v1/extract \
-H "X-API-Key: YOUR_KEY" \
-H "Content-Type: application/json" \
-d '{
"url": "https://pitchbook.com/example-page",
"schema": {"properties": {"ticker": {"type": "string"}, "price": {"type": "string"}, "change_percent": {"type": "string"}}}
}'
```
Both examples hit the AlterLab Extract API endpoint documented [here](/docs/api/extract). The platform handles rendering, proxy rotation, and schema validation behind the scenes.
## Define your schema
Passing a JSON schema gives you two guarantees:
1. **Field selection** – Only the keys you list appear in the output.
2. **Type safety** – Values are coerced to the declared type (string, number, boolean) or set to `null` if extraction fails.
A richer schema for all five finance fields looks like this:
```python title="full_schema.py" {3-15}
schema = {
"type": "object",
"properties": {
"ticker": {"type": "string"},
"price": {"type": "string"},
"change_percent": {"type": "string"},
"volume": {"type": "string"},
"market_cap": {"type": "string"}
},
"required": ["ticker", "price"]
}
```
Setting `"required"` ensures critical fields are present; optional fields may be `null` when the source page omits them. AlterLab validates the extracted payload against this schema before returning it, so downstream code can trust the shape.
## Handle pagination and scale
When extracting many PitchBook pages (e.g., a list of company profiles), consider:
- **Batching**: Group 50‑100 URLs per async job to amortize connection overhead.
- **Rate limiting**: AlterLab enforces per‑account limits; stay below the threshold to avoid HTTP 429 responses.
- **Cost estimation**: Use the `/v1/extract/estimate` endpoint to preview spend before launching a large job. Pricing details are available on the [pricing page](/pricing).
Here’s a Python snippet that submits a batch of URLs and polls for completion:
```python title="batch_extract.py" {8-20}
import alterlab, time
client = alterlab.Client("YOUR_API_KEY")
urls = [
"https://pitchbook.com/company/1",
"https://pitchbook.com/company/2",
# … add more
]
jobs = []
for url in urls:
resp = client.extract(
url=url,
schema={"type": "object", "properties": {"ticker": {"type": "string"}}},
async_=True,
)
jobs.append(resp.job_id)
# Poll until all jobs finish
results = []
while len(results) < len(urls):
for jid in jobs:
status = client.job_status(jid)
if status.state == "completed":
results.append(status.output)
jobs.remove(jid)
elif status.state == "failed":
raise RuntimeError(f"Job {jid} failed")
time.sleep(2)
print(results)
```
The `async_=True` flag tells AlterLab to run the extraction in the background, returning a job ID immediately. This pattern scales to thousands of pages while keeping your script responsive.
## Key takeaways
- AlterLab's Extract API converts public PitchBook pages into typed JSON using a schema you define.
- No need to maintain fragile parsers; the platform handles rendering, anti‑bot bypass, and validation.
- Start with a simple schema, test on a single URL, then scale with async jobs and cost estimation.
- Always verify that your extraction complies with PitchBook's robots.txt and Terms of Service.
- **99.2%** — Extraction Accuracy
- **1.4s** — Avg Response Time
- **100%** — Typed JSON Output
1. **Define Schema** —
2. **Call Extract API** —
3. **Receive Typed JSON** —
```
## Frequently Asked Questions
### Is there an official PitchBook data API?
PitchBook offers limited data access through its own platform, but does not provide a public REST API for arbitrary page extraction. AlterLab fills this gap by turning publicly visible PitchBook pages into typed JSON via a schema-driven extraction.
### What PitchBook data can I extract with AlterLab?
You can extract any publicly visible finance fields such as ticker, price, change_percent, volume, and market_cap. The output conforms to a JSON schema you define, guaranteeing typed, validation-checked results.
### How much does PitchBook data extraction cost?
AlterLab charges per extraction based on compute used, with a minimum of $0.001 and a maximum of $0.50 per call. There are no minimums, no expiring credits, and you pay only for what you use.
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