Tracking SaaS Revenue Benchmarks Across 500 Indie Hackers Products

#webdev #programming #beginners #webscraping

Early-stage market researchers and bootstrapped founders frequently lack structured financial benchmarks for niche software businesses. Unlike public equities that report standardized quarterly 10-K filings, micro-SaaS and solo-founder businesses rarely share financial figures. Indie Hackers is one of the few platforms where software founders voluntarily post verified or self-reported financial milestones, business descriptions, product taglines, tech stacks, and interview transcripts.

Manually reviewing these listings does not scale if you need to analyze cohort metrics—such as average monthly revenue grouped by specific product categories or founding year. Automating this extraction turns unstructured community discussions and product profiles into normalized records for competitive intelligence.

The Indie Hackers Scraper extracts product profiles, discussion groups, and founder interviews into clean records. It allows you to browse and search products sorted by revenue, newest additions, or member count, outputting core fields such as product name, tagline, monthly revenue, founder names, and assigned tags.

What the Extracted Dataset Contains

When running the actor across product listings, each record returned to the default dataset contains discrete fields representing the business's public profile:

{
  "productName": "FeedbackWidget",
  "tagline": "Collect user feedback and bug reports with a lightweight script",
  "monthlyRevenue": 4200,
  "founder": "AlexRivera",
  "tags": ["saas", "analytics", "developer-tools"],
  "memberCount": 184,
  "interviewAvailable": true
}

This structured output isolates commercial indicators from narrative interviews. By extracting the raw numeric value in monthly revenue alongside product category tags, you can construct category-level valuation multiples, median recurring revenue across specific sub-niches, or time-to-revenue models for modern software products.

The actor also supports fetching founder interviews and listing community discussion groups. Founder interview records capture the qualitative strategies behind those revenue numbers, including initial distribution channels, tech stack choices, and customer acquisition costs.

Executing a Targeted Run via API

You can trigger the scraper programmatically within an existing data pipeline using Python. The actor uses a pay-per-event pricing structure, meaning runs can be integrated directly into automated ETL workflows without maintaining long-running browser instances.

Here is a Python example utilizing the Apify API client to trigger the run, wait for dataset generation, and load the records directly into a data processing workflow:

from apify_client import ApifyClient

# Initialize the client with your platform API token
client = ApifyClient("YOUR_API_TOKEN")

# Run the Indie Hackers Scraper actor
# You can browse/search products sorted by revenue, newest, or member count
run = client.actor("crawlerbros/indie-hackers-scraper").call()

# Fetch results from the run's default dataset
dataset_items = client.dataset(run["defaultDatasetId"]).list_items().items

print(f"Extracted {len(dataset_items)} records.")

# Example transformation: Filter for SaaS products generating over $1,000 MRR
revenue_leaders = [
    item for item in dataset_items
    if item.get("monthlyRevenue", 0) >= 1000
]

for product in revenue_leaders[:5]:
    print(f"{product.get('productName')}: ${product.get('monthlyRevenue')}/mo - Tags: {product.get('tags')}")

This script initiates the scraper, waits for the job to complete, and iterates over the returned items to isolate high-performing projects based on self-reported revenue.

Step-by-Step Run Walkthrough

To integrate this scraping workflow into your recurring market research process:

Configure the Data Target: Determine whether your downstream pipeline requires product discovery lists (sorted by revenue, newest, or member count), founder interviews, or community discussion group listings.
Execute the Scraper: Start the run either via the Apify API as shown in the script above or directly from the actor interface on the platform.
Stream or Export the Dataset Items: Once the status transitions to finished, query the default dataset endpoint to retrieve the array of extracted JSON objects.
Validate and Clean Numerical Fields: Check the monthlyRevenue field across records. Because Indie Hackers products can have unverified self-reported revenue or missing numbers, cast these fields safely to numeric types and handle null values.
Load into Downstream Storage: Insert the cleaned records into a relational database or warehouse (such as PostgreSQL or BigQuery) to build longitudinal datasets tracking how individual products grow their monthly revenue over time.

Calculating Run Costs

The billing model for this actor uses explicit Pay-Per-Event pricing rather than variable execution duration metrics:

Actor Start Event: Flat rate of $0.005 per GB of memory allocated to the run. For a baseline run allocated 1 GB of memory, this equals $0.005 charged once per run.
Result Event: The default dataset item event is charged at $0.005 per event under the FREE tier ($5.00 per 1,000 items). Higher platform volume tiers provide discounted rates for this event: BRONZE at $0.00433, SILVER at $0.00367, and GOLD, PLATINUM, and DIAMOND tiers at $0.003 per event.

For an analytical run that extracts 500 product records on a standard 1 GB allocation under the FREE tier:

Actor Start event: 1 run × $0.005 = $0.005
Result events: 500 items × $0.005 = $2.50
Total run cost: $2.505

Because costs are tied directly to returned dataset items, budgeting for continuous dataset syncs remains strictly predictable based on the number of items extracted per sync cycle.

Pipeline Integration and Limitations

This approach provides high-fidelity access to indie product metadata, but it is not a complete verification tool for enterprise financial audits. A key limitation of this dataset is that revenue figures on Indie Hackers are largely self-reported or connected via Stripe verification badges that the scraping layer captures as reported values, meaning unverified profiles can introduce noise into quantitative research.

For downstream pipelines, always run schema validation to flag missing revenue keys before calculating aggregate industry medians.

The examples here were produced with Indie Hackers Scraper. Its README lists the output fields, so you can check a response against the schema before you build on it.

Prices quoted above are this Actor's published pay-per-event rates on the Apify Store, read from the Apify platform API on 2026-09-13. Check the Actor page for the current rates.

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