We extract software profiles, pricing matrices, SW Scores, feature lists, and user reviews from Saasworthy. Delivered as clean JSON, CSV, or Parquet to S3, BigQuery, or Snowflake.
Structured, schema-consistent data across all major object types — delivered clean, typed, and ready to query.
Complete list of extractable fields for Product Profiles objects from saasworthy.com. All fields typed and schema-versioned.
"software_id": "sw-9921", "name": "HubSpot CRM", "category": "CRM Software", "sw_score": 98.4, "website_url": "hubspot.com", "founded_year": 2006, "deployment": "Cloud"
| # | software_id | name | category | sw_score | description | website_url |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Pricing Plans objects from saasworthy.com. All fields typed and schema-versioned.
"software_id": "sw-9921", "plan_name": "Professional", "price": 800.0, "currency": "USD", "billing_cycle": "Monthly", "free_trial": true, "freemium": false
| # | software_id | plan_name | price | currency | billing_cycle | free_trial |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Reviews & Ratings objects from saasworthy.com. All fields typed and schema-versioned.
"review_id": "rev-10492", "software_id": "sw-9921", "rating_overall": 4.5, "rating_features": 4.8, "rating_ease_of_use": 4.2, "rating_support": 4.5
| # | review_id | software_id | reviewer_name | rating_overall | rating_features | rating_ease_of_use |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Features & Integrations objects from saasworthy.com. All fields typed and schema-versioned.
"software_id": "sw-9921", "feature_name": "Email Tracking", "feature_category": "Sales Automation", "is_supported": true, "api_available": true, "mobile_app": true
| # | software_id | feature_name | feature_category | is_supported | integration_name | integration_type |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Alternatives objects from saasworthy.com. All fields typed and schema-versioned.
"software_id": "sw-9921", "competitor_id": "sw-1042", "competitor_name": "Salesforce", "sw_score_diff": -1.2, "price_diff": "Higher", "shared_features": "['Lead Management', 'Pipeline Tracking']"
| # | software_id | competitor_id | competitor_name | comparison_url | sw_score_diff | price_diff |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Our Saasworthy scraper handles every layer of the platform, extracting software profiles, dynamic pricing tiers, SW Scores, and user reviews with anti-bot circumvention built in.
Extract software name, SW Score, target audience, deployment types, and vendor details across the entire catalogue.
Capture complex pricing tiers, billing cycles, freemium availability, and feature gating per plan.
Monitor Saasworthy proprietary SW Score changes over time to gauge product momentum.
Extract user reviews, pros, cons, and sub-ratings for features, support, and ease of use.
Scrape competitor recommendations and side-by-side comparison matrices.
Map supported third-party integrations, API availability, and platform extensions.
Normalise unstructured feature lists into boolean matrices for direct product comparisons.
Traverse the entire SaaS category tree to map software into primary and secondary markets.
Run one-off bulk exports or configure continuous pipelines at weekly cadences with change detection.
Brief in. Clean data out.
Provide category URLs, specific software IDs, or full catalogue requirements. We design the extraction schema together.
We configure Scrapy crawlers, proxy rotation, session management, and CAPTCHA handling for saasworthy.com.
Schema validation, null-rate checks, pricing outlier detection, and sample profiles before full launch.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
B2B software directories employ rate limiting and complex DOM structures. Here is how we stay resilient.
Directory sites rate-limit aggressive crawlers. Our system uses residential ISP proxies with realistic browser fingerprints and randomised request timing to avoid IP bans.
Pricing toggles and review paginations often require JavaScript. We run Playwright browser sessions to trigger lazy-loads and hydrate dynamic React components.
SaaS feature tables vary wildly between products. We use fallback chains and DOM traversal heuristics to normalise inconsistent feature matrices into clean boolean columns.
We maintain a hash index of last-seen values per field. Subsequent runs only push diffs, reducing compute cost and downstream processing load.
Every run emits structured logs to our observability stack. We alert on null-rate spikes and schema drift, responding before you notice.
SaaS vendors monitor competitor pricing changes, feature releases, and SW Score fluctuations.
Analysts track category growth, deployment trends, and integration ecosystems to identify market gaps.
Agencies and integration partners extract target audience data and technology stacks to qualify prospects.
ML teams use software descriptions and feature matrices to train B2B recommendation engines.
Product managers analyse pricing tiers and freemium models across categories to optimise their own pricing.
PE firms track review velocity and SW Score momentum to evaluate SaaS companies.
"SaaS software discovery platforms hold the most concentrated datasets on B2B pricing and feature matrices, yet extracting structured matrices requires significant infrastructure."
Most engineering teams underestimate the complexity of scraping nested B2B software directories. Extracting accurate pricing tiers, SW Scores, and alternative mappings requires session management, proxy rotation, and daily schema validation. DataFlirt handles the infrastructure so your engineers focus on data modelling.
Everything supported by our saasworthy.com scraper — rendered SPA elements, auth walls, rate-limit evasion and beyond.
Open-source tooling on proven cloud infra — no vendor lock-in, full observability.
Scrapy handles crawl orchestration and deduplication. Playwright handles JavaScript rendering and interaction flows.
We maintain pools of residential ISP proxies. Rotation happens per-request with sticky sessions where required.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling, dependency management, and SLA alerting.
Data delivered to where your team already works — no new tooling required.
About saasworthy.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information is generally permissible under applicable law. DataFlirt targets only public, non-authenticated software profiles, pricing, and reviews. We do not extract personal data or circumvent authentication walls.
We use heuristic parsing to map variable pricing tiers, billing cycles, and feature gating into structured JSON arrays, normalising the data across different vendors.
Yes. Every pipeline run produces timestamped snapshots. We maintain a time-series table per software profile for SW Score and review counts.
Full catalogue refreshes typically run at a weekly cadence to capture new software listings, pricing updates, and fresh reviews.
Yes. We scrape the Alternatives sections to map competitor relationships and side-by-side comparison metrics.
Absolutely. We provide a sample run of up to 100 software profiles as part of the pre-engagement scoping process so you can validate schema fit.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off export of a specific category or continuous monitoring of the entire SaaS catalogue, we scope, build, and operate the pipeline.
