Show HN: We built PriceLevel to find out what companies pay for SaaS
Opaque enterprise SaaS pricing is being challenged by a new service, PriceLevel, that crowdsources real contract data so buyers can see what companies actually pay. Commenters welcome the transparency and practical features like price normalization and geographic filters, but raise concerns about buyer anonymity, legal exposure around NDAs and trade secrets, and the risk of falsified submissions. Many see strong demand for this kind of “Glassdoor for SaaS pricing,” while noting that highly customized enterprise deals and complex contract terms will limit how precise or comparable the data can be.
Overall Reception & Value
- Many commenters love the concept: “Glassdoor/levels.fyi for SaaS pricing,” filling a painful gap in opaque enterprise pricing.
- Buyers want quick ballpark figures (e.g., is it $3k vs $90k vs $200k) without entering sales funnels.
- Smaller SaaS providers see it as a way to understand typical enterprise deal sizes.
- Some argue $500/year pricing invites competitors to undercut with similar services.
Privacy, Anonymity & Data Fuzzing
- Strong concern that precise prices and seat counts can uniquely identify specific customers.
- Multiple suggestions:
- Round to 2 significant digits or nearest thousand/hundred.
- Show price/seat count ranges or percentage intervals (e.g., ±5%).
- Aggregate/faceted views with error bars once enough data exists.
- Fuzz attributes like geography, contract length, and company size.
- The team reports implementing rounding and adding geography, plus normalizing to annual pricing.
Legal & Trade-Secret Debates
- Long, conflicted discussion on whether pricing can be a trade secret.
- Some point to case law and say price lists and pricing strategies can be trade secrets, especially under NDA/confidentiality clauses.
- Others argue that what a customer paid is their own information, not the vendor’s secret, and that enforcing secrecy on prices is anti-competitive and contrary to efficient markets.
- Many note that SaaS contracts often include confidentiality around pricing.
- Ideas for risk mitigation: aggregate data, delete contributor PII, disclaim that contributors must have rights to share.
- Unclear consensus; several emphasize the operator should get legal counsel.
Data Quality, Manipulation & Verification
- Concern about fake or adversarial entries (e.g., inflating competitors’ prices).
- Proposed mitigations:
- Require documentation (quotes, invoices, contracts).
- Use company email; ban accounts submitting bad data.
- Use medians/outlier filtering.
- Allow vendors to respond or flag entries.
- Some note that sharing internal documents may violate corporate rules.
Product Design & Scope Feedback
- Need clarity on units: per year vs per month, per contract vs per seat; the site later normalizes to annual.
- Requests for more dimensions: region, contract length, feature tiers, seat counts, and global coverage.
- Recognition that bespoke enterprise deals (custom features, SLAs, support) limit comparability and may explain wide price spread.