battles / Fraud
FraudLabs Pro vs Vesta
FraudLabs Pro ($99/mo/mo, vibe code 6/10) vs Vesta ($1,500/mo/mo, vibe code 3/10). FraudLabs Pro is the easier one to rebuild yourself — here is what you lose either way.
Fraud
$99/mo/mo
- MVP
- 3 days
- Full replacement
- Never fully replaceable without proprietary cross-merchant fraud signals
easier to rebuild
get the build prompt →Fraud
$1,500/mo/mo
- MVP
- 1 week
- Full replacement
- Never, due to chargeback financial underwriting and proprietary global risk network
price gap / year
$16,812/mo
running both / year
$19,188/mo
our call
Start with FraudLabs Pro — highest vibe code, weakest moat.
FraudLabs Pro
Coding a custom rule evaluation engine (e.g., flag order if billing country differs from IP country) is trivial. However, FraudLabs Pro relies on global blacklists, IP risk intelligence, and cross-merchant chargeback data that you cannot replicate in a standalone application.
you can rebuild
- Custom rule builder engine (e.g. IF order > $500 AND shipping != billing THEN flag)
- Manual order review status dashboard and approval queues
- Velocity checking per customer email or IP address
- Disposable email domain blacklisting using static open-source lists
- Automated order hold status updates via e-commerce platform webhooks
what you lose
- Access to a global cross-merchant blacklist of malicious buyers and emails
- Proprietary real-time IP reputation, proxy, VPN, and TOR exit node detection data
- Cross-site device fingerprinting telemetry
- Phone number and carrier risk scoring lookups
- Historical global chargeback statistics associated with individual buyer attributes
real moats
- Proprietary global dataset of reported fraud cases across thousands of online stores
- Network effects: every merchant reporting a chargeback strengthens protection for all other users
- Deep integration with commercial IP intelligence databases (such as IP2Location)
open source escape hatches
- json-rules-engine MIT
- Apache Unomi Apache-2.0
- Rspamd Apache-2.0
Vesta
While building a device fingerprint collector and rule-based risk dashboard takes days, Vesta's core offering is zero-liability financial guarantees and cross-merchant network signals. AI agents cannot generate financial underwriting balance sheets or billions of historical fraud data points.
you can rebuild
- Basic rule-based risk scoring engine (velocity, country blocklists)
- Client-side device fingerprinting collector script
- Order approval and rejection queue for manual review
- IP geolocation, proxy, and VPN detection API integration
- Threshold-based 3D Secure dynamic triggers
what you lose
- 100% zero-liability chargeback reimbursement guarantee
- Cross-merchant consortium dataset tracking fraudsters across thousands of stores
- Machine learning models trained on billions of historical card transactions
- Automated chargeback evidence submission and dispute handling
- Dedicated fraud analyst teams and custom enterprise risk models
real moats
- Financial balance sheet supporting chargeback indemnification
- Global cross-merchant graph dataset linking identities, devices, and cards
- Proprietary ML models trained on real-world dispute outcomes
open source escape hatches
- FingerprintJS (Open Source) MIT
- Ruru Engine Apache-2.0
- Zen Engine MIT
Questions people ask
Which is easier to rebuild with AI, FraudLabs Pro or Vesta?
FraudLabs Pro. It scores 6/10 on vibe code with a moat of 6/10, so an AI-assisted MVP takes about 3 days and a full replacement about Never fully replaceable without proprietary cross-merchant fraud signals.
Which one costs less, FraudLabs Pro or Vesta?
FraudLabs Pro at $99/mo/mo for a typical mid-market store. The gap between the two is about $16,812/mo a year.
What do I lose if I replace FraudLabs Pro?
Access to a global cross-merchant blacklist of malicious buyers and emails Proprietary real-time IP reputation, proxy, VPN, and TOR exit node detection data Cross-site device fingerprinting telemetry
What do I lose if I replace Vesta?
100% zero-liability chargeback reimbursement guarantee Cross-merchant consortium dataset tracking fraudsters across thousands of stores Machine learning models trained on billions of historical card transactions
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