battles / Fraud
Ravelin vs Sardine
Ravelin ($2,500/mo/mo, vibe code 3/10) vs Sardine ($1,500/mo/mo, vibe code 3/10). Ravelin is the easier one to rebuild yourself — here is what you lose either way.
Fraud
$2,500/mo/mo
- MVP
- 2 weeks
- Full replacement
- 12-24 months, due to the need for continuous ML training, global consortium data, and fraud feedback loops
easier to rebuild
get the build prompt →Fraud
$1,500/mo/mo
- MVP
- 2 weeks
- Full replacement
- Never — requires proprietary global device graph data and ML models trained on billions of cross-merchant sessions.
price gap / year
$12,000/mo
running both / year
$48,000/mo
our call
Start with Ravelin — highest vibe code, weakest moat.
Ravelin
While you can easily build a basic heuristic rule engine with AI, you cannot replicate Ravelin's core value: cross-merchant network intelligence and trained machine learning models. Without shared fraud signals and historic chargeback ground truth, a self-built system will either bleed money to bad actors or block real sales.
you can rebuild
- Static rule-based order evaluation engine
- Manual order review dashboard and queue management
- Basic velocity checks (e.g., requests per IP or email)
- Simple email domain and IP geolocation lookups
- Basic webhook notifications for suspicious orders
what you lose
- Global consortium network signals identifying known fraud actors across merchants
- Client-side JavaScript and mobile SDK device fingerprinting and behavioral telemetry
- Automated machine learning models trained on historical chargeback feedback loops
- Dynamic 3D Secure (3DS2) exemption routing to optimize checkout conversion rates
- Dedicated graph visualization engine for linked accounts and fraud rings
real moats
- Global network effect: signals from one merchant immediately protect all other network merchants
- Proprietary training dataset derived from millions of verified chargeback ground truth labels
- Sub-100ms real-time evaluation infrastructure integrated directly into payment pipelines
open source escape hatches
- Apache PredictionIO Apache-2.0
- PostHog MIT
- Neo4j Community Edition GPL-3.0
Sardine
Sardine relies on low-level device telemetry (typing cadence, accelerometer data, canvas fingerprinting) cross-referenced against millions of global transactions. An AI-built alternative lacks the cross-merchant data consortium required to catch sophisticated fraud rings and stolen credentials.
you can rebuild
- Custom static risk rules (e.g., flag orders over $500 with mismatched billing/shipping)
- Basic IP geolocation and proxy/VPN detection checks
- Dashboard UI for manual order review and approval workflows
- Basic velocity checking (e.g., card attempts per IP per hour)
- Email domain validation and disposable email domain checks
what you lose
- Global cross-merchant device fingerprinting and risk identity graph
- Behavioral biometrics tracking (hesitation, typing cadence, paste detection, mouse movement patterns)
- Real-time bank account and crypto-onramp risk intelligence
- Automated machine learning models trained on millions of fraud outcomes
- Zero-day fraud attack pattern detection across peer networks
real moats
- Cross-merchant shared identity and fraud intelligence graph
- Patented behavioral biometric feature extraction algorithms
- Integrated AML, sanctions screening, and chargeback guarantee coverage
open source escape hatches
- FingerprintJS Open Source BNSL-1.1
- MaxMind GeoIP2 Apache-2.0
- FraudLabs Pro SDK MIT
Questions people ask
Which is easier to rebuild with AI, Ravelin or Sardine?
Ravelin. It scores 3/10 on vibe code with a moat of 8/10, so an AI-assisted MVP takes about 2 weeks and a full replacement about 12-24 months, due to the need for continuous ML training, global consortium data, and fraud feedback loops.
Which one costs less, Ravelin or Sardine?
Sardine at $1,500/mo/mo for a typical mid-market store. The gap between the two is about $12,000/mo a year.
What do I lose if I replace Ravelin?
Global consortium network signals identifying known fraud actors across merchants Client-side JavaScript and mobile SDK device fingerprinting and behavioral telemetry Automated machine learning models trained on historical chargeback feedback loops
What do I lose if I replace Sardine?
Global cross-merchant device fingerprinting and risk identity graph Behavioral biometrics tracking (hesitation, typing cadence, paste detection, mouse movement patterns) Real-time bank account and crypto-onramp risk intelligence
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