battles / Fraud
Ravelin vs Sift
Ravelin ($2,500/mo/mo, vibe code 3/10) vs Sift ($12,500/mo/mo, vibe code 4/10). Sift 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
Fraud
$12,500/mo/mo
- MVP
- 2-3 weeks
- Full replacement
- 9-18 months
easier to rebuild
get the build prompt →price gap / year
$120,000/mo
running both / year
$180,000/mo
our call
Start with Sift — 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
Sift
An AI coding agent can write a custom rule engine, device fingerprinting intake, and XGBoost fraud scoring API in a couple of weeks. However, you cannot replicate Sift's global data network, which uses cross-merchant behavioral signals across 34,000+ sites to stop fraud on arrival.
you can rebuild
- Client-side JS snippet integration for basic device signal capture
- Custom rule builder for block/allow lists (e.g., country code matching, velocity limits)
- Real-time scoring API endpoint returning risk numbers (0-100)
- Manual investigation review queue console for fraud analysts
- Shopify and Magento webhook trigger integrations
what you lose
- Access to Sift's global consortium data tracking over 1 billion unique digital personas
- Pre-trained cross-merchant Account Takeover (ATO) and identity theft detection models
- Sift ActivityIQ generative AI pattern detection across global network events
- Out-of-the-box integrations with major enterprise processors (Adyen, Stripe, PayPal) and platforms
- Automated continuous machine learning retraining on global chargeback data
real moats
- Global Consortium Network: Shared signal intelligence across thousands of global enterprises processing 70B+ events monthly.
- Petabyte-Scale Behavioral Graph: Historical link analysis linking IPs, device hashes, credit card bins, and shipping addresses across decades.
- Zero-Day Fraud Vector Detection: Ability to detect novel botnet and account takeover patterns before individual merchants experience them.
open source escape hatches
- FraudLabs / open rules engines (Drools) Apache-2.0
- Feedzai alternatives — River BSD-3
- MaxMind minFraud open clients Apache-2.0
Questions people ask
Which is easier to rebuild with AI, Ravelin or Sift?
Sift. It scores 4/10 on vibe code with a moat of 9/10, so an AI-assisted MVP takes about 2-3 weeks and a full replacement about 9-18 months.
Which one costs less, Ravelin or Sift?
Ravelin at $2,500/mo/mo for a typical mid-market store. The gap between the two is about $120,000/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 Sift?
Access to Sift's global consortium data tracking over 1 billion unique digital personas Pre-trained cross-merchant Account Takeover (ATO) and identity theft detection models Sift ActivityIQ generative AI pattern detection across global network events
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