battles / Fraud
FraudLabs Pro vs NoFraud
FraudLabs Pro ($99/mo/mo, vibe code 6/10) vs NoFraud ($250/mo/mo, vibe code 5/10). 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
$250/mo/mo
- MVP
- 2-3 weeks
- Full replacement
- 12-18 months
price gap / year
$1,812/mo
running both / year
$4,188/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
NoFraud
NoFraud (Wyllo) is fundamentally an insurance product wrapped in an API. While the order scoring and Shopify tagging features can be cloned with standard LLM tools in a couple of weeks, you cannot write code that underwrites thousands of dollars in credit card chargebacks.
you can rebuild
- Automated order risk scoring based on standard rules (IP, distance, proxy, email).
- Shopify order tagging and automatic cancellation API triggers.
- Manual order review dashboard with signal visualizations.
- Basic velocity and heuristic-based risk engine.
what you lose
- 100% financial reimbursement guarantee on fraudulent chargebacks passed by the engine.
- 24/7 human analyst team conducting manual order reviews on borderline transactions.
- Network-level risk detection trained on shared cross-merchant fraud signals.
- Direct chargeback dispute handling and representment operations.
real moats
- Balance sheet capital to guarantee chargeback reimbursements at scale.
- Operational infrastructure of human fraud analysts performing 24/7 manual reviews.
- Consolidated network intelligence across thousands of high-volume e-commerce storefronts.
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, FraudLabs Pro or NoFraud?
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 NoFraud?
FraudLabs Pro at $99/mo/mo for a typical mid-market store. The gap between the two is about $1,812/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 NoFraud?
100% financial reimbursement guarantee on fraudulent chargebacks passed by the engine. 24/7 human analyst team conducting manual order reviews on borderline transactions. Network-level risk detection trained on shared cross-merchant fraud signals.
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