Lacework (now FortiCNAPP) vs Alloy.ai
Detailed side-by-side comparison to help you choose the right tool
Lacework (now FortiCNAPP)
Data Analysis
AI-powered cloud-native application protection platform providing behavioral threat detection, compliance monitoring, and vulnerability management across multi-cloud environments
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Starting Price
$50,000/yearAlloy.ai
Data Analysis
Demand and inventory control tower for consumer brands providing insights and analytics.
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Starting Price
CustomFeature Comparison
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Lacework (now FortiCNAPP) - Pros & Cons
Pros
- ✓Polygraph behavioral engine automatically baselines normal activity and surfaces anomalies without requiring teams to write and maintain detection rules, dramatically reducing tuning overhead
- ✓Unified CNAPP consolidates CSPM, CWPP, CIEM, Kubernetes security, and vulnerability management into a single platform, replacing multiple point tools and their separate licenses
- ✓Agentless cloud scanning provides rapid time-to-value across AWS, Azure, and GCP accounts, with deeper eBPF agent-based runtime protection available for critical workloads
- ✓Strong attack path analysis correlates vulnerabilities, misconfigurations, and identity risks to prioritize the handful of exposures that actually create exploitable chains
- ✓Post-acquisition integration with the Fortinet Security Fabric enables unified visibility between cloud workload telemetry and network/endpoint security data
- ✓Continuous compliance automation with prebuilt policy packs for PCI DSS, HIPAA, SOC 2, NIST, and CIS saves significant audit preparation effort
Cons
- ✗Enterprise-only pricing with no published tiers or self-serve options makes it inaccessible for smaller teams and creates friction for evaluation
- ✗Brand transition from Lacework to FortiCNAPP has created documentation inconsistencies, confusion about product roadmap, and uncertainty for existing customers during integration
- ✗Initial deployment and onboarding across multi-cloud environments can be complex, particularly when tuning Polygraph baselines for noisy or highly dynamic workloads
- ✗Alert quality improves substantially after several weeks of behavioral learning, meaning early-stage detection can produce false positives before baselines stabilize
- ✗UI and query experience, while improved, still lags behind more recent CNAPP entrants like Wiz in terms of intuitive navigation and graph exploration
Alloy.ai - Pros & Cons
Pros
- ✓Pre-built integrations with 100+ retailers, 3PLs, distributors, and ERPs eliminate the need to build custom data pipelines
- ✓CPG-specific data model harmonizes messy retailer data (Walmart Retail Link, Target Partners Online, Amazon Vendor Central) into a consistent schema
- ✓Acts as both a native analytics app (Lens) and a data platform that feeds Snowflake, Databricks, Tableau, and Power BI
- ✓Serves multiple teams (sales, supply chain, C-suite, IT) from the same underlying data, reducing internal data silos
- ✓AI-driven lost sales and out-of-stock insights help recover revenue that would otherwise go unnoticed
- ✓Industry-specific use cases (Target replenishment, excess retail inventory, promotion lift) are pre-configured rather than requiring custom builds
Cons
- ✗Enterprise-only pricing with no public tiers makes it inaccessible to small brands or those evaluating on a budget
- ✗Narrowly focused on consumer goods brands selling through retailers — not useful for DTC-only or non-CPG businesses
- ✗Requires meaningful data volume and retailer relationships to justify the investment
- ✗Implementation and onboarding typically require IT and analytics involvement rather than being truly self-serve
- ✗Website does not disclose specific customer counts, ROI benchmarks, or pricing ranges, making vendor comparison difficult
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