Query.AI Federated Detections vs Sesame IT HOSHI: Side-by-Side Comparison (2026)

Short answer

Mid-market and enterprise security teams with fragmented data across multiple SIEMs, data lakes, and cloud platforms will get the most from Query.AI Federated Detections because it runs threat hunts and detections without forcing you to centralize or ingest everything into a single repository. The library of 1,000+ pre-built FSQL recipes lets you start detecting in days rather than months of tuning custom correlation rules. Skip this if your organization has already consolidated on a single SIEM with deep historical retention and wants tight coupling to your existing detection workflow; Query.AI shines when data governance or cost makes centralization impractical, not when you already have it. Mid-market and enterprise security teams that struggle converting threat intelligence into actionable detection rules will benefit most from Sesame IT HOSHI, which automates that translation and feeds industry-specific attack scenarios directly into Jizô AI's detection engine. The daily-updated use case library means your detection coverage stays current without forcing analysts to manually build rules from intelligence reports. Not ideal for organizations seeking a standalone threat intelligence platform; HOSHI is purpose-built as a detection rule factory for teams already committed to the Jizô AI ecosystem.

Based on our analysis of core features, integrations, company size fit, deployment model, here is our conclusion: