Palo Alto Networks, Inc.

Record Q3 performance; strong bookings momentum + guidance raised - Company delivered a record quarter and surpassed every guided metric. - NGS ARR: $8.13B (+60% YoY) (the call highlights this as the biggest outperformance vs guidance to date). - RPO: $18.4B (+36% YoY); organic NGS ARR +28% YoY, organic RPO +22% YoY. - Revenue: $3.0B (+31% YoY); product revenue $594M (+31% YoY), services $2.4B (+31% YoY). - Key Q4 FY26 outlook (raised by company): - Q4 NGS ARR: $8.9B–$8.95B (+59% to +60% growth) - Q4 RPO: $20.9B–$21.0B (+32% to +33% growth) - Q4 revenue: $3.345B–$3.355B (+32% growth) - Q4 diluted non-GAAP EPS: $0.96–$0.98 - FY26 outlook (raised): - NGS ARR: $8.9B–$8.95B (+59%–60%) - RPO: $20.9B–$21.0B (+32%–33%) - Revenue: $11.415B–$11.425B (+24%) - Operating margin: 28.9%–29.2% - Adjusted free cash flow margin: 37.5% (and EPS $3.77–$3.79 non-GAAP)

Platformization flywheel is the core story (and it’s working) - 110 net new platformizations in Q3, including 20 from the CyberArk + Chronosphere integrations. - Total platformized customers: ~2,280; management emphasizes these are deep architectural commitments. - Among platformized cohort: ~120% net retention and single-digit churn, supporting the idea that integration drives durable expansion. - Management expects 4,000 platformizations by fiscal 2030, tied to their path toward the $20B NGS ARR target.

AI is accelerating security demand, but customers are shifting to “real-time” enforcement - Strong narrative that AI reduces attack timelines (minutes instead of months) and increases the need for run-time, in-line defense. - Network security positioned as the structural beneficiary of AI-driven traffic + higher inspection requirements. - Hardware remains a key upside lever: next-gen firewall bookings nearly +40% YoY, with early adoption including sovereign infrastructure providers. - Prisma AIRS called out as fastest-growing product historically: - 300+ Prisma AIRS customers in Q3 (up from ~100 at end of Q2) - Management cites strong enterprise shift from experimental to production AI workloads.