How Palo Alto Networks Takes Control of Its High-Stakes Cloud
Learn how Palo Alto Networks dramatically reduced its cloud costs with Sedai
Fix Problems Before Users Find Them
Static alerting thresholds fire too late, too often, or both, leaving your team in permanent reaction mode. Sedai analyzes real-time resource trends to identify what's about to go wrong, and autonomously intervenes before it becomes an incident. Shift from firefighting to prevention without adding headcount.
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From Reactive Alerts to Autonomous Prevention
Sedai doesn't wait for thresholds to breach. It monitors the trajectory of your workloads in real time and acts before a trend becomes an outage.
Establish What Normal Looks Like
Sedai monitors your workload metrics over time to build a precise baseline of normal behavior, so it knows the difference between high utilization and a genuine risk.
Predict What's About to Go Wrong
Regression models analyze the direction of resource usage, not just the current level. Sedai flags a memory trend climbing from 40% to 80% in minutes, even if it hasn't crossed a threshold yet.
Intervene Before Users Are Impacted
When Sedai confirms a risk, it autonomously remediates — increasing memory limits, expanding CPU headroom, or scaling capacity — without waiting for a human to respond.
“By having Sedai in place, we’re not just saving money, we’re preventing would-be customer problems before they become an issue.”
Matt Duren
VP of Engineering // KnowBe4
Key Capabilities
Sedai covers the full spectrum of availability risks — from resource exhaustion to application-level failures — with autonomous remediation at every layer.
OOM Prevention
Detects upward memory trends in Kubernetes pods and Lambda functions and increases allocation before an out-of-memory event occurs.
CPU Throttling Prevention
Identifies containers approaching their CPU limits and expands headroom before performance degrades.
Memory Leak Mitigation
Provides autonomous temporary relief for memory leaks — keeping services available while your team diagnoses the root cause, without the pressure of an active incident.
Trend-Based Alerting
Replaces noisy static thresholds with trajectory-aware signals — fewer false positives, and early warning on the risks that actually matter.
The Case for Autonomous Action
85%
of incidents are caused by human error
0
Production incidents ever caused by a Sedai autonomous action
30%
Average reduction in availability incidents for Sedai customers
Resources
The Bottleneck Test | Vinay Pereti
Vinay Perneti (VP of Eng at Augment) shares the Bottleneck Test: a framework for leaders to empower their teams when they become the blocker.
Why Your ML Training Data Fails in Production
On this episode of 1 IDEA, Suresh Mathew sits down with Eugene Fratkin (VP of Engineering @ Salesforce) to break down why ML training data fails in production and what to validate before you ship.
the cloud that can't afford to fail
Suresh Sangiah, SVP of Engineering at Palo Alto Networks, explains how his team keeps its cloud resilient, always on, and with zero wasted costs.
Stop Responding to Incidents.
Start Preventing Them.