Prevent OOM kills and CPU throttling before they happen
Static thresholds fire too late, or too often. Sedai learns how each workload normally behaves, spots the trends heading toward trouble, and adds memory or CPU headroom before your users notice.
Animation: with a static memory limit, a workload’s memory climbs until it hits the limit and is OOM killed, twice. With Sedai, the same climb is detected early, its trend is projected to cross the limit, and the limit is raised before it does. Memory keeps rising safely below the new limit, and there are no OOM kills.
By the time the alert fires, users already noticed
Most Kubernetes reliability tooling watches where a metric is. The problems that page you at night are about where it’s heading.
OOM kills arrive without warning
A static threshold sees memory at 70% and stays quiet, even when it’s climbing fast. The first real signal is the pod restarting.
Throttling hides in averages
Kubernetes enforces CPU limits in short intervals, so containers get throttled during bursts while average CPU looks fine and latency quietly climbs.
Every fix waits for a human
Someone has to get paged, find the cause, raise the limit, and redeploy. Meanwhile the incident keeps running.
From reactive alerts to autonomous prevention
Sedai monitors the trajectory of every workload and acts before a trend becomes an outage.
OOM prevention
Detects upward memory trends in pods 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 relief
Keeps a leaking service available with temporary relief, so your team can find the root cause without an active incident.
Trend-based alerting
Replaces noisy static thresholds with trajectory-aware signals: fewer false positives, earlier warning on the risks that matter.
Every remediation checked before and after
Before Sedai acts, a remediation has to pass a series of safety checks, and if any fail, it isn’t attempted. After it acts, Sedai verifies the result and rolls back automatically if risk appears.
Cut padding without the fear
Teams pad requests and limits because they’re afraid of OOM kills and throttling. Sedai’s vertical scaling works in both directions: it removes padding a workload doesn’t need and adds headroom the moment a workload does. Savings and reliability come from the same system.
Pod Rightsizing
Right-size requests continuously, knowing headroom comes back when it's needed
Proactive Incident Prevention
Sedai prevents incidents across Kubernetes, serverless, and more
Safety By Design
Safety checks, verification, and rollback for every autonomous action
Static alerts vs. Sedai
One tells you something broke. The other makes sure it doesn’t.
| Static thresholds and alerts | Sedai |
|---|---|
Fires after the threshold is crossed | Acts on the trend before it crosses |
Pages a human to diagnose and fix | Remediates autonomously, after safety checks |
Noisy thresholds, frequent false positives | Trajectory-aware signals, fewer false positives |
Limits stay padded for fear of OOM kills | Headroom added only when it’s needed |
Throttling hidden in average CPU | Containers near CPU limits get headroom first |
Find the incidents waiting to happen
See which of your workloads are trending toward their limits right now.
Frequently asked questions
For stateless Kubernetes workloads, Sedai detects errors, out-of-memory and CPU pressure, and timeouts, based on a learned baseline of each workload’s normal behavior.
Depending on the risk, Sedai increases memory limits, expands CPU headroom, or scales capacity, without waiting for someone to respond to a page.
Yes. Availability starts in recommend mode by default, so Sedai flags issues and proposes fixes for you to approve. Switch a resource type to autonomous mode when you’re ready, or turn monitoring off.
The remediation isn’t attempted. It’s recorded as unsafe to act, so your team can see what Sedai found and decide what to do.
Sedai provides temporary relief that keeps a leaking service available while your team diagnoses the root cause, so you can fix it without an active incident.
Not with Sedai. The same vertical scaling that removes unneeded padding adds memory or CPU back as soon as a workload’s trend calls for it, and every change is verified with automatic rollback.
Sedai reports zero production incidents caused by its autonomous actions. Every change is incremental, verified before, during, and after execution, and rolled back automatically if risk is detected.