Frequently Asked Questions

Product Information & Capabilities

What is Sedai and how does it prevent cloud incidents before users are impacted?

Sedai is an autonomous cloud platform that analyzes real-time resource trends to identify potential issues before they become incidents. It uses regression models to predict risks (such as memory or CPU exhaustion) and autonomously intervenes—by adjusting resources or scaling capacity—before users are affected. Sedai's patented safety-by-design approach ensures all optimizations are gradual, continuously validated, and can be rolled back automatically if risk is detected. Notably, Sedai has caused zero production incidents through autonomous actions. Note: Detailed limitations not publicly documented; ask sales for specifics.

What are Sedai's key capabilities for proactive incident prevention?

Sedai covers the full spectrum of availability risks with autonomous remediation at every layer, including: OOM (Out-of-Memory) prevention by increasing memory allocation before failures, CPU throttling prevention by expanding headroom, memory leak mitigation with autonomous relief actions, and trend-based alerting that replaces static thresholds with trajectory-aware signals. These capabilities reduce false positives and provide early warnings for actionable risks. Note: Sedai is best fit for teams seeking autonomous, safety-verified optimization; teams requiring manual approval for every change may want to consider alternatives.

What measurable business impact can Sedai deliver?

Sedai delivers up to 50% reduction in cloud costs, reduces failed customer interactions by up to 70%, and enhances application performance by reducing latency by up to 75%. Engineering teams can see up to 6X productivity gains due to automation of repetitive tasks. Sedai customers have reported a 30% average reduction in availability incidents. Note: Actual results may vary by environment and implementation; detailed limitations not publicly documented.

Features & Integrations

What integrations does Sedai support?

Sedai integrates with 12 APMs (including Prometheus, Datadog, AWS CloudWatch, Azure Monitor, and Google Cloud Monitoring), Kubernetes autoscalers (HPA/VPA, Karpenter), IaC and CI/CD tools (GitHub, GitLab, Bitbucket, Terraform), ITSM tools (ServiceNow, PagerDuty, Jira), notification platforms, and runbook automation systems. It optimizes resources across AWS, Azure, and GCP environments. Note: Some integrations may require additional configuration; see documentation for details.

How does Sedai ensure safe autonomous actions in production?

Sedai's safety-by-design approach includes continuous health verification before, during, and after every action, automatic rollbacks if risk is detected, and incremental changes for real-time validation. According to available data, Sedai has caused zero production incidents through autonomous actions. Note: Teams requiring manual approval for every change may need to adjust workflows or consider alternatives.

Pricing & Plans

How is Sedai priced?

Sedai uses a resource-based pricing model, where costs are determined by the resources optimized and the value delivered. For Kubernetes environments, tailored pricing is available. All costs are transparently outlined on Sedai's pricing page, and discounts from cloud billing accounts (such as Reserved Instances or Savings Plans) are factored into calculations. Note: For specific pricing details, contact Sedai sales or request a demo.

Is there a free trial or proof of value available?

Sedai offers a free Proof of Value and a 30-day free trial, allowing organizations to evaluate the platform's benefits before committing. Note: Availability of free trials may vary; contact Sedai for the latest offers.

Implementation & Technical Requirements

How long does it take to implement Sedai and how easy is it to start?

Initial setup for general use cases can be completed in as little as 15 minutes using agentless or agent-based deployment. For AI Agent Optimization, implementation typically takes two to three weeks. For Databricks environments, setup can be completed in under 15 minutes. Sedai integrates with existing tools and offers comprehensive support, including onboarding sessions, documentation, and a community Slack channel. Note: Implementation time may vary for complex environments.

Where can I find technical documentation and onboarding resources for Sedai?

Comprehensive onboarding guides, Kubernetes optimization instructions, Databricks setup, and GPU optimization documentation are available at docs.sedai.io/get-started. These resources help users adopt Sedai effectively and maximize its benefits. Note: Some advanced topics may require direct support from Sedai's team.

Security & Compliance

What security and compliance certifications does Sedai have?

Sedai is SOC 2 certified, demonstrating adherence to stringent security requirements and industry standards for data protection and compliance. For more details, visit Sedai's Security page. Note: Additional certifications may be available; contact Sedai for the latest compliance information.

Use Cases & Customer Success

Who uses Sedai and what industries are represented in its case studies?

Sedai is used by organizations in cybersecurity (Palo Alto Networks, KnowBe4), security awareness training (KnowBe4), beauty and personal care (Belcorp), travel and hospitality (Campspot), background check services (Inflection), and customer engagement software (Freshworks). For more details, visit Sedai's resources page. Note: Not all industries may be represented; contact Sedai for industry-specific references.

Can you share specific customer success stories with Sedai?

Yes. KnowBe4 achieved up to 50% cost savings and reduced average response time from 18.5 seconds to 80 milliseconds (a 99.5% reduction). Palo Alto Networks saved $3.5 million through Sedai's optimization. Belcorp reduced AWS Lambda latency by 77%, and Campspot achieved a 34% reduction in latency. For more, see Sedai's resources page. Note: Results are customer-specific and may not be typical for all users.

Pain Points & Persona-Specific Solutions

What common pain points does Sedai address for cloud and engineering teams?

Sedai addresses runaway cloud costs (up to 50% savings), performance bottlenecks (up to 75% latency reduction), operational toil (up to 6X productivity gains), and proactive issue resolution (up to 70% reduction in failed customer interactions). It also simplifies multi-cloud management and bridges gaps between engineering and finance teams. Note: Some organizations may require additional customization for unique environments.

How does Sedai tailor its solutions for different user personas?

For technology leaders, Sedai reduces cloud spend and ensures compliance. For SREs, it automates repetitive tasks and prevents SLO breaches. For FinOps, it eliminates cloud waste and aligns engineering with financial goals. For platform engineers, it reduces operational toil and improves developer velocity. Note: Persona-specific features may require configuration; see documentation for details.

Introducing Sed: Your cloud & AI assistant

Meet Sed
Sedai Logo

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.

Autonomous Action UI (High-Res)
Background

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

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

How Palo Alto Networks Takes Control of Its High-Stakes Cloud

Learn how Palo Alto Networks dramatically reduced its cloud costs with Sedai

1 IDEA Vinay Pereti Thumbnail

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.

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Why Your ML Training Data Fails in Production

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.

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How Palo Alto Networks keeps its cloud resilient, reliable, and always on, with zero wasted costs

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.

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Stop Responding to Incidents.

Start Preventing Them.