KnowBe4 saved over $1.2 million in AWS cloud costs after adopting Sedai's autonomous optimization platform. This included a 27% overall reduction in cloud compute costs, with an average monthly savings of $81,000. In some ECS services, cost reductions reached up to 87% in development and up to 50% in production. [Source]
KnowBe4 achieved up to 99.5% latency reduction on AWS Lambda functions, with execution times dropping from 18.5 seconds to approximately 80 milliseconds. Overall, the team eliminated over 10,000 days of latency and saw up to 80% faster processing. [Source]
Sedai significantly reduced manual toil for KnowBe4's engineering team. 98% of approximately 9,500 services were running autonomously, and over 1,100 autonomous actions were executed in the first three months. This allowed engineers to focus on high-value work instead of repetitive optimization tasks. [Source]
No, Sedai executed millions of autonomous actions at KnowBe4 with zero incidents or outages. The platform's safety-by-design approach and phased implementation ensured production stability. [Source]
KnowBe4 achieved return on investment (ROI) in just five months after deploying Sedai's autonomous optimization platform. [Source]
KnowBe4 used a 'Crawl-Walk-Run' framework for implementation: starting with low-risk services, analyzing early results, and then scaling to all services and AWS accounts. This phased approach ensured safety and predictability. [Source]
Sedai was integrated into KnowBe4's CI/CD and GitOps workflows, ensuring that frequent deployments did not overwrite autonomous optimizations. This preserved Infrastructure as Code (IaC) as the source of truth while allowing Sedai to maintain optimizations automatically. [Source]
Sedai optimized KnowBe4's AWS ECS Fargate and AWS Lambda environments, focusing on service right-sizing, dynamic scaling, and application-aware optimization based on real workload behavior and latency. [Source]
Sedai's autonomous optimization allowed KnowBe4 to balance cost efficiency and performance by dynamically tuning resources, scaling to meet demand, and optimizing configurations based on real application behavior, all without risking production stability. [Source]
KnowBe4 saw up to 99.3% cost savings on individual Lambda functions, with a 31% cost decrease and 54% latency decrease for production Lambda functions. Execution times dropped from 18.5 seconds to about 80 milliseconds. [Source]
Sedai enabled KnowBe4's engineers to focus on high-value, innovative work by automating low-value, repetitive optimization tasks. This widened the impact of each SRE and allowed developers to concentrate on building new features. [Source]
KnowBe4's engineering leaders were initially concerned about letting a system make changes in production without human intervention, fearing potential outages. These concerns were addressed by Sedai's phased implementation, safety-by-design approach, and a short but rigorous test trial period, which resulted in zero incidents. [Source]
Sedai enabled KnowBe4 to scale its AWS environment efficiently by automating resource optimization across thousands of ECS services and Lambda functions, handling massive traffic volumes and frequent releases without manual intervention. [Source]
The 'Crawl-Walk-Run' framework is a phased approach where KnowBe4 started with low-risk services (Crawl), analyzed results and expanded to flagship products (Walk), and then rolled out autonomous optimization across all infrastructure (Run). This ensured safe and predictable adoption. [Source]
By integrating with KnowBe4's CI/CD and GitOps workflows, Sedai ensured that its autonomous optimizations were preserved and not overwritten during frequent deployments, maintaining continuous cost and performance improvements. [Source]
KnowBe4 improved several AWS metrics with Sedai, including 58% year-over-year ECS growth, 422% year-over-year Lambda growth, and 250 million+ daily Lambda invocations, all while reducing costs and latency. [Source]
Sedai allowed KnowBe4's SRE team to scale their impact by automating optimization tasks, reducing manual intervention, and enabling them to focus on higher-value engineering work. [Source]
In KnowBe4's development environment, Sedai delivered up to 87% cost reduction on some ECS services and an average reduction of ~35%, equating to about $390,000 annually. In production, ECS services saw up to 50% cost savings. [Source]
Within the first three months, Sedai executed over 1,100 autonomous actions at KnowBe4, with 98% of approximately 9,500 services running autonomously, demonstrating large-scale operational efficiency. [Source]
Sedai's autonomous cloud management platform uses machine learning to optimize cloud resources for cost, performance, and availability without manual intervention. It covers compute, storage, and data across AWS, Azure, GCP, and Kubernetes environments. [Source]
Key features include autonomous optimization, proactive issue resolution, full-stack cloud coverage, smart SLOs, release intelligence, plug-and-play implementation, multiple modes of operation (Datapilot, Copilot, Autopilot), enhanced productivity, and safety-by-design. [Source]
Yes, Sedai integrates with monitoring tools (Cloudwatch, Prometheus, Datadog, Azure Monitor), Kubernetes autoscalers, IaC and CI/CD tools (GitLab, GitHub, Bitbucket, Terraform), ITSM (ServiceNow, Jira), notification tools (Slack, Microsoft Teams), and runbook automation platforms. [Source]
Sedai offers three modes: Datapilot (observability), Copilot (one-click optimizations), and Autopilot (fully autonomous execution), allowing organizations to choose the level of automation that fits their needs. [Source]
Sedai integrates with Infrastructure as Code (IaC), IT Service Management (ITSM), and compliance workflows, ensuring all changes are safe, validated, and auditable. The platform is also SOC 2 certified. [Source]
Sedai is designed for platform engineers, IT/cloud operations, technology leaders, site reliability engineers (SREs), and FinOps professionals in organizations with significant cloud operations across industries such as cybersecurity, IT, financial services, healthcare, travel, and e-commerce. [Source]
Sedai addresses cost inefficiencies, operational toil, performance and latency issues, lack of proactive issue resolution, complexity in multi-cloud environments, and misaligned priorities between engineering and FinOps teams. [Source]
Customers can expect up to 50% cloud cost savings, up to 75% latency reduction, up to 6X productivity gains, reduced failed customer interactions by up to 50%, and improved release quality and reliability. [Source]
Sedai's case studies cover cybersecurity (Palo Alto Networks), IT (HP), financial services (Experian, CapitalOne Bank), security awareness training (KnowBe4), travel (Expedia), healthcare (GSK), car rental (Avis), retail/e-commerce (Belcorp), SaaS (Freshworks), and digital commerce (Campspot). [Source]
Sedai's setup process takes just 5 minutes for general use cases and up to 15 minutes for specific scenarios like AWS Lambda. More complex environments may vary. [Source]
Sedai offers plug-and-play implementation, agentless integration via IAM, personalized onboarding sessions, detailed documentation, a community Slack channel, and a 30-day free trial. [Source]
Sedai provides detailed technical documentation, onboarding support, a dedicated Customer Success Manager for enterprise customers, a community Slack channel, and email/phone support. [Source]
Yes, Sedai is SOC 2 certified, demonstrating adherence to stringent security and compliance standards for data protection. [Source]
Sedai offers 100% autonomous optimization, proactive issue resolution, application-aware intelligence, full-stack cloud coverage, release intelligence, and rapid plug-and-play implementation. Unlike competitors, Sedai operates autonomously and optimizes based on real application behavior. [Source]
Unique features include 100% autonomous optimization, proactive issue resolution before user impact, application-aware intelligence, release intelligence, and a quick setup process (5–15 minutes). [Source]
Em Kochanek
Content Marketing Manager
February 9, 2026
Featured
KnowBe4 is the global leader in Human and AI Risk Management. More than 70,000 organizations use its platform, which is built entirely on AWS.
Explosive growth drove massive cloud scale, making manual optimization unsustainable.
KnowBe4 moved from manual optimization to Sedai’s safe, autonomous optimization in production.
When it came to the cloud, KnowBe4 was a victim of its own success.
As the global leader in Human and AI Risk Management, the company saw its customer base grow rapidly to more than 70,000 organizations. But that success brought unprecedented scaling challenges in its AWS environment.
Matt Duren, VP of Engineering at KnowBe4, was leading the team bringing new services to market every single month and handling massive traffic volumes. At this point, he saw that scaling was non-negotiable.
“We're seeing tons of growth right now,” said Matt. “Just from our day-to-day, we peak out at thousands and thousands of requests per second."
Running entirely on AWS with the bulk of infrastructure on Lambda and Fargate ECS, the KnowBe4 Engineering teams were operating at a level they could no longer manage manually:
The frequent releases and high performance required for real-time cybersecurity delivery put immense pressure on the engineering team to optimize resources continually.
“My team doesn’t really have the luxury of spending time doing toil,” said Matt. “They can’t wait for a production release to see how it responds, and then wait for another team to optimize the service for them,” said Matt.
For Nate Singletary, Staff Site Reliability Engineer at KnowBe4, it was impossible to know whether ECS services were truly optimized. Engineers were stuck monitoring metrics & alerts while balancing under- & over-provisioning.
“If a service was running too low on memory or CPU, we’d see performance issues,” explained Nate. “But if it was running too rich, we were missing out on maybe hundreds or thousands of dollars of cost savings across several services.”
Needing a solution for continuous optimization, Sedai was an obvious solution.
As an autonomous optimization platform, Sedai reduces cloud costs, boosts performance, and improves availability. Its patented ML models learn real app behavior to make safe, production-aware optimizations.
To date, Sedai has executed millions of autonomous actions with zero incidents, making it a strong fit for teams struggling to keep up with growth.
But Matt had real concerns about letting a system make changes in production without human intervention.
“We needed something that we could take out of the hands of developers and put into the hands of an agent that would allow us to continue to scale our engineering teams,” said Matt. “Which is terrifying.”
That fear was real. Because availability is critical to KnowBe4’s platform, Matt’s engineers worried one bad optimization could immediately impact customers.
But building a team solely dedicated to optimization wasn’t viable. There was simply no way to scale the SRE team fast enough to keep up with KnowBe4’s growth.
Although initially skeptical about autonomous optimization, Sedai’s measured approach to implementation assured Matt it was a safe solution.
“We took a big bet that Sedai wouldn’t cause problems after a short but rigorous test trial period. And really, the proof is in the pudding there. We never had a major outage, that’s for sure.”
With the decision to adopt Sedai, KnowBe4 wanted to ensure engineering could optimize safely with clear guardrails and without putting customers at risk.
As part of the plan, the team intentionally wanted to validate real production traffic through Sedai so they could understand how Sedai behaved in real-world conditions.
To do this, the team took a phased approach to adopting full autonomy, allowing them to prove it was safe, predictable, and trustworthy before scaling further.
This approach became its “Crawl, Walk, Run” framework:
This strategy proved KnowBe4 could balance cost efficiency & performance across its environments, without risking production.
Sedai was deployed across KnowBe4’s two primary compute layers: ECS Fargate and AWS Lambda.
Its key optimization areas included:
With releases happening every ~20 minutes, the team wanted to ensure the frequent deployments did not overwrite the autonomous optimizations. To do that, KnowBe4 integrated Sedai into its existing CI/CD and GitOps workflows.
This approach preserved the IaC as the source of truth, while allowing Sedai to safely make and maintain optimizations. Engineers no longer had to manually update configs or worry about reverting optimizations during deployments.
“We have a full autonomous feedback loop,” said Nate.
With Sedai fully implemented, the optimization results were staggering.
“We achieved ROI in just five months. And Finance is very pleased about that,” said Matt. “If you look back in our savings history, it’s gone up by hundreds of thousands of dollars a year, even when we added new resources, accounts, services, and products.”
For cloud costs alone, the team was able to save 27% with Sedai’s autonomous optimization.
For Lambda functions specifically, the cost & performance improvements shot up to nearly 100%.
The performance impact was equally impressive. For one Lambda function serving real customer traffic, execution time dropped from 18.5 seconds to ~80 milliseconds (a 99.5% duration reduction) after Sedai autonomously adjusted memory allocation.
“By having Sedai in place, we’re not just saving money. We’re preventing would-be customer problems, before they become an issue,” said Matt.
Operational efficiency saw a massive boost, and engineers didn’t need to constantly tune services by hand anymore.
“Most of the engineering team probably doesn’t know what Sedai is at all. And I think that’s pretty great,” Matt explained. “They expect that optimization just happens for them because it does.”
This high level of autonomy significantly reduced the manual workload on the engineering team and enabled them to focus on work that matters.
“We've been able to widen the impact that each individual SRE has by not having to focus on that low-value work,” said Matt.
By adopting safe and autonomous cloud management with Sedai, KnowBe4 optimized its operations and laid the groundwork for future growth.
But beyond the day-to-day cost savings, one of the biggest value drivers was giving Matt’s developers the freedom to build and innovate.
“Builders want to focus on building,” Matt said. “What Sedai really lets us do is keep people focused on high-value work that they’re passionate about, while being a really good cost-saving tool.”
Cut your cloud costs. Boost performance. Let your engineers build.
