As organizations manage their digital transformation initiatives in today’s business world, their technology investments are often viewed under a microscope. Do they align with strategic objectives? Do they support the company’s innovation goals? Will they pose a business risk? And of course, how cost effective is the investment? Is it a financially responsible choice and what’s the ROI? When it comes to costs associated with protecting an organization’s vital resources, infrastructure, and data, many CTOs, CISOs, and CIOs have to weigh the cost of investment in new technologies vs relying on legacy systems that actually increase their exposure to nefarious actors.

The simple truth is that the typical security methods employed by many enterprises today often come with hidden costs: potential for costly breaches, increased vulnerability, wasted man-hours, and operational inefficiencies.  This is where HashiCorp Vault and Boundary come in. When leveraged together they deliver a host of benefits, including cost optimization, streamlined workflows, simplified compliance, and of course they reduce the risk and minimize the financial impact of security incidents.

Understanding the Traditional Cost Conundrum

Before exploring how Vault and Boundary save money, let’s examine the cost burdens associated with traditional security approaches:

Cost-Saving Advantages of Vault and Boundary

By integrating Vault and Boundary, organizations can unlock cost-saving benefits without compromising their organization’s security posture:

  1. Reduced Administrative Burden:
  1. Enhanced Efficiency and Productivity:
  1. Simplified Compliance Management:
  1. Indirect Cost Savings:

Long-Term Value Proposition:

While the upfront costs of acquiring and implementing Vault and Boundary should be considered, the opportunity for cost savings over the long term through increased efficiency, reduced downtime, improved security, and simplified compliance makes the investment financially sound.

By implementing HashiCorp Vault and Boundary together, organizations can optimize their financial investment in both platforms. Through automation, centralization, and streamlined workflows, these powerful tools empower organizations to achieve a balance between robust security and financial sustainability, paving the way for an organization to achieve long-term success in the ever-evolving digital world. 

Need help maximizing the benefits of using Vault & Boundary? Contact the experts at RPT. As HashiCorp’s 2023 Global Competency of the Year and the only HashiCorp partner with all 3 certifications (Security, Infrastructure, & Networking), you know you’re working the leading HashiCorp services partner. Contact today. 

About River Point Technology

River Point Technology (RPT) is an award-winning cloud and DevOps service provider that helps Fortune 500 companies accelerate digital transformation and redefine what is possible. Our passionate team of engineers and architects simplify the deployment, integration, and management of emerging technology by delivering state-of-the-art custom solutions. We further position organizations to experience Day 2 success at scale and realize the value of their technology investments by offering best-in-class enablement opportunities. These include the subscription-based RPT Resident Accelerator program that’s designed to help enterprises manage the day-to-day operations of an advanced tech stack, the just-launched RPT Connect App, and our expert-led training classes. Founded in 2011, our unique approach to evaluating and adopting emerging technology is based on our proprietary and proven Value Creation Technology process that empowers IT teams to boldly take strategic risks that result in measurable business impact. What’s your vision? Contact River Point Technology today and see what’s possible.

By, Samuel Cadavid, Senior Solutions Consultant


In the dynamic world of cloud computing, Kubernetes has emerged as a frontrunner in orchestrating containerized applications. However, as the complexity and scale of deployments grow, managing resources efficiently becomes a daunting task. This is where Artificial Intelligence (AI) steps in, revolutionizing how we approach Kubernetes management, specifically in in-place pod resizing, vertical and horizontal scaling, and power-aware batch scheduling. During my recent trip to KubeCon, I was able to attend a session hosted by Vinay Kulkarni (eBay) and Haoran Qiu (UIUC) that delved into this topic, including how cluster autoscaler currently handles pods pending due to insufficient resources, changes to the autoscaling workflow that right-sizes over-provisioned pods, and the latest research that leverages machine learning to achieve multi-dimensional autoscaling. The session got me thinking about how AI plays a role in pod-resizing, and it inspired me to keep digging into it afterward.  

AI-Driven In-Place Pod Resizing 

Traditionally, resizing pods in Kubernetes meant recreating them with the new size specifications. This process, while effective, leads to downtime and potential service disruptions. AI-driven in-place pod resizing changes this narrative.

How it Works 

AI algorithms continuously monitor the resource usage patterns of each pod. When a pod requires more resources, AI predicts this need and dynamically adjusts CPU and memory allocations without restarting the pod. This approach minimizes downtime and ensures that applications scale seamlessly with fluctuating demands. 




Vertical and Horizontal Scaling: AI at the Helm 

Vertical Scaling with AI 

AI-driven vertical scaling involves adjusting the CPU and memory limits of a pod. Using predictive analytics, AI determines the optimal size for a pod based on historical data and current trends. This proactive resizing prevents resource exhaustion and improves performance. 

Horizontal Scaling with AI 

In horizontal scaling, AI plays a pivotal role in deciding when to add or remove pod instances. By analyzing traffic patterns, workload demands, and system health, AI can automate the scaling process, ensuring that the cluster meets the demand without manual intervention. 


Power-Aware Batch Scheduling with AI 

Energy efficiency is becoming increasingly important in data center operations. AI-driven power-aware batch scheduling in Kubernetes is a game-changer in this realm.

The Concept

This approach involves scheduling batch jobs in a manner that optimizes power usage. AI algorithms analyze the power consumption patterns of nodes and schedule jobs on those consuming less power or during off-peak hours, significantly reducing the overall energy footprint. 


The integration of AI into Kubernetes management is not just a trend; it’s a necessity for efficient, cost-effective, and sustainable operations. AI’s role in in-place pod resizing, vertical and horizontal scaling, and power-aware batch scheduling marks a significant leap towards smarter, more autonomous cloud infrastructures. As we continue to embrace these AI-driven strategies, we pave the way for more resilient, responsive, and responsible computing environments. Here, I’ve only scratched the surface of what AI can do for Kubernetes management. As technology evolves, we can expect even more innovative solutions to emerge, further simplifying and enhancing the way we manage cloud resources. 


Watch the full lecture here!

By Dan Quackenbush


In the dynamic world of tech conferences, there exists a gem unlike any other – KubeCon, where the orchestrators of Kubernetes gather to share tales of triumph and innovation. As a seasoned navigator of cluster administration, I found myself immersed in the heartbeat of this dynamic symphony of ideas, particularly drawn to the stories that unfolded after the initial deployment – the fascinating Day 2 Operations. 

The first stop, Major League Baseball + Argo CD: A Home Run, implemented GitOps through Argo CD to empower feature-driven development using Helm charts. The stage was set with a compelling case study, highlighting how developers were handed the reins to their applications without drowning in the sea of YAML configurations. Through the power of abstraction, Helm deployed through Argo, allows them to focus on features, such as enabling monitoring, injecting secrets, and exposing their services across two hundred clusters, bringing consistency to the service runtime landscape. 

Next up was a talk on the alpha feature introduced in Kubernetes 1.27 – In-Place Resource Resize. A meaningful change for administrators dealing with dynamic resource-intensive applications, especially those built on JVM. Sustainable Scaling of Kubernetes Workloads with In-Place Pod Resize and Predictive AI, unfolded the power of dynamically adjusting pod sizes, unveiling a new level of flexibility for Kubernetes clusters. It was not about vertical scaling; but talked about how we could use ML to power these configurations without over-allocating resources. 


The talk FinOps at Grafana Labs illuminated the path to financial accountability, transforming it into a cultural cornerstone. The speaker painted a vivid picture of a world where accountability, transparency, and a culture of openness were the guiding lights. Through real-world examples, the audience learned the impact of “cash positive chaos testing,” moving to spot instances, aligning cost optimization measured against service reliability, and the importance of continuously stress-testing applications in various infrastructure conditions. 


In a creative twist, Burden to Bliss: Eliminate Patching and Upgrading Toil with Cluster Autoscaler at Scale, dived into leveraging Cluster Autoscaler for applying security patches. The ingenious strategy involved creating new node pools and strategically shifting pods with tolerations to the patched nodes. By creating new node pools with the patched system and strategically forcing a single pod with toleration to the patched node, the talk demonstrated how eventual consistency mechanisms could be leveraged to shift all pods to the new node through eviction. This innovative strategy ensures that security patches are seamlessly applied without affecting ongoing workloads. 


The final act in this symphony of talks explored the intersection of Kubernetes, service mesh, and content delivery networks (CDNs). Take It to the Edge: Creating a Globally Distributed Ingress with Istio & K8gb, unveiled the critical role of service mesh in handling disruptions during DNS load balancing, offering a solution to the dreaded 502 errors. Through health checks and local failover endpoints, the talk unfolded how Kubernetes Global Balancer could redefine CDN construction, providing a resilient and scalable solution for distributed applications. 


It is always interesting to hear how people are handling similar problems. These talks show us, how as a Kubernetes administrator, with a new mindset, can provide a central way for developers to be feature vs configuration driven, scale those workloads either in place, or through cheaper means, all while reducing the burden on sustainability. Once deployed, these applications can then spread across regions, on hardened nodes. I invite you to check out the talks, dive into the dynamic Day 2 Operations, and discover the secrets shared by industry leaders.