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The Kubernetes Day 2 Gap is Real

In the rush to adopt cloud native architectures, organizations couldn’t wait to realize the promise of speed, agility,

scalability, and a better user experience. Kubernetes and containers play a key role in underpinning these architectures, but they also bring added complexity and the need for new skills that are in short supply. Going from Day 1 (deployment) to Day 2 (management, monitoring, and maintenance) is where the rubber meets the road. Unfortunately, it’s also where many organizations see their investments in Kubernetes failing to deliver the value they were after. So, what makes Day 2 operations so challenging?

  • Costs to run applications can exceed expectations

  • The number of apps actually being deployed falls well short of the anticipated number

  • You build it, but users don’t come

  • SLAs can’t be met

It’s no wonder that 94% of respondents claim that Kubernetes is a source of pain or complexity for their organization, according to a D2iQ survey.

Ramping Up to Production Brings Choices

Ramping up to Day 2 operations requires you to acquire knowledge and information quickly, but you don’t know how applications are going to operate in production. You need to make choices that can impact applications, users, and your organization, and every choice you make will have cost, performance, and business implications:

  • Over-provisioning cloud resources means driving costs through the roof

  • Poor application performance and availability degrades the user experience

  • Manually tuning your complex Kubernetes environment slows time-to-market

Of course, you can always deploy applications and learn from there, but every app is different, and you need as many iterations as possible to optimize. Doing this manually at scale is not viable or operationally efficient. Fortunately, machine learning and automation can make things easier.

Optimizing Kubernetes App Testing and Optimization in Pre-Deployment and Production

Taking advantage of ML and automation in pre-production and production helps ensure that applications run well and meet, or exceed, performance SLOs and SLAs with minimal effort and cost. This means taking an approach that lets you understand app behavior by experimenting in pre-production while also making adjustments in production based upon data that provides actionable insights.

By addressing Kubernetes application testing and optimization, StormForge informs, optimizes and operates throughout the cloud-native development cycle. This gives developers and operations managers an intelligent and comprehensive platform that maximizes the return on Kubernetes investments and delivers on the promise of Kubernetes and cloud native.

StormForge helps eliminate the Day 2 gap so you can:

  • Spend your time innovating and automating, not tuning, tweaking, and troubleshooting Kubernetes

  • Empower application owners and effortlessly ensure cloud native applications are always operating at peak efficiency

  • Optimize Kubernetes resource utilization while reducing work and errors in day 2 operations

Explore how StormForge dramatically increases operational efficiency in cloud native environments by automatically providing the best application performance with ML.

Let’s talk.

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