The 4 Biggest Pain Points of Monitoring Cloud Service Providers

The cloud is limitless – a key factor in its appeal – but it also introduces a new set of problems. Enterprises are looking to innovate at rapid speed, to achieve business outcomes, and deliver superior user experiences. But the majority of enterprises need to continue to maintain complex hybrid infrastructures as well as diversify their cloud strategies across multi-cloud environments. What starts as a simple cloud project or initiative quickly becomes an exponentially complex system of hybrid multi-cloud architecture with hundreds of different cloud services in IaaS, PaaS, and SaaS forms.

Running cloud operations is a multi-domain collaboration. NetworkOps, DevOps, SREs, SecOps, and ITOps, all need equal access, cross-functional visibility, and common data sets. Without this, teams end up operating in silos, across disparate functions, without consolidation and visibility. This hinders organizations on their path to the cloud – leaving businesses with a hefty price to pay for the cost of their innovation and desire for extensibility. In this blog, we outline the top pain points for enterprises leveraging cloud services, and how to avoid them.

Pain Point 1: Native tools that don’t talk to each other 

Each cloud service provider offers a native set of tools to access and monitor that perform only the most basic functions and are rarely easy to scale across teams. Modern enterprises with hybrid and multi-cloud infrastructure environments quickly amass a grab bag of tools that don’t communicate with each other. With different teams working in different systems – and data that won’t correlate across systems – silos are inevitable and any attempts to create a unified view of your entire infrastructure end up comparing apples to oranges. This prevents companies from being able to plan, scale, and optimize their cloud initiatives. With no accurate representation of what performance, spend, and system health actually look like, the entire business suffers.

Pain Point 2: You don’t know what you don’t know 

When issues inevitably arise, enterprises without hybrid observability find themselves continually reacting instead of getting ahead of potential problems. In the face of a surprise service disruption, the last thing you want to do is spend valuable time trying to track down the information you need in multiple systems. Even if you’re looking in the right place, the limited native tools aren’t providing the data you need to truly analyze and prevent the problem. Silos and uncorrelated data create risk. The only way to move to a proactive stance is to implement a system where service limits and performance monitoring can be analyzed side by side across your entire infrastructure.

Pain point 3: Cloud sticker shock 

“Limitless” cloud services shouldn’t mean limitless spend. Using disparate native tools prevents enterprises from being able to understand their infrastructure capacity as a whole, much less plan what spend should look like. With different teams spinning up instances in different systems, a lack of a centralized view makes governance almost impossible, leading to high operational costs with no clear actionable data to reduce them. Complicating matters more, with different teams operating with different budgets, CIOs and CTOs have no clear way to separate spend and ensure they’re getting the most out of their cloud investments. 

The bottom line is this: spend needs to be monitored alongside the health and performance of your systems. This is the only way to truly be able to plan for capacity and scalability. Without this, enterprises either end up overspending or overburdening their underlying infrastructure and other ops teams. 

Pain point 4: Manual processes for onboarding and monitoring 

New tools take time to learn, and with increasingly complex tech environments, it’s difficult to understand what you have, where you have it, and how it’s being used. Leveraging point tools for monitoring cloud environments limits enterprises’ ability to scale, optimize, and maintain their systems. And every time you introduce a new disparate tool, productivity plunges as teams work to manually complete onboarding, discovery, and monitoring of resources. Even if you have a tool with autodiscovery, teams still need to manually set expected thresholds and know exactly what kind of data they can and should get from each kind of resource. And they have to do it over and over. 

Automated discovery needs to be paired with pre-configured intelligent thresholds that instantly give your teams actionable insights and data across the entire stack. You’ll know you’ve found the right tool if you can onboard all your cloud resources and start monitoring them within five minutes. 

There’s no one right way to migrate to and leverage cloud services. Your enterprise might be migrating some applications and services to the cloud or building something cloud-native from scratch, or anywhere in between. Determining what to move to the cloud and when should always be considered alongside your company goals and the potential impact on customer experience and your business as a whole. Being aware of these potential pain points can help you optimize your journey to the cloud and take advantage of all the possibilities.

In conclusion

A powerful AI-powered hybrid observability platform is a key factor in avoiding every one of these cloud monitoring pain points. LM Envision delivers a truly dynamic observability experience for modern hybrid and multi-cloud enterprises, offering a single source of truth in all of your cloud services, applications, and infrastructure. Intuitive correlation of cloud performance metrics and cloud provider health status to your infrastructure gives Ops teams the insights they need, across any and all clouds so they can work smarter not harder. Built for any and every stage of your cloud journey, so you can cloud with confidence. Learn more in this guide: Why Observability Matters & How to Build a Use Case for Your Boss