In this blog, I wanted to take a deep-dive into our Notifications feature and explain some of the product design decisions we made. Notifications is one of the most frequently used features of SquaredUp. We designed this feature to be quick and easy to set up, with a primary focus on delivering timely notifications to the appropriate audience.

We’re excited to announce the general availability of dashboard variables in SquaredUp. With this new feature, dashboards you create are flexible and reusable. Instead of hardcoding specific objects within the tiles on a dashboard, you can use variables to create just one dashboard to be reused across all your objects of the same type – be it your pipelines, apps, or microservices. Viewers of the dashboard can then select which objects they are interested in on the fly.

Tool consolidation outcomes have driven some customers to drop Splunk and consolidate their log analytics use cases on Sumo Logic. Long-term Splunk customers with many dashboards, saved searches and monitors understandably want to retain a consistent experience for end users. As a result, a replacement strategy requires migration.

Over the last few weeks I've been talking about the key differences between Amazon EKS and Cycle. If you happened to miss it and want to catch up before diving into this post you can check it out here: This post will round out the series by taking a look at how worker nodes are added to a cluster and the major differences between EKS and Cycle there.

Load balancers are some of the most important load-bearing (pun intended) components in cloud environments. They perform multiple critical tasks: network switching, packet inspection, and of course, routing. Most cloud-based load balancers focus on load balancing within a single zone, but what if you have resources spread across multiple zones?

Trying to find the best virtual machine on the market that gives you the flexibility of easy scalability and the promise of a secure network – and doesn’t cost an arm and a leg (and maybe another arm)? Azure VM is likely the best solution for you… assuming you can project costs correctly. However, Azure doesn’t make it easy with its different offerings and pricing models.

Enterprise generative artificial intelligence (GenAI) projects are gaining traction as organizations seek ways to stay competitive and deliver benefits for their customers. According to McKinsey, scaling these initiatives is challenging due to the required workflow changes. With AI adoption on the rise across industries, the need for robust monitoring and observability solutions has never been greater.

Robotic process automation (RPA) initiatives often falter because enterprise application leaders choose use cases that involve intricate business logic, extended workflows, and require significant orchestration or additional technologies to deliver business value. In essence, RPA is not well-suited for complex workflows, lengthy processes, and those requiring extensive coordination.

Foundation models, or large AI models, are the driving force behind the advancement of generative AI applications that cover an ever-growing list of use cases including chatbots, code completion, autonomous agents, image generation and more. However, when it comes to understanding observability metrics, current large language models (LLMs) are not optimal.

Hybrid work is very much here to stay. But in spite of that, many companies still haven’t come around to the idea, meaning the tools and systems offered aren’t always used to best effect. This leaves these businesses unprepared to embrace the hybrid world of work we now all find ourselves in. Take hybrid meetings for example – which combine in-person and remote participation, using digital tools to facilitate communication and collaboration across various locations.