Operations | Monitoring | ITSM | DevOps | Cloud

Comparing Prometheus vs. Cortex - Scalability, Cost, Performance, Known Weaknesses.

Azure Virtual Machines (VMs) represent the power of cloud computing, making the infrastructure of the digital age accessible to every business. With Azure VMs, Microsoft has democratized access to virtualized environments, allowing businesses to focus on building their applications without worrying about the underlying hardware. Azure VMs are a part of Microsoft’s Azure platform, one of the most comprehensive cloud platforms on the market.

Choosing the right cloud storage service requires an understanding of your needs and the technical capabilities of each platform. In this article, we delve into the specifics of Azure and Google Cloud Platform (GCP) storage services, providing a detailed comparison to help inform your decision.

This is the second of a two-part blog on things to consider when mapping your information architecture. In the first, we shared some tips on how to approach this process based on best practices from the Cortex Customer Engineering team. In this blog, we’ll share how new and existing features in Cortex accelerate creation of a more resilient information architecture.

If you’ve been anywhere in the DevOpsphere in recent times, you have certainly encountered the Platform Engineering vs. DevOps vs. SRE debates that are all the rage. Is DevOps truly dead?! Is Platform Engineering all I need?! Have I been doing it wrong all along? These have become more popular than the mono vs. multi-repo flame wars from a few years back.

In the constantly evolving world of software development and IT management, choosing the right operating system for your enterprise developers is a strategic choice. Ubuntu vs macOS, which is right for you? Our latest whitepaper helps you answer this question and make an informed decision. This blog post provides a sneak peek into our findings covering both the developer landscape, and the key considerations for IT administrators. Considering macOS or Linux for your organisation?

Note: This post is co-authored by Simon Aronsson, Senior Engineering Manager for Canonical Observability Stack. AI/ML is moving beyond the experimentation phase. This involves a shift in the way of operating because productising AI involves many sophisticated processes. Machine learning operations (MLOps) is a new practice that ensures ML workflow automation in a scalable and efficient manner. But how do you make MLOps observable?