Operations | Monitoring | ITSM | DevOps | Cloud

At Bunnyshell, we’re building the environment layer for modern software delivery. One of the hardest problems our users face is converting arbitrary codebases into production-ready environments, especially when dealing with monoliths, microservices, ML workloads, and non-standard frameworks. To solve this, we built MACS: a multi-agent system that automates containerization and deployment from any Git repo.

We're thrilled to announce that we have been recognized as a Strong Performer in the 2025 Gartner Peer Insights Voice of the Customer for Digital Experience Monitoring (DEM). We think that this recognition is a result of direct customer feedback on their experience with our solutions, underscoring the trust and value users associate with our solutions.

In modern software development, the pressure to move fast is matched only by the need to get it right. Teams working within the software development lifecycle (SDLC) must constantly balance velocity and quality, ensuring releases are stable, secure, and performant. Traditional software development models often relied on manual verification and human intuition to validate releases; however, as systems have grown in complexity, guesswork is no longer sufficient to meet these rising needs.

When Microsoft engineers went looking for a modern visualization platform to help track critical signals and make quicker decisions, Grafana emerged as the clear favorite. But there was just one hitch: the available authentication methods didn’t quite meet their needs.

ITIL covers a wide range of practices, and it’s easy to feel overwhelmed when trying to figure out which tools support what. Not every organization follows the framework in full, but many do apply its principles to improve how they plan, deliver, and support IT services. ITIL tools help turn those practices into something you can act on, automate, or report against.

Cloud Asset Management software is a smart move for modern IT teams. It helps you manage your assets from anywhere, scale with your needs, and avoid the hassle of on-premise maintenance. But here’s the catch: picking the right tool isn’t easy. There are a lot of options out there. And every organization has its own goals, budget, and way of working. What works for one team might not work for another.

You know the feeling. It’s that familiar rhythm of playing defense, racing from one network fire to the next. The alerts pile up, users report slowdowns, and your team of brilliant engineers spends its days tracing packets instead of focusing on the future. For years, automation has been the answer. You’ve built scripts and workflows to handle repetitive tasks, which has certainly helped.

Networks aren’t what they used to be. While your network operations teams still have legacy equipment to manage, they’re also contending with the expanded reliance on software-defined networking (SDN), hybrid and multi-cloud architectures, private clouds, and more. These environments are anything but static. They’re sprawling, dynamic, and evolving faster than ever—which means that establishing and retaining visibility and control is more challenging than ever.

60% of Gen Z won’t engage with a slow-loading website. In today’s digital economy, that’s a deal-breaker. Whether it’s a banking portal, a travel app, or an AI-powered SaaS platform, users expect performance. Instant loading, global reliability, and smooth interactivity aren’t just nice to have—they define the winners.

If you’re using Python for search, log aggregation, or analytics, you’ve probably worked with Elasticsearch. It’s fast, scalable, and fairly complex once you go beyond the basics. The official Python client gives you raw access to Elasticsearch’s REST API. But getting it to work the way you want, especially under load, can be tricky. This blog walks through practical ways to index, query, and monitor Elasticsearch from Python code, without getting lost in the docs.