Senser blog

There’s no better way to check the pulse of the observability problem space than by attending industry events and speaking directly with the SREs, IT professionals,..

As we enter this next phase of growth, we are taking steps to ensure that Senser is positioned for even greater success. That’s why I am..

Logs are fundamental to diagnosing issues in Kubernetes environments. Yet, working with logs alone can often feel like reading a mystery novel with the last few..

Despite their utility, IDPs often fall short when it comes to providing visibility into real-time production environments. This lack of context can lead to inefficient troubleshooting,..

For the first time in history, we're shifting the task of summarization away from humans and onto AI. With a bit of assistance, LLMs can now..

In my last blog, I teased two LLM use cases we’re building at Senser. This piece double-clicks on one of them: Chatting with data.

While the buzz around implementing generative AI capabilities in the observability market is recent and loud, we've been busy quietly evaluating the specific roles LLMs can..

Explore why generic AI models aren’t a strong fit for a critical element of production troubleshooting: root cause analysis and how LLMs could be integrated to..

What can soccer teach us about the impact of Graph ML on generating insights from complex, distributed systems? It turns out: a lot.

Kubernetes observability is critical for data operations because it provides the insights necessary for effective system management and troubleshooting, especially with Kubernetes managing multiple workloads across..

Grafana is essential for any organization's observability stack. But full observability of all layers in a production environment requires a suite of tools...

The pros and cons of open source vs. commercial observability solutions – and the limitations of both.

Explore the 4 critical components powering the next generation of AIOps.

ML has the potential to revolutionize root cause analysis by mimicking and enhancing human diagnostic capabilities at machine scale. But genuine breakthroughs require overcoming significant data,..

Traditional monitoring tools detect problems, but cannot trace causality across interconnected infrastructure, network, and application layers.

Teams tasked with maintaining service quality must embrace automated root cause analysis to quickly discover and remediate production issues.

Announcing the launch of our new end-to-end SLA and SLO management service.

Senser announces its recognition as one of Intellyx’s 2023 Digital Innovators of the Year.

While traditional monitoring tools are essential for detecting symptoms, they often fall short in identifying the root cause, especially when the issue spans multiple layers[...]

Today, Senser emerges from stealth – unveiling our AIOps platform and announcing a $9.5 million funding round led by Eclipse, with participation by Amdocs Ventures and..

In today’s complex and interconnected production environments, when something goes wrong, the blame almost always lands first on the DevOps team. Here’s why that’s the wrong..

Kubernetes, alongside the distributed architecture of micro-service based applications, introduces complexity that makes debugging failures vastly more challenging. SRE and DevOps teams need new approaches to..