Operations | Monitoring | ITSM | DevOps | Cloud

We’re thrilled to share that Mezmo has been recognized by G2 with 25 badges across four key categories: Enterprise Monitoring, Log Monitoring, Log Analysis, and Cloud Infrastructure Monitoring. These awards are more than just a celebration of our platform—they’re a reflection of you, our customers. Your feedback, support, and insights push us to build better solutions and deliver the highest standards of performance and service.

As any good cook knows, if you want to create a top-notch dish, you have to use the best ingredients. So when the engineering team for Cookidoo — an online platform and app that features more than 80,000 guided recipes for the Thermomix, an all-in-one kitchen small appliance — realized the observability tool they were using to monitor the platform wasn’t delivering what they needed, they decided to switch to Grafana Cloud and OpenTelemetry.

One of Sumo Logic’s core company values is, “be customer champions.” It’s more than just a phrase on a slide, it’s a principle that shows up in every roadmap discussion, product iteration, support conversation, and onboarding call. Customer Appreciation Day is April 18th.

While AI offers powerful benefits for network operations, building an in-house AI solution presents major challenges, particularly around complex data engineering, staffing specialized roles, and maintaining models over time. The effort required to handle real-time telemetry, retrain models, and manage evolving environments is often too great for most IT teams.

Almost two years ago I wrote the first installment of what was supposed to be a 3 part series on Serverless Monitoring. Parts two and three never materialized. Today, however, I am revisiting that original idea and expanding upon it. I hope to succeed this time in making it a full three-part series. For this first installment (Revisited), I will again work with Google Cloud Run to monitor MongoDB Atlas.

When something slows down in prod, it’s too easy to fall into old habits. Throw in a few more logs, ship some metrics, try to reproduce the issue locally, and maybe reach for perf or py-spy if you’re feeling ambitious. Traces can help, but they usually stop just short of explaining why things are slow, especially when it’s deep in the stack.