Operations | Monitoring | ITSM | DevOps | Cloud

Telecom executives face a retention crisis that headcount cannot solve. Customers churn within 48 hours of a service incident, and traditional support models, even AI-powered chatbots, respond to problems rather than prevent them. The carriers closing this gap aren't expanding call centers. They're deploying agentic AI that predicts issues, executes resolutions, and learns from every network signal.

This guest blog post is authored by Dieter Matzion, a seasoned cloud practitioner who has operated exclusively in public cloud environments since 2013, with experience at leading technology companies including Google, Netflix, Intuit, and Roku. Custom metrics play a crucial role in enabling teams to monitor their applications and businesses. The flexibility of these metrics allows engineers to measure what matters most to their domain.

Early-stage engineering teams ship fast and learn in production. While speed is a competitive advantage, it can also lead to a high volume of noisy signals, like stack traces, timeouts, and dashboards full of red. Some of those problems can affect your users and revenue, but many don’t.

“Every engineering decision is a cost decision,” notes Ben Johnson, co-founder and CTO of Obsidian Security. That’s the reality of building modern SaaS products in the cloud. But as Ben points out, the answer isn’t to make engineers think long and hard about every dollar they spend. “You don’t want your team hesitating to solve risky technical problems because a choice might add $100 to the bill.

This blog post is based on a product presentation by Antonis Kalipetis, Staff Engineer at SymfonyCon 2023, about Upsun's experience with Nix. We utilized ChatGPT for transcription and to enhance the grammar and syntax. Django deployments have evolved since the framework's early days.

Holiday shoppers are notoriously impatient, and in 2025, they’re increasingly impatient when it comes to slow websites. Keywords like “website downtime tracking” and “ecommerce site reliability” are often trending because businesses are realizing that slow is the new down. This holiday season, the goal is to safeguard your website against business-critical slowdowns without adding “manual monitoring” to your already busy plate.

The speed at which you can go from making a change in your code, to understanding if it actually works, has long been a popular topic of discussion (and often, humour) for engineers. This remains true in a world with AI. Developer experience isn't just important for humans anymore. Those agents we're all using hundreds of times a day? Feedback cycles matter just as much for them, if not more.

In 2025, Elastic and Google Cloud created a powerhouse of AI-driven insights, providing an end-to-end search, observability, and security journey for our joint customers. We continue to partner on many opportunities for success and have made even further progress this year to empower all our users, especially around generative AI (GenAI). This blog highlights our collaboration with Google Cloud to help you harness the power of data at scale as well as our top moments from Google Cloud Next ‘25.

Guided Shadow Configuration removes the setup overhead of shadow databases in Flyway Desktop, allowing teams to adopt migrations-based workflows quickly and safely with minimal configuration. A Shadow Database is a disposable, ‘sandbox’ database that Flyway uses to generate and verify migration scripts.

You’re thinking about implementing an incident response tool, but you’re not quite sure what to look for – or which solution is the right fit? Of course, we could tell you a lot about the benefits of an incident response tool. After all, we’ve been involved with our software from day one and know the thinking behind every feature. But how can you know whether an incident response tool like SIGNL4 will truly work for you in real-world scenarios?