Operations | Monitoring | ITSM | DevOps | Cloud

Generative AI can produce code faster than humans, and developers feel more productive with it integrated into their IDEs. That productivity is only real if CI/CD tests are solid and automated. When not appropriately tested, you may encounter a production issue that you haven’t seen before. According to the State of Software Delivery 2025 report, 67% of developers spend more time debugging and resolving security vulnerabilities in code generated by AI.

The third quarter of 2025 has been a busy one for VictoriaMetrics Cloud! We expanded globally, polished the user experience, introduced new enterprise debugging tools, and delivered smarter alerts to help users make the most of their observability data. If you missed our Quarterly Live Update, don’t worry! You can watch the full recording here: Let’s recap what’s new in VictoriaMetrics Cloud this quarter.

For a long time, public cloud has been the default answer to scaling infrastructure, but it's not the only path forward. As more teams weigh the risks of vendor lock-in, data residency, and dependence on US-based providers, the conversation around private cloud has taken on new urgency. However, building on private infrastructure doesn't have to mean sacrificing flexibility.

Business teams today move fast. Developers, site reliability engineers (SREs), and product owners expect to manage incidents, changes, and requests in a way that fits naturally into how they already work with tools like Jira and Confluence. Customers expect a seamless service experience powered by automation and AI. The result is a wave of teams adopting tools like Jira Service Management to get everything they need in one place without slowing down.

My first 6 months here at PagerDuty have been a thrilling ride! PagerDuty continues to set the pace in incident management. With our 16-year track record of helping companies forge a path towards modern operations, we’ve been trusted by over 32,000 companies as the incident management platform of choice. Over these years, we’ve continuously delivered value to our customers at a rapid pace. And our customers have been vocal with us about wanting more.

Extending Large Language Models (LLMs) with custom tools has become increasingly valuable in today’s AI landscape. Model Context Protocol (MCP) servers provide a standardized way to connect external tools and resources to LLMs. This can enhance their capabilities beyond basic text generation. While thousands of pre-built MCP servers exist, creating your own allows you to address specific workflows. You can implement use cases that off-the-shelf solutions cannot handle.

Redis earns its place in modern stacks because it’s an in-memory data store with microsecond latency and rich data structures, making it perfect for things like caching, sessions, and rate limiting. Since it often sits on the request path, small issues (connection churn, blocked commands, memory pressure) can quickly ripple into user-visible incidents.

Over the years, Chaos Engineering has proven its effectiveness time and time again, uncovering risks and saving companies millions they would have lost in painful, brand-impacting outages. But as Chaos Engineering adoption increased, we found organizations running into the same stumbling blocks when they tried to scale. Individual teams would get great results with Chaos Engineering, then stall as they tried to get more teams involved.