Operations | Monitoring | ITSM | DevOps | Cloud

Azure Managed Redis is a Microsoft first-party, fully managed in-memory data store, replacing Azure Cache for Redis tiers. It includes Redis Enterprise features such as RediSearch for vector search and full-text search, in addition to RedisJSON, RedisTimeSeries, and Active Geo-Replication. As Azure Cache for Redis reaches end of life, more teams are planning migrations to Azure Managed Redis in search of better performance, lower cost, and modern capabilities for AI and real-time workloads.

In AWS environments, a data perimeter is a set of preventative controls that help ensure that your trusted cloud identities (principals or AWS services acting on your behalf) are accessing trusted resources from authorized networks. You can apply these controls at various levels of your infrastructure, such as per resource or across all resources in your AWS account.

CloudZero continues to ship new features and enhancements at a fast pace. We’re publishing these product update posts to keep you up to date on our velocity and how we’re adding customer value.

AI spend is going parabolic, and the labels on the bill (OpenAI, Anthropic, Gemini) are about all a CXO gets to work with. The hard part of tying that spend to outcomes is structural. A major portion of AI spend isn’t COGS. It’s the spend on coding agents producing the software, the spend on building marketing content, the spend on custom sales tooling, the spend on Intercom agents and Sybill analysis.

IT leaders rarely think they have an infrastructure problem. When a roadmap slips or an audit finding lands, the reflex is to hire more senior engineers, a bigger platform team, another DevOps lead. But headcount is rarely the real lever. The bottleneck is the "hidden factory": the undocumented, invisible work that sits between a developer writing code and that code reaching customers. It doesn't show up in post-mortems because engineers treat the workarounds as normal.

With SRE AI, Google plans to fully adopt AI and agentic technologies, leveraging AI as a force multiplier while also maintaining control.

The first post in this series explored why traditional observability breaks down in hybrid cloud environments. As infrastructure, applications, and dependencies stretch across on-premises networks and cloud services, isolated monitoring views leave teams with an incomplete understanding of what is happening and why. That challenge raises the next question: what kind of operational model actually works in a hybrid environment?

The video was a product manager who claimed she worked at Netflix. (Her claim, not mine. I have no way of verifying it, and I can’t find the video now.) She was talking about how Netflix now requires every PM to vibe code a working prototype before presenting an idea to engineering. Show, don't spec. Build the thing first. I sat there for about ten seconds being mildly annoyed.

The best cloud monitoring tools are Hyperping (uptime, server monitoring, status pages, and on-call at a flat rate), Datadog (full-stack observability with the broadest integration catalog), New Relic (usage-based observability with the most generous free tier), Dynatrace (AI-driven automatic root-cause analysis for large enterprises), Better Stack (monitoring paired with logs and incident response), and Prometheus + Grafana (the open-source standard for cloud-native metrics).