Operations | Monitoring | ITSM | DevOps | Cloud

Harness has modernized Commitment Orchestrator to give FinOps teams a clearer, faster, and more intelligent way to manage cloud commitments. The redesigned experience unifies visibility across RIs, Savings Plans, on-demand, and Spot usage, with expanded metrics and streamlined workflows for smarter decisions. Built on a foundation for AI-powered insights, it helps teams optimize spend with greater confidence and less manual effort.

Managing team access in Grafana can be tricky—especially as your organization grows. That’s where Grafana access groups (also known as Limited Access Groups in Hosted Graphite) come in. They allow you to define groups of dashboards and restrict which team members can access them. If you’re using Hosted Graphite with Grafana dashboards, this feature helps you organize teams, maintain data privacy, and simplify access control—all while giving users just the permissions they need.

At LogicMonitor’s Senior Leadership Team Offsite in July, I sat down for a candid conversation with Kyle Hill, CTO of ANS Group. As a longtime LogicMonitor customer and leader of a 700+ person tech powerhouse, Kyle offered sharp insights into scaling infrastructure, unlocking AI-driven value, and what true partnership looks like in today’s MSP world. Here’s an edited and condensed version of our conversation.

Aiven is introducing a new pricing plan for Aiven for PostgreSQL services. Starting at $8 USD per month, the Developer tier offers more storage, so you can scale up your free PostgreSQL service in a cost-effective way. Unlike the Free tier, services on the Developer tier are not automatically powered off if inactive. The Developer tier also automatically includes Basic support services. More information on the Developer tier is available in the Aiven docs.

Harness surveyed 500 security practitioners and decision makers responsible for securing AI-native applications from the United States, UK, Germany, and France to share findings on global security practices. The State of AI-Native Application Security 2025 dives deep into AI visibility and the changing landscape of security vulnerabilities. If 2024 was the year AI started quietly showing up in our workflows, 2025 was the year it kicked the door down.

Welcome to CloudZero’s inaugural Cloud Economics Pulse! This is our monthly snapshot of how cloud spend is evolving across providers, services, and emerging AI workloads. Each month, we’ll surface key trends, highlight where the money’s moving in cloud spend, and offer practical insight to help FinOps and cloud cost teams stay on top of things. October — and preceding months — shows a turbulent stretch for cloud economics.

If you’ve ever opened your Google Cloud bill and felt confused, you’re not alone. Costs in GCP can spike up fast. One project here, a few APIs there, until the total looks nothing like what you expected. That’s because Google Cloud pricing is built for flexibility. You pay for what you use, across dozens of services, each with its own rules, discounts, and data charges. It’s robust, but also easy to lose sight of what’s driving your spend. The good news?

Go’s os.Root feature, introduced in Go 1.24, is designed to improve filesystem security by restricting operations to a specific directory, making it much harder for attackers to exploit path traversal vulnerabilities.

Some parts of a system don’t lend themselves to quick instrumentation changes. You might have a production binary that hasn’t been rebuilt in years, or a stack made of several languages where each team manages telemetry differently. In those situations, getting consistent signals often means touching code you’d rather leave alone or coordinating updates across many services. OpenTelemetry eBPF Instrumentation (OBI) approaches this from the kernel side.

There’s a viral saying folks use about flaky crushes, spouses, and forgetful friends: "if he wanted to, he would." The idea is straightforward: when someone cares, they make the effort. As it turns out, the same principle applies surprisingly well to AI. Systems, like people, have things they "want" to do. Each model has patterns of reasoning and synthesis it performs naturally.