Operations | Monitoring | ITSM | DevOps | Cloud

We are launching a new version of our Slack app for Spike. New architecture, a proper incident workflow you can run end to end without leaving Slack, and our first real go at bringing AI into the mix. We didn’t get here in a straight line though. This is the story of why we rebuilt our Slack app from the ground up, how we did it, and what we ran into along the way.

Application monitoring tools track your application's health, speed, errors, and resource usage in real time. Also called APM tools or application performance monitoring software, these tools are essential for any team running production workloads. The leading options in 2026 are Datadog, New Relic, Dynatrace, Grafana, and Elastic APM for traditional workloads, plus Arize AI, LangSmith, and Weights & Biases for AI observability.

Terraform state doesn't stay put forever. Teams outgrow local state files, consolidate multiple backends into one, switch cloud providers, or decide it's time to move off a platform that's no longer working for them - HCP Terraform's free tier ending and its resource-based pricing is a common trigger these days. Whatever the reason, the state file itself is the part everyone's afraid to touch, because it's the only record of what Terraform thinks your infrastructure actually is.

Personal Settings let you make CloudZero look and run the way you do.

Modern infrastructure teams are expected to deliver cloud-like speed, consistency, and reliability, even when their workloads run on physical servers. Bare metal remains essential for many environments: private clouds, Kubernetes clusters, AI infrastructure, edge sites, regulated platforms, and large Ubuntu estates. But operating physical infrastructure at scale is difficult when provisioning, patching, monitoring, and lifecycle management are handled by disconnected tools and manual processes.

The first stage of AI SRE maturity is a laptop, a throwaway cluster, and zero production access. Here’s how to set it up, and what to watch for. AI SRE (Site Reliability Engineering) agents are AI-powered systems that automate the most time-consuming parts of incident response: triaging alerts, correlating logs and metrics, generating root-cause hypotheses, and proposing remediation steps.

Today, we’re excited to announce that Boost is moving out of beta and into public preview. After months of building, breaking, and rebuilding inside JFrog’s own R&D organization, Boost is ready for the world. If you are currently running into token limits, unpredictable costs, or runaway usage from AI agents, Boost was built for you. It has already helped our teams reduce token spend while maintaining performance.