Operations | Monitoring | ITSM | DevOps | Cloud

Redgate Monitor ships new features every month and the past few months have brought some exciting new additions to empower your workflows. Spanning AI-powered tooling, cloud deployment, cross-database platform support and enterprise security, these updates reflect some of the biggest areas shaping how database teams work today. Whether you're managing compliance requirements, trying to get on top of alert management or looking to get a better grip on cloud costs, there's something here for you.

Canonical, the publisher of Ubuntu, today announced the general availability (GA) of Managed Kubeflow on the Microsoft Azure Marketplace. This solution enables AI teams to get a fully managed, production-ready MLOps platform in their own tenant. Upstream Kubeflow is a powerful tool for machine learning, but it remains notoriously challenging to deploy and maintain.

I’ve yet to meet a developer that enjoys working with metered AI APIs. The need to pay for every API call in development works in direct opposition to the ethos of rapid iteration, and it’s easy for the costs to get out of hand. That’s why Canonical has created a different approach to building AI-powered applications; one where the model lives inside your app, not behind a pay-per-token HTTP call.

Certificate automation does a lot of work on your behalf. Agents running on your servers, talking to certificate authorities, deploying certs to your infrastructure. At some point someone (your CISO, your auditor, or your own brain at 3am) is going to ask: what exactly happened, and when? Today we’re shipping audit logs. Every action taken in CertKit is now recorded: logins, invitations, certificates added, issued, renewed, revoked, and deployed. Agent registrations, approvals, and config changes.

There’s a growing ecosystem of AI coding agents: Claude Code, Cursor, Copilot, Codex, Gemini CLI, Windsurf, and others. They’re good at writing code, but they don’t inherently know how to instrument that code for observability, configure monitoring infrastructure, or troubleshoot production systems using real telemetry data. That knowledge lives in documentation, runbooks, and the heads of your senior SREs.

Continuous integration (CI) costs can escalate quickly as engineering teams scale. While most organizations focus on cloud bills, the true cost of CI includes slow build times, developer wait time, inefficient test execution, and overprovisioned infrastructure. CI cost optimization is the practice of reducing the total cost of CI pipelines by improving build efficiency, minimizing compute usage, and eliminating unnecessary work without slowing down development.