Operations | Monitoring | ITSM | DevOps | Cloud

We are excited to announce the release of the brand new line of MCP Servers (Model Context Protocol), designed to connect AI assistants, AI agents, and large language models directly to enterprise databases and cloud business platforms. The release includes 19 specialized MCP Servers and the flagship Universal MCP Server, which enables AI access to virtually any data source through the ODBC standard.

If you run a service desk, you’ve likely seen this pattern: Service requests, incidents, and change requests often end up in the same queue under the same SLA, even though they require different handling. Many requests that could be resolved through self-service still go through manual intervention, while misclassification adds further delays and confusion. Service request management brings structure to this by defining how requests are handled end to end.

Modern engineering teams are drowning in telemetry data. A mid-sized Kubernetes cluster running 50 microservices can generate millions of log lines per minute. Add distributed traces, Prometheus metrics, cloud provider events, and application-level instrumentation and you're looking at terabytes of observability data every day. The problem isn't just volume. It's what you do with it.

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.

Networks generate the most data of any system in your stack and have the least patience for stale dashboards. Interface counters tick every second. BGP sessions flap. Flow records arrive in bursts. When something goes wrong, you don’t have 10 seconds to wait for an aggregation to finish.

You already have everything you need. If you’re using Sentry, you have traces, structured logs, and now application metrics. Most teams use that stuff for debugging and stop there. But get this: that same data can answer most of the product questions you’ve been sending to a separate analytics tool, maintained by a separate team, with a separate data model and a separate bill. (Not all of them.