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Learn how to use Megaport Storage as an S3-compatible Veeam backup target for scalable, private, offsite backup storage. Table of Contents A backup is only useful if it can be retrieved when needed. Where backups are stored determines how well they’re protected from incidents at the primary site, how long recovery transfers take, and what it costs to bring the data back.

A bot requests /wp-login.php on your Rails app. Rails can’t route it, raises ActionController::RoutingError, and returns a 404. That should cost almost nothing. On a Rails 8.1 app with a few thousand compiled templates, it can cost 13 MB of allocations and 27 ms of CPU. A detailed report on rails/rails#58887 traces the cost to one method, ActionDispatch::ExceptionWrapper#build_backtrace.

The fastest way to cut cloud compute costs is to stop paying for capacity your workloads do not use. Right-size CPU and memory to real usage, scale idle workloads to zero, and make cost policy a platform default instead of a quarterly review. Control Plane does all three at the platform level: Capacity AI right-sizes running workloads, autoscaling scales idle ones to zero, and customers typically spend 30 to 50 percent less on compute than running directly on AWS, GCP, or Azure.

Building on the major release of Tempo 3.0, Tempo 3.1 is here, delivering community-contributed Kafka client improvements, query-based trace redaction, sampling-aware TraceQL metrics, and more. Together, the updates in 3.1 make it easier to operate Tempo, get accurate insights from your trace data, and investigate issues more efficiently. You can continue reading and check out the video below to learn more about the latest features.

Your Mean Time to Resolution (MTTR) has likely stayed flat for three or four quarters. The investment was real: scheduling tools, dispatch optimization, new training modules, and more technicians. Operations reviews still dissect response time, travel time, and wrench time. The metric still refuses to move. Most field service leaders measure MTTR from the start of the repair to the moment the asset returns to service.

AI cost allocation is the practice of attributing every dollar of AI spend to the team, product, feature, or customer that generated it. That spend includes API tokens, GPU compute, per-seat tools, and shared infrastructure. It's harder than cloud allocation because AI spend arrives untagged, spans vendors, and pools in shared resources. Four methods cover most cases: tag-based, key-based attribution, proportional split, and usage-telemetry.