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Learn why engineers ignore cloud cost optimization and how to build a culture of FinOps governance. See how Harness helps. Engineers often overlook cloud costs due to lack of visibility, fragmented tooling, and competing delivery priorities. Organizations can fix this by embedding FinOps guardrails into developer workflows and providing real-time cost feedback during build cycles.

SAST vs SCA vs DAST vs IAST: a clear breakdown of what each scan finds, when to run it, and how to combine them across your SDLC. Most AppSec teams don't run one type of scan - they run several, at different points in the pipeline, because no single tool sees the whole picture. This article breaks down SAST vs SCA vs DAST vs IAST: what each one actually tests, where it fits in the software development lifecycle (SDLC), and how to combine them without duplicating effort or drowning developers in findings.

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.