Operations | Monitoring | ITSM | DevOps | Cloud

If you've been running production workloads on AWS for more than a year, you've felt it: the 3 am PagerDuty alert, the scramble to check the AWS console, the frantic Slack thread asking, "Is this us or is this AWS?" And then, minutes or hours later, the AWS Service Health Dashboard finally acknowledges what your users have been experiencing all along. It happens because AWS is the backbone of modern infrastructure.

The real economic decision for observability happens at ingest, before storage, billing, and retention choices are locked-in. Until now, the logic governing that decision could only see three broad fields: application, subsystem, and severity. That just changed. TCO routing now matches on any field in the event payload, including nested keys, custom fields, and event body content, using DPXL, the DataPrime Expression Language.

Virtual event will explore what it takes to use AI in production SRE, from incident response and observability to platform design, cost control and self-healing operations TEL AVIV and SAN FRANCISCO, April 22, 2026 — Komodor, the autonomous AI SRE company, today announced it will host AI SRE Summit 2026, a free live virtual event on Tuesday, May 12, 2026, bringing together site reliability, platform engineering and cloud-native leaders to discuss how AI is changing production operations, and where i

When a job fails, gets canceled, or never runs, the work that still needs to happen afterward (cleanup, notifications, teardown) has no clean way to trigger. There is no easy way to express “run this no matter what” in your pipeline config without duplicating jobs or adding fragile workaround branches. Terminal jobs change that.

The evolution of Ubuntu’s toolchains story goes beyond just providing up-to-date GCC, LLVM, and Python. It is also about opinionated openJDK variants, task-focused devpacks, FIPS compliant toolchains, and snaps, like the new.NET snap and Snapcraft plugin. These are enhancements that collapse half a day of setup into a single command or two, demonstrating what a frictionless developer experience means in practice for framework and application developers on Ubuntu.

For years, progress in AI was equated with scale. Larger models, broader parameter counts, and increasingly complex cloud architectures were treated as signals of advancement. In enterprise operations, however, scale alone does not determine success. Economics does. As AI becomes embedded in operational workflows, organizations are discovering that model size is less important than cost stability under continuous load. AI-driven operations do not run in bursts. They run constantly.