Operations | Monitoring | ITSM | DevOps | Cloud

AI gross margin is what remains of SaaS profitability after inference, model routing, and AI infrastructure land in cost of revenue. The numbers have moved. AI products averaged 45% gross margin in 2025 and are projected near 53% in 2026, against the 70% to 85% that SaaS built its valuations on. The compression is real, measurable, and manageable for companies that can see their cost to serve.

Compare Periods launched with two display modes: Aligned and Overlay. The top request from customers was bringing back a true side-by-side view, the kind where two independent charts sit next to each other so you can compare the overall shape of each period. Split brings that back. But the team also added Delta, a new mode that shows the dollar difference between periods.

Enterprise AI agent programs overspend because leaders optimize token costs, which represent just 20-25% of variable run costs. Human oversight accounts for 70-75%. The real fix is reducing exception rates and embedding governance directly into agent execution from the start.

A frequently encountered issue when exploring database optimizations is proving that a proposed query rewrite is actually faster than the existing query. Often, proving this isn’t just as simple as running both queries and comparing their execution times. When your hot queries are measured in milliseconds, factors such as cache states, concurrent queries competing for CPU and I/O, and noisy neighbors can mask the true performance difference between query versions.

Developers and SREs who use Bitbucket Cloud often need to move between their source code, observability data, test results, and security findings to investigate a problem. Connecting an error or slow method to the correct repository, file, and commit takes time. Teams may also discover vulnerabilities, infrastructure misconfigurations, coverage regressions, or flaky tests only after code has progressed further through the software development life cycle, when remediation is more disruptive.

AI ticketing puts machine learning and language models to work on the IT help desk. An AI ticketing system reads tickets, sorts them, routes them, drafts replies, and resolves routine requests without a technician.