Operations | Monitoring | ITSM | DevOps | Cloud

You shouldn’t have to grant full repository admin rights just to let an engineer or release manager trigger a deployment. To solve the all-or-nothing access problem, Bitbucket Pipelines introduces custom deployment permissions (Beta). You can now gate specific environments to authorized users and groups, ensuring safer releases and easier compliance. Left: Add users and groups to gate deployments for this environment.

Software release management moves code through testing, approval, and into production with a clear rollback plan. 72% of organizations have hit a production incident from AI-generated code; developers now ship 63% faster (Harness, 2025). Effective release management needs defined stages, approval gates, automated testing, and the ability to roll back quickly. Feature flags, automated safety gates, and progressive delivery let teams ship faster and safer as AI raises code volume.

A design partner told us the AI Explorer table needed a way to get data out so it could be saved and shared elsewhere. Now you export the whole view in one click, cost, tokens, cache, and model count all included. The values come through as clean numbers, not text you have to scrub. It’s the same pattern as Explorer, so there’s nothing new to learn.

For years, the cloud migration business case was framed around one comparison: “Will AWS be cheaper than our data center?” That question still matters, but it is no longer where the value is. The 2017-2024 wave of mass migration is largely complete. Most enterprise workloads are already in the cloud - often in a lift-and-shift state: oversized instances, commercial-OS BYOL, on-prem-shaped network designs, and legacy frameworks that block the next step.

When platform providers talk about autoscaling, they usually mean one thing: application autoscaling, adding or removing web instances based on traffic. The pattern is well understood. Define a metric, set a threshold, and let the platform act on it. Most platforms still leave database capacity as a separate, mostly manual problem. Teams either pre-provision for the worst hour of the worst day, upgrade plans on a calendar, or wire together their own scaling logic on Kubernetes or RDS.

Across the United States, data center projects are being stalled, local governments are putting ordinances in place to limit data center buildouts, all because people hate data centers. Due to community pushback, in Q1 2026 alone, over $130 billion’s worth of AI data center projects has been blocked or delayed.