Operations | Monitoring | ITSM | DevOps | Cloud

Developer self-service pipelines fail most often at the handoff between resource provisioning and production deployment. A service catalog might let developers scaffold a new microservice in seconds, but if they still need to file tickets to wire up CI/CD, provision environments, or update deployment configurations, the value proposition collapses. The friction reappears exactly where velocity matters most: the path from code commit to running production workload.

Task containers are now generally available on Upsun Cloud, giving you a place to run AI agents alongside the rest of your application. The agent runs as a container inside your Upsun Cloud project, next to the app it works on, with the same access to your environment and data as everything else deployed there. The lifecycle is the part that's new: it fits how agent workloads behave instead of forcing them to pretend to be servers. A few things up front: This is a hands-on guide.

What's in this article? For over a decade, the enterprise IT narrative was simple: move everything to the public cloud. Yet as enterprise architectures have matured, many UK technology leaders have discovered that hyperscale cloud environments do not always deliver the outcomes they expected. The promises of seamless scalability and lower total cost of ownership often come with trade-offs that become more apparent over time.

Every organization knows what their database monitoring tool costs on the invoice. Almost none of them have added up what it costs in the people running it, the incidents it doesn't catch fast enough, and the savings sitting in the environment that nobody has visibility to find.

GitHub Copilot Enterprise pricing is $39 per user per month, and since June 1, 2026, each seat includes $39 in monthly GitHub AI Credits, pooled across your whole organization and consumed by token usage at per-model rates. The seat price is fixed. The bill is not: usage beyond the pooled credits is charged on top, which is why the real Enterprise question in 2026 isn't the price per seat. It's your engineers' token consumption.

CUR 2.0 is generally available and on by default, so every AWS connection you make now starts with the richest billing data AWS offers. That richness is important. CUR 2.0 carries AWS caller identity, which is what puts Bedrock and other AI spend on the role that drove it instead of leaving it as one number in your bill. It also populates AWS Account Name automatically. Both of those are yours the moment the connection is live. Getting there is a single step now.

In conversations with customers, compliance rarely starts as a tooling discussion. It usually starts with a practical concern: how do we keep moving, keep modernizing, and still know that our infrastructure is aligned to the policies we are accountable for? That question comes up because the pressure is real. Teams are managing hybrid estates, responding to audit requests, dealing with drift, supporting new platforms, and being asked to move faster without creating more risk.

Testing consumes up to 50% of CI/CD pipeline time. That’s the single most expensive place in your delivery pipeline, and a meaningful share of that time is often wasted running tests unaffected by the change. Every time a developer pushes a commit, the full test suite runs. A two-line bug fix in auth.py triggers 2,000 tests, including 1,900 that don’t touch anything near auth.py. Those unrelated tests are overwhelmingly likely to pass, but the pipeline still spends 20 minutes running them.