Operations | Monitoring | ITSM | DevOps | Cloud

In Kubernetes, applications are constantly changing — new pods start, old ones shut down, workloads shift across nodes. The challenge is making sure that different parts of your system, and even external clients, can still find each other when the actual locations keep moving. That’s what service discovery handles. It provides a stable way for applications to connect and communicate, no matter where they’re running or how often the underlying infrastructure changes.

When someone leaves your organisation — whether they resign, retire, or are let go — it’s easy to think the hard work is over. But the moment an employee’s last day arrives, a new risk window opens. If their access isn’t revoked properly or their data isn’t captured, organisations face security breaches, data loss, compliance issues, and rising costs. This is why a well-designed user off-boarding process is just as important as onboarding.

The popular narrative around AI economics is changing. At one time, Moore’s Law conditioned us to expect that smarter, faster computing would steadily get cheaper. When it comes to AI, that expectation holds true at the unit level. Per-token costs are indeed declining. But the number of tokens consumed per task is growing exponentially, making total costs spike. The tension here is important: on paper, inference is getting cheaper.

Tired of complex cloud setups? Explore the top 10 Fargate alternatives and discover how Qovery can simplify your container deployment, saving you time and money.

When I started at Speedscale, I looked like this: And after one year of learning, growing, and keeping pace with innovation well, let’s just say the journey has left its mark: Of course, I’m joking (sort of). The truth is, this past year has been intense, energizing, and filled with new challenges. If anything, it’s made me feel younger in spirit, even if the mirror might disagree some mornings.

You were tired of renewing all those certificates, and Certbot looked so easy. Now you have scripts thousands of lines long filled with command line incantations you have to Google every time you open it. The script is running on all the critical servers. And some of the printers. If someone looks at it the wrong way, a certificate expires.

I carved one Azure H100 into virtual “cubicles” using MIG (Multi-Instance GPU), compared it to an A100, ran Triton inference workloads, and captured both latency and cost. The verdict – The H100 with MIG delivers better latency and consistency, while the A100 is more cost-effective at scale, depending on your workload.

Managing containerized applications or microservices can be difficult. It is even more demanding and prone to error if you do it manually. So, what’s the alternative? Container orchestration. Container orchestration is an automation technology that enables engineers to coordinate when containers start and stop, schedule and execute tasks, manage failovers, and perform recovery processes. The technology helps automate these tasks throughout a container’s lifecycle.