Operations | Monitoring | ITSM | DevOps | Cloud

Josh Mesout (Chief Innovation Officer at Civo) took the main stage at Civo Navigate London 2025 to deliver a critical message: The AI revolution isn't just coming, it's here, and the way companies are built is changing faster than ever before. His session cut through the hype, delivering hard data on what separates the companies that scale AI from the ones that sink money into failed prototypes. The takeaway is blunt: The biggest threat to your AI ambition isn't the model; it’s your infrastructure.

Cloud vendor lock-in threatens agility and raises costs. Discover the high price of proprietary services, egress fees, and technical entrenchment, plus the strategic roadmap to escape. Learn how embracing open standards, Kubernetes, and an exit strategy from day one ensures long-term flexibility and control.

I'm excited to share that we're launching Braintrust by Cortex, a podcast I've been wanting to create for a while now. After years of building Cortex and working with engineering leaders across industries, I’ve noticed an opportunity for conversations that dig deeper into the daily work of building great engineering teams.

Running distributed systems means you need clear visibility into how your services behave. Prometheus has been the standard for metrics for a long time, and OpenTelemetry is now giving teams a more consistent way to collect telemetry across their stack. In many setups, you'll have both: existing Prometheus instrumentation that's already in place, and new components instrumented with OpenTelemetry.

As companies race to ship AI features into production, FinOps teams everywhere are scrambling to keep up with the variability of their engineers’ new favorite toys. The rapid pace of AI experimentation is creating a new wave of unpredictable usage patterns and costs.

When you're shipping LLM features, a lot of the work goes into keeping the model's behavior predictable. You deal with questions like: These are everyday concerns when you integrate LLMs into production systems. Guardrails AI provides a Python framework that helps you enforce those expectations. You define the schema or constraints you need, and the framework validates both the inputs going into the model and the outputs coming back.