Operations | Monitoring | ITSM | DevOps | Cloud

Though AI tools have made individual developers dramatically more productive at writing code, most engineering organizations report moving only about 20% faster than before. As Honeycomb CTO Charity Majors recently wrote, "AI came for code generation first because it was the easiest problem to solve, but it was never the thing holding developers back.".

If your organization is running AI or machine learning workloads on Kubernetes, the bill is real. GPU instances are among the most expensive resources in cloud infrastructure, where a single high-end node can run $30 to $40 per hour, and a multi-day training job on a cluster can cost tens of thousands before anyone looks up from their terminal. What most engineering and FinOps teams haven’t been able to do (until now) is connect that spend to the workloads that caused it.

In a large-scale DevOps environment, small discrepancies lead to massive headaches. You’ve likely experienced it: a script runs perfectly on a developer’s laptop but fails in the production pipeline. You spend hours hunting for the cause, only to discover a mismatch in CLI versions. At JFrog, we know the JFrog CLI is vital to your automation, but managing it manually across thousands of users and pipelines is a hurdle that slows you down.

There’s always been a bit of a communication breakdown between finance and engineering when it comes to cloud costs. Cloud costs are driven by technical factors expressed in esoteric terms, and so speaking the language of finance does not guarantee that you’ll speak the language of cloud cost. But AI is changing that. Fast. With the right AI tools, finance leaders can now ask natural-language questions about their cost data and get fast, accurate answers.

Debugging a broken deployment can take hours, especially when the cause is unclear. Recently, a customer ran into this exact situation: their AI agent produced a Drupal site with broken composer scripts and mismatched database credentials, and nothing they tried got it running. This video shows how debugging works in practice on Upsun.

When you’re building.NET applications, the choice between free ORM tools and commercial ones can make or break your project’s future. It’s not about one side winning, both have standout strengths. Free tools like Entity Framework Core or Dapper offer flexibility without the price tag. However, as projects grow, teams need commercial tools to deal with larger schemas, more complex mappings, and multiple developers working on the same data layer.

Short Summary: Many companies evaluate cloud platforms when they reach scalability limits on existing infrastructure, with migration decisions typically driven by a broader mix of factors: cost optimization, availability, security, and access to managed services. However, despite this shift, a lot of teams still run SQL Server on their own servers. Keeping these systems running well requires good monitoring, performance tuning, and regular maintenance.