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At some point in an AI company's growth, the GPU bill stops making sense, and we are looking at a cluster running at 3 am for a model that never shipped. That's the bill that eventually lands on someone's desk, and the first instinct is a cleanup to identify waste and kill orphaned resources. It worked when cloud spend drifted slowly enough for a monthly review to catch up, but by 2025, AI infrastructure spend grew 166% year over year. The job was run, and the bill for it would arrive only two

Firmware failures don’t stay confined to software. They stop lines, knock out motors, and ruin batches. Once production is down, firmware stops being “just code.” Even so, many manufacturers still treat firmware as a fixed machine component: ship it once, assume it will hold up, and deal with the fallout later. That approach breaks down fast at scale. Last year, 61% of manufacturers faced unplanned downtime, causing nearly $1 billion in losses. At the same time, the software estate keeps getting

DevOps metrics are no longer limited to engineering teams. In 2026, they directly affect costs, delivery speed, and business risk. The financial impact of failure makes this clear. New Relic’s 2025 Observability Forecast shows that high-impact IT outages carry a median cost of $2 million per hour, or more than $33,000 per minute. The median annual cost of such outages reaches $76 million per organization. When downtime carries this level of cost, the metrics used to guide delivery and operation

AI-powered products can create real value, but only when they continue working reliably in the hands of customers. What makes this difficult is that their behavior doesn’t stay fixed after release. As data changes, so does model performance, which means that quality can decline even when no one touches the code. According to the 2024 DORA report, elite teams typically deploy on demand (multiple times per day), recover from failed deployments in under an hour, and keep change failure rates around

Java remains a powerhouse in software development, with 30% of developers naming it their primary language and its continued ranking as the third most-used language on the TIOBE Index. What sets Java apart is its ability to stay relevant where it matters most: building cloud-native applications, integrating AI solutions, driving IoT innovation, and powering scalable microservices. As we move into 2025, advancements like GraalVM and Project Loom push Java to new heights, making it faster, more e

Microservices architecture has absolutely changed the approach of developers to building large and complex systems reliably. And, over the last few years, they are rapidly gaining popularity. According to the research conducted by Statista in 2021, 85% of companies utilize microservices. Well, one of the technical goals of building the application is making it scalable and secure, and microservices allow us to do this, simultaneously making the final product fault-tolerant. So, by breaking down

Serving machine learning models as an API is a common approach for integrating ML capabilities into modern software applications. This process helps to simplify the development of applications and has multiple benefits, such as scalability, efficiency, flexibility, and accessibility. Basically, the aim of such an API is to integrate machine learning models into other components of the application, which enables the use of the predictive power of machine learning in real time. So, this process al

Using microservices for building apps is rapidly gaining popularity as they can bring so many different benefits to the business: they are safe and reliable, scalable, optimize the development time and cost, and are simple to deploy. In our previous articles, we discussed the best tools to manage microservices, the advantages and disadvantages of using microservices and the differences they have with monolith architecture, and hexagonal architecture. Despite multiple benefits, app development