A bottleneck-ridden code, or in other words, inefficient software, consumes more compute resources than necessary. In a traditional, on-premises setup, inefficient software often slows application down or requires more frequent hardware upgrades, but the costs remain somewhat predictable. In the cloud however, the story is completely different. The on-demand nature of the cloud, where resources scale up to meet demand, means that inefficient software immediately translates into increased costs. This inefficiency exponentially scales with growth, meaning that as companies scale, inefficient software contributes to soaring cloud costs which can become detrimental to profit margins and pose a serious financial burden.
Beyond consuming more resources, inefficient software can significantly contribute to unstable applications. This instability can manifest as frequent crashes, slow response times, and unpredictable behavior, which in turn affects user experience and reliability. The more resources an application consumes unnecessarily, the higher the likelihood of overloading the system or encountering conflicts that lead to instability.
Furthermore, there's an environmental angle to consider. Inefficient software requires more computing power, which in turn requires more energy. This increased energy usage has a direct environmental impact, contributing to larger carbon footprints for businesses.
“Software today is massively inefficient; it’s become prime time again for software programmers to get really good at optimization.” - Marc Andreessen, the US entrepreneur and investor (a16z Podcast, 2020)
The good news is that companies don’t need to sit and pray for cloud vendors to reduce their prices or hope that global warming isn’t actually a thing. Instead, companies can take matters into their own hands, cut their excess cloud costs and improve performance by optimizing the code of their applications. Alas, this is easier said than done!
The nature of code optimization, since the early computing days, has always been manual and labor-intensive. Profiling tools emerged in the 1980s and provided insights into the runtime behavior of programs, allowing developers to identify hotspots in the code where most of the execution time was spent. Since then, profilers have gained popularity and developed to support changing computing paradigms and new programming languages. That said, they ultimately haven’t evolved beyond providing data to support the process of performance analysis.
With the rise of artificial intelligence (AI), it was only a matter of time until performance analysis would progress to the next stage in its evolution; automation of bottlenecks identification.
Raven is proud to pioneer the evolution of performance analysis. Powered by (an AI with 4 patents pending and 2 more on the way), Raven pinpoints bottlenecks in your production code and hands them to you on a silver platter (intuitive UI). The bottlenecks are also sorted by priority so you can focus on improving code while ensuring maximum impact of every minute spent on optimization.
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If you want to improve application performance and application stability or reduce cloud costs and manual effort, Raven is for you.
Raven offers a free evaluation, so you can see for yourself how much gold is buried in your code.
So, what are you waiting for? 🙂