
Akamai Technologies has entered into an agreement to acquire SOASTA.
The acquisition is intended to give Akamai customers greater visibility into the business impact of their website and application optimization strategies. The all-cash transaction is expected to close early in the second quarter.
“Akamai has long been associated with delivering exceptional technology solutions for optimizing web and mobile application performance,” explained Ash Kulkarni, SVP and GM, Web Performance and Security, Akamai. “The addition of SOASTA’s technology is intended to give our customers new ways to measure, optimize and validate the business impact of their web performance strategies.”
Through its acquisition of SOASTA, Akamai plans to add several new capabilities to its Web Performance Solutions portfolio. Akamai customers will have improved ability to accurately measure how real users experience their applications, and how that experience impacts their behavior. This will help customers prioritize and implement the most impactful performance optimization strategies to positively affect business outcomes. Through SOASTA solutions, Akamai customers will then be able to test optimizations at scale prior to deployment and validate the business impact of those optimizations once they are live in production. The result is a comprehensive set of cloud-based performance and business outcome optimization solutions.
“As important as web and mobile site and application optimization is to online businesses, the ability to truly understand the result of those optimization strategies is crucial to continued success,” stated Tom Lounibos, CEO, Co-founder of SOASTA. “This acquisition will provide Akamai customers, many of whom are already SOASTA customers, with a new way to measure and test the optimizations they are making to their sites, and validate the actual business impact of their site’s performance.”
The Latest
Cloud outages are usually described as technical failures. When a service goes down, a dependency breaks, or a region has issues, the focus immediately shifts to infrastructure. But if you look closely at how these incidents actually unfold, the root cause is rarely the technology itself. It is almost always tied to decisions made earlier, during design, implementation, or day-to-day operations. The system behaves the way it was built. The real question is how it was built ...
77% of leaders say their teams need AI skills urgently. 64% say their organization plans to train current employees rather than hire new ones. So far, so reasonable. The part that surprised me is who's been put in charge: 34% of those leaders say IT and engineering own the AI skills mandate. Learning and Development or HR own it at 7% of organizations. That's roughly five-to-one in favor of the people who understand the tools, over the people whose actual job is teaching adults how to learn new ones ...
In the ever-evolving digital landscape, enterprises are increasingly focused on enhancing their observability stacks to gain deeper insights into their IT environments. Observability has become a cornerstone of modern IT operations, enabling organizations to monitor, diagnose, and optimize their systems with unprecedented precision. However, a critical piece of the puzzle often goes unnoticed in this transformation: IBM i ...
We just surveyed 300 frontend and mobile engineers across 16 countries, and the finding that keeps sticking with me isn't the one about AI. It's this: 74% of engineering teams rate themselves in the "middle" of the observability maturity scale. Not reactive, not strategic. Stuck in the middle. They have dashboards, they have tracing, they have alerts. And yet when something goes wrong, they still can't tell you why ...
In MEAN TIME TO INSIGHT Episode 25, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses AI's impact on the Wide Area Network (WAN) ...
Application performance monitoring (APM) dashboards are only as useful as what they are configured to measure. The default setup covers obvious failure modes such as downtime, error spikes, and latency breaches, but it does not cover everything. Some failures produce no alerts or anomalies. The dashboard stays green while users experience a broken product. Here are six signs that is happening ...
The race to deploy AI is largely over. Most enterprises have entered it. The question now is not whether artificial intelligence is running inside the organization. The question is whether anyone is genuinely responsible for what it does. That is not a technical question. It is a leadership one. And most organizations are not yet structured to answer it honestly ...
A new analysis of 250 real-world queries across common retail tasks, such as product pricing, availability, ratings, shipping and specifications, reveals systemic inefficiency at the heart of web-based AI agents. On average, 97.9% of the data retrieved by agents from live web pages is irrelevant to the query being answered. Specifically, the average page ingested ran nearly 9,000 characters, while the average answer was just 32 characters, resulting in a noise-to-signal ratio of 278:1. Price queries were the most extreme outlier, with noise rates approaching 99.5%. That's not a rounding error. That's a structural problem ...
The enterprises that will define the next decade are not the ones that deployed the most technology. They are the ones who understood what their technology was actually doing. That distinction is not a philosophical point. It is the central operational challenge facing every organization that has spent the last five years modernizing at speed ...
AI is becoming the operating system of the enterprise. It acts as an invisible coordination layer that understands intent, connects systems, and executes work across complex SaaS environments. Previously, employees had to click through multiple systems — CRM, ERP, support tools, collaboration platforms — to complete a single task. Now, instead of navigating each application manually, they can simply state what they need to accomplish ...