SOASTA announced its Fall 2016 Release, extending its Digital Performance Management (DPM) platform with the introduction of deep data science integration across the product portfolio; correlation with performance metrics and key marketing analytics tools; expansion of predictive analytics into new verticals; and enhanced Single-Page Applications (SPA) support for performance monitoring and load testing.
The Fall release also illustrates SOASTA’s commitment to “BizOps,” breaking down silos that exist between technical and business teams and allowing them to frictionlessly pursue a common goal – delivering an excellent customer experience and incremental revenue.
Fall Product Release Highlights
- Deeper data science integration and expanded capabilities designed for analyzing desktop and mobile traffic. With this release, mPulse also becomes formally powered by SOASTA’s data science engine, which allows for RUM-based testing and integration merged with SOASTA mPulse dashboards, real-time campaign performance, and real-time visibility and correlation with key marketing analytics tools – Adobe Analytics, IBM Coremetrics and Google Analytics.
- Already a key part of mPulse predictive analytics, the “what-if” dashboard now offers expanded functionality that enables users to select any conversion or revenue metric, such as session length or duration, bounce rate, and non-revenue-related conversions. This takes predictive analytics to a new level for verticals outside of e-commerce/retail. Most importantly, IT operations can now accurately forecast the value of their web and mobile performance improvements.
- With SOASTA’s new SPA support for performance testing, customers can easily create performance load test for SPA sites using the Chrome browser recorder extension. It records SPA apps, catching the “page” changes that are no longer full HTML downloads but instead are smaller region changes that deliver a faster end user experience.
- Measuring SPA is also improved with JavaScript error tracking, alerting and analytics in mPulse and provides, at a glance, critical error information, such as error rate, session experiencing errors, errors broken down by build, and recent error stack. “I’ve run into issues with other software and service providers not being able to fully support the Meteor framework. SOASTA was ahead of the curve in already supporting SPA-based applications. The implementation was a piece of cake,” said Dustin Behr, IT Development Supervisor at HOM Furniture.
- SOASTA can isolate first-party resources to determine the impact from third-party resources to remediate problems and establish SLAs for third- party vendors.
The SOASTA Fall 2016 Release is now available to all SOASTA customers in a limited release, and general availability will be in late October.
The Latest
In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...
Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...
In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ...
Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...
Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...
Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...
The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...
The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...
In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...
AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.