AppNeta announced its integrated SaaS Application Monitoring (SAM) bundle, a new offering in its Application Performance Management (APM) solution suite that delivers improved end user satisfaction and decreased operational costs.
SAM measures the performance, availability, and usage of all SaaS applications across the enterprise. SAM identifies applications in use, provides deep, granular visibility into performance of critical applications, and speeds troubleshooting of impacted services. With this unique combination of capabilities, AppNeta ensures visibility into the end-user experience with key applications, tied directly to infrastructure metrics throughout the internal and external application delivery networks.
“AppNeta addresses the needs of development and IT professionals who build, operate, and support business critical web applications in production, but also those professionals that support the users of SaaS-based external applications,” said Matt Stevens, President and CEO at AppNeta. “AppNeta is the only solution to provide SaaS-based full stack performance insight, from the application through the network to the end user. Using the AppNeta solution with SAM provides the deep insight to know who is using an app, whether it’s available, and how it is performing from the end user’s perspective,” says Matt Stevens, President and CEO at AppNeta.
SAM combines multiple data sources to deliver this insight and identifies applications, resource usage, and real user performance based on “from-the-wire” APM. This insight is augmented with specific extensions for key applications by executing transactions using a real web browser, along with AppNeta’s patented network technology for unprecedented full-stack insight. SAM aggregates this information in order to assure SLAs, visualize SaaS usage enterprise-wide, and identify slowdown or outages at specific end-user locations, via either a central SaaS-based dashboard or proactive alerts, allowing IT to isolate and remediate the source of the performance degradation.
SAM enables IT and operations managers to know quickly where problems are and what the cause is before users are impacted, providing the following key technical features:
- Automatic discovery of applications, identifying resource competition, usage patterns, and impact to other SaaS and non-SaaS applications. Multi-location visualizations enable users to view and measure traffic volume and traffic rates across multiple office locations and various applications and application classes and categories.
- Application extensions for targeted monitoring of SaaS applications such as Salesforce Microsoft Dynamics, NetSuite, Office365 and AthenaHealth. By using a real web browser, executing transactions from “behind the firewall” at user locations, SAM provides industry-standard Apdex scoring for each critical application, allowing quick, detailed troubleshooting without having to configure custom scripts.
- SaaS performance monitoring from either behind the corporate firewall or at remote offices or data centers as well as from AppNeta-managed Internet facing locations
- Web App SLA Report for assuring delivery of applications. By combining information about the end user experience and direct monitoring of the external network infrastructure for application readiness, SAM enables both proactive and ad-hoc analysis of the network’s ability to support all applications in use.
SAM supports most major SaaS applications, including Salesforce, Microsoft Dynamics, Office 360, and Google Docs.
The Latest
For many B2B and B2C enterprise brands, technology isn't a core strength. Relying on overly complex architectures (like those that follow a pure MACH doctrine) has been flagged by industry leaders as a source of operational slowdown, creating bottlenecks that limit agility in volatile market conditions ...
FinOps champions crucial cross-departmental collaboration, uniting business, finance, technology and engineering leaders to demystify cloud expenses. Yet, too often, critical cost issues are softened into mere "recommendations" or "insights" — easy to ignore. But what if we adopted security's battle-tested strategy and reframed these as the urgent risks they truly are, demanding immediate action? ...
Two in three IT professionals now cite growing complexity as their top challenge — an urgent signal that the modernization curve may be getting too steep, according to the Rising to the Challenge survey from Checkmk ...
While IT leaders are becoming more comfortable and adept at balancing workloads across on-premises, colocation data centers and the public cloud, there's a key component missing: connectivity, according to the 2025 State of the Data Center Report from CoreSite ...
A perfect storm is brewing in cybersecurity — certificate lifespans shrinking to just 47 days while quantum computing threatens today's encryption. Organizations must embrace ephemeral trust and crypto-agility to survive this dual challenge ...
In MEAN TIME TO INSIGHT Episode 14, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses hybrid multi-cloud network observability...
While companies adopt AI at a record pace, they also face the challenge of finding a smart and scalable way to manage its rapidly growing costs. This requires balancing the massive possibilities inherent in AI with the need to control cloud costs, aim for long-term profitability and optimize spending ...
Telecommunications is expanding at an unprecedented pace ... But progress brings complexity. As WanAware's 2025 Telecom Observability Benchmark Report reveals, many operators are discovering that modernization requires more than physical build outs and CapEx — it also demands the tools and insights to manage, secure, and optimize this fast-growing infrastructure in real time ...
As businesses increasingly rely on high-performance applications to deliver seamless user experiences, the demand for fast, reliable, and scalable data storage systems has never been greater. Redis — an open-source, in-memory data structure store — has emerged as a popular choice for use cases ranging from caching to real-time analytics. But with great performance comes the need for vigilant monitoring ...
Kubernetes was not initially designed with AI's vast resource variability in mind, and the rapid rise of AI has exposed Kubernetes limitations, particularly when it comes to cost and resource efficiency. Indeed, AI workloads differ from traditional applications in that they require a staggering amount and variety of compute resources, and their consumption is far less consistent than traditional workloads ... Considering the speed of AI innovation, teams cannot afford to be bogged down by these constant infrastructure concerns. A solution is needed ...