CopperEgg's cloud monitoring solution for servers, websites, applications and end users is now included in AWS Activate, a program from Amazon Web Services (AWS) that helps early stage companies get started on the AWS cloud.
Startups using AWS Activate now have access to CopperEgg’s intuitive, user-friendly dashboard to monitor, troubleshoot and optimize applications in the cloud, from server to website to end user.
Launched in October 2013, AWS Activate provides startups with resources needed to help them get started on the AWS cloud including AWS credits, training, developer support and special offers from select third-party companies.
CopperEgg’s solutions help ensure quality of service by analyzing real user experience and infrastructure behavior across physical, virtual, cloud and on-premises environments. Organizations using CopperEgg immediately know the availability and health of their applications and are alerted to potential failures, long before customers experience a slow down.
“We’ve seen first hand how application performance can make or break an organization, especially a startup,” said Chad Bockius, CEO of CopperEgg. “AWS Activate offers tremendous value to early stage companies and we’re proud to be involved and providing real-time visibility into infrastructure health.”
As an AWS Partner Network (APN) Advanced Technology Partner and AWS Marketplace vendor, CopperEgg has experience helping AWS customers monitor their cloud infrastructure, including Amazon Elastic Compute Cloud (Amazon EC2).
The Latest
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 ...
AI is the catalyst for significant investment in data teams as enterprises require higher-quality data to power their AI applications, according to the State of Analytics Engineering Report from dbt Labs ...