Carbon Relay, now StormForge, announced the acquisition of StormForger, a performance testing as a service platform.
With the acquisition, Carbon Relay is renaming to StormForge and is announcing the integrated platform as the first available to DevOps and IT professionals that can proactively and automatically test, analyze, configure, optimize, and release containerized applications to remove the risk of negatively affecting user experience and customer trust.
The StormForge Platform uses machine learning to study, replicate, and stress-test application environments, and then proactively learn and deploy optimal configurations, schedules, and resource allocations—all with minimal engineering effort. The StormForge Platform can be integrated into an organization's CI/CD workflow to build automated, continuous performance testing and optimization into the release process. The acquisition of StormForger's technology introduces a core layer of proactive application optimization into the Platform with enhanced performance and load testing capabilities.
The StormForge Platform now offers a comprehensive suite of features aimed at identifying and deploying efficient containerized applications. It is built with the vision of helping DevOps team members release their applications with confidence and IT leaders to realize the promise of a cloud-native approach through the combination of tools, community, and expertise.
- Performance Testing as a Service: Quickly create repeatable, automated load tests as code that can be incorporated into your CI/CD workflow to foster shift-left testing for better performance and reduced deployment risk.
- Machine Learning-Powered Application Optimization: Analyze how your application will perform in real-world scenarios and optimize based on your goals for performance, stability, and cost.
- Democratize Insights into Application Behavior: Share data, information, and recommendations with your whole organization. Let a sustainable performance and optimization culture evolve.
"Our mission, fueled by today's acquisition, is to transform containerized application optimization from a reactive engineering function to a proactive, automatic, and continuous process," said Matt Provo, co-founder and CEO of the newly named StormForge. "Technology leaders adopt containerized applications and cloud-native architectures to drive digital transformation and enable innovation. But under pressure to ship code faster, most developers trade quality for speed and react to problems after their customers and users have already experienced them. Transformation efforts ultimately fall short, deadlines are missed, applications fail, and costs spiral out of control. Navigating containerized environments is complex. The StormForge Platform breaks through the complexity and removes the guesswork from getting the most out of your applications."
With StormForge, users can leverage machine learning to automatically transform applications for cloud-native success before ever encountering issues and dramatically cut operational costs. By combining performance testing with application optimization, cloud-native applications automatically become more reliable, scalable, and cost-efficient.
"Coming from a strong focus on performance testing for all kinds of applications, we realized that our visions are fundamentally aligned. Taming complexity and everything it entails is one of the fundamental problems in our industry," Lars Wolff, co-founder of the acquired StormForger commented. "By combining our platforms and our expertise in our respective fields we are able to close an important gap unlike anyone else is able to. Automated optimization, driven by machine learning, is the perfect continuation of our vision which started our journey in 2014."
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 ...