Blue Medora announced a $10 million investment from growth equity investor Edison Partners.
Proceeds will be used to accelerate development and enterprise market adoption of the company’s innovations including the recently introduced BindPlane service.
“There has been awe-striking advancement in monitoring in the past two years thanks to AI, but the industry is still relying on a hodgepodge of data collection strategies. It’s painful for end-users, who struggle to get clear insights when things go wrong,” explains Nathan Owen, CEO and co-founder of Blue Medora. “Edison’s partnership validates the value of decoupling data collection from data analytics and will help us to elevate monitoring to its fullest potential.”
“There is a massive amount of value in using untapped IT performance data to proactively prevent crippling system outages like we’ve seen in airlines, retailers and other technology-intensive industries,” said Lenard Marcus, General Partner at Edison Partners, who led the investment and will join the board of the company. “Blue Medora has a one-of-a-kind vantage point in this market, with significant differentiation and momentum. We are thrilled to partner with Nathan and team in reshaping the expectations for IT performance monitoring, management and data-analytics.”
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 ...