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Concertio Raises $4.2M in Seed Funding

Concertio announced the closing of its $4.2M Seed Round, led by Differential Ventures.

The funds will be used to scale operations of its AIOps Optimizer platform and to further the company’s technology lead in dynamic, continuous, and static optimization.

“We’re entering the era of self-tuning servers, where servers automatically adjust their settings dynamically in real-time according to the workloads that they run,” says Dr. Tomer Morad, co-founder and CEO of Concertio. “Our Optimizer products transform general-purpose systems into high-performant special-purpose systems, thereby boosting performance and slashing infrastructure costs.”

Concertio's Optimizer products are aimed at addressing the system performance challenges enterprises face, starting from development in the lab all the way to deployment in production.

Leveraging machine-learning technology, Concertio Optimizer enhances applications and systems to achieve maximum performance through the optimization of the myriad of configuration settings employed in these complex systems. Concertio Optimizer features dynamic, continuous and static modes of optimization to tackle any parameter and resource tuning challenge enterprises face today.

Concertio Optimizer products are used in a variety of use-cases, including maximizing system performance, reducing IT and cloud costs, Kubernetes resource optimization, minimizing latencies in high-frequency trading platforms, compiler flag mining, database optimization, optimization of CPU and ASIC products’ defaults, maximizing networking bandwidth, maximizing benchmark performance and more. Concertio products deliver out-of-the-box support for configuration parameters in numerous platforms, including Intel CPUs, Linux, Kubernetes, OpenMPI, Hadoop, MongoDB, MySQL, PostgreSQL, Redis, Java, PHP, NGNIX, Apache Web Server, HHVM, Mellanox NICs, GCC flags, LLVM flags, and more. Concertio features three modes of optimization: agent-based dynamic real-time optimization for use in production servers, continuous optimization where static optimization is implemented within the CI/CD pipeline, and static optimization for use by hardcore performance engineers and IT professionals. Intel, Marvell and Mellanox have each published use-cases with Concertio.

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Concertio Raises $4.2M in Seed Funding

Concertio announced the closing of its $4.2M Seed Round, led by Differential Ventures.

The funds will be used to scale operations of its AIOps Optimizer platform and to further the company’s technology lead in dynamic, continuous, and static optimization.

“We’re entering the era of self-tuning servers, where servers automatically adjust their settings dynamically in real-time according to the workloads that they run,” says Dr. Tomer Morad, co-founder and CEO of Concertio. “Our Optimizer products transform general-purpose systems into high-performant special-purpose systems, thereby boosting performance and slashing infrastructure costs.”

Concertio's Optimizer products are aimed at addressing the system performance challenges enterprises face, starting from development in the lab all the way to deployment in production.

Leveraging machine-learning technology, Concertio Optimizer enhances applications and systems to achieve maximum performance through the optimization of the myriad of configuration settings employed in these complex systems. Concertio Optimizer features dynamic, continuous and static modes of optimization to tackle any parameter and resource tuning challenge enterprises face today.

Concertio Optimizer products are used in a variety of use-cases, including maximizing system performance, reducing IT and cloud costs, Kubernetes resource optimization, minimizing latencies in high-frequency trading platforms, compiler flag mining, database optimization, optimization of CPU and ASIC products’ defaults, maximizing networking bandwidth, maximizing benchmark performance and more. Concertio products deliver out-of-the-box support for configuration parameters in numerous platforms, including Intel CPUs, Linux, Kubernetes, OpenMPI, Hadoop, MongoDB, MySQL, PostgreSQL, Redis, Java, PHP, NGNIX, Apache Web Server, HHVM, Mellanox NICs, GCC flags, LLVM flags, and more. Concertio features three modes of optimization: agent-based dynamic real-time optimization for use in production servers, continuous optimization where static optimization is implemented within the CI/CD pipeline, and static optimization for use by hardcore performance engineers and IT professionals. Intel, Marvell and Mellanox have each published use-cases with Concertio.

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UK IT leaders are reaching a critical inflection point in how they manage observability, according to research from LogicMonitor. As infrastructure complexity grows and AI adoption accelerates, fragmented monitoring environments are driving organizations to rethink their operational strategies and consolidate tools ...

For years, many infrastructure teams treated the edge as a deployment variation. It was seen as the same cloud model, only stretched outward: more devices, more gateways, more locations and a little more latency. That assumption is proving costly. The edge is not just another place to run workloads. It is a fundamentally different operating condition ...

AI can't fix broken data. CIOs who modernize revenue data governance unlock predictable growth-those who don't risk millions in failed AI investments. For decades, CIOs kept the lights on. Revenue was someone else's problem, owned by sales, led by the CRO, measured by finance. Those days are behind us ...

Over the past few years, organizations have made enormous strides in enabling remote and hybrid work. But the foundational technologies powering today's digital workplace were never designed for the volume, velocity, and complexity that is coming next. By 2026 and beyond, three forces — 5G, the metaverse, and edge AI — will fundamentally reshape how people connect, collaborate, and access enterprise resources ... The businesses that begin preparing now will gain a competitive head start. Those that wait will find themselves trying to secure environments that have already outgrown their architecture ...

Ask where enterprise AI is making its most decisive impact, and the answer might surprise you: not marketing, not finance, not customer experience. It's IT. Across three years of industry research conducted by Digitate, one constant holds true is that IT is both the testing ground and the proving ground for enterprise AI. Last year, that position only strengthened ...

A payment gateway fails at 2 AM. Thousands of transactions hang in limbo. Post-mortems reveal failures cascading across dozens of services, each technically sound in isolation. The diagnosis takes hours. The fix requires coordinated deployments across teams ...

Every enterprise technology conversation right now circles back to AI agents. And for once, the excitement isn't running too far ahead of reality. According to a Zapier survey of over 500 enterprise leaders, 72% of enterprises are already using or testing AI agents, and 84% plan to increase their investment over the next 12 months. Those numbers are big. But they also raise a question that doesn't get asked enough: what exactly are companies doing with these agents, and are they actually getting value from them? ...

Many organizations still rely on reactive availability models, taking action only after an outage occurs. However, as applications become more complex, this approach often leads to delayed detection, prolonged disruption, and incomplete recovery. Monitoring is evolving from a basic operational function into a foundational capability for sustaining availability in modern environments ...

In MEAN TIME TO INSIGHT Episode 22, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses DNS Security ... 

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