Concertio Raises $4.2M Seed Round to Scale Operations

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Concertio is proud to announce the closing of our $4.2M Seed Round. The funds will be used to scale operations of our AIOps Optimizer platform and to further Concertio’s technology lead in dynamic, continuous, and static optimization. Please find the full press release below:

New York, New York, April 2, 2020 – Concertio, the leading provider of AI-powered performance optimization software, today announced the closing of its $4.2M Seed Round, led by Differential Ventures with participation from Empire State Development, The Jacobs Technion-Cornell Institute, NextLeap Ventures, Doug Alpuche, Wharton Alumni Angels, Plug and Play Venture Group, Scott Smith, J-Angels, Howard Morgan, David Magerman, Big Red Ventures, and Rough Draft 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.”

Modern servers employ an ever-growing number of configuration settings. Misconfiguring these settings drastically slows the performance of critical systems and leads to bloated infrastructure environments and cloud spend. Best performance is attained when these settings are tailored to the actual applications that currently run. Optimizing these hundreds of settings, however, is no small feat, as the number of potential configuration combinations grows exponentially. The result is a complex and costly manual tuning process that is challenging to maintain over time, even for experts, as any change in the system, such as a new software version, requires re-tuning.

Concertio has tackled this growing problem with its patented Optimizer products, 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.


About Concertio

Concertio is the leading provider of AI-powered system performance optimization tools from the lab to deployment. Concertio’s patented Optimizer boosts performance by tailoring the many system and application settings to work in concert with the currently running workloads, all automatically, continuously and in real-time. Welcome to the era of self-tuning systems!


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