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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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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 ...

Misaligned architecture can lead to business consequences, with 93% of respondents reporting negative outcomes such as service disruptions, high operational costs and security challenges ...

A Gartner analyst recently suggested that GenAI tools could create 25% time savings for network operational teams. Where might these time savings come from? How are GenAI tools helping NetOps teams today, and what other tasks might they take on in the future as models continue improving? In general, these savings come from automating or streamlining manual NetOps tasks ...

IT and line-of-business teams are increasingly aligned in their efforts to close the data gap and drive greater collaboration to alleviate IT bottlenecks and offload growing demands on IT teams, according to The 2025 Automation Benchmark Report: Insights from IT Leaders on Enterprise Automation & the Future of AI-Driven Businesses from Jitterbit ...

A large majority (86%) of data management and AI decision makers cite protecting data privacy as a top concern, with 76% of respondents citing ROI on data privacy and AI initiatives across their organization, according to a new Harris Poll from Collibra ...

According to Gartner, Inc. the following six trends will shape the future of cloud over the next four years, ultimately resulting in new ways of working that are digital in nature and transformative in impact ...

2020 was the equivalent of a wedding with a top-shelf open bar. As businesses scrambled to adjust to remote work, digital transformation accelerated at breakneck speed. New software categories emerged overnight. Tech stacks ballooned with all sorts of SaaS apps solving ALL the problems — often with little oversight or long-term integration planning, and yes frequently a lot of duplicated functionality ... But now the music's faded. The lights are on. Everyone from the CIO to the CFO is checking the bill. Welcome to the Great SaaS Hangover ...

Regardless of OpenShift being a scalable and flexible software, it can be a pain to monitor since complete visibility into the underlying operations is not guaranteed ... To effectively monitor an OpenShift environment, IT administrators should focus on these five key elements and their associated metrics ...