Granulate raises $30 million for AI that optimizes server workloads
February 4, 2021
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Granulate, a startup developing a platform that optimizes computing infrastructure, today announced it has raised $30 million, bringing its total raised to $45.6 million. The company claims the cash infusion will help it develop products that reduce the time engineers spend tuning enterprise system performance, freeing them up to pursue potentially more creative, impactful, and challenging projects.

Applying AI to datacenter operations isn’t a new idea — IDC predicts that 50% of IT assets in datacenters will run autonomously using embedded AI functionality by 2022. To this end, Concertio, which recently raised $4.2 million, provides AI-powered system optimization tools for boosting hardware and software performance. Facebook has said it employs AI internally to speed up searches for optimal server settings. And IBM offers a tool called Data Center Advisor with Watson that calculates datacenter health scores and anticipates possible issues or failures.

But Granulate’s solution uses software agents that can be installed on any Linux server in datacenters or cloud environments, including virtual machines. These AI-powered agents adapt to operating systems and kernels, prioritizing threads while taking into account each request’s processing stage and employing a network stack that enables parallelism. Granulate analyzes usage patterns and sizes to tailor allocations and memory for each app, autonomously crafting congestion control prioritizations between connections to optimize throughput for the current workload.


Above: Above: Granulate’s analytics dashboard. Image Credit: Granulate

Image Credit: Granulate

Granulate’s suite, which works with existing monitoring tools like Prometheus, AppDynamics, New Relic, Datadog, Dynatrace, and Elastic, is installed in dozens of production environments and tens of thousands of servers, including those owned by Coralogix and AppsFlyer. The company claims it’s more performant than most, improving the throughput of machines by up to 5 times and leading to an up to 60% compute cost savings and a 40% reduction in latency.

Startapp, a mobile data platform with over 1.5 billion monthly active users and 800,000 partner apps, reports that Granulate achieved a 30% reduction in average latency and a 25% processor utilization reduction, netting a 33% compute cost reduction. Another customer — advertising technology company Bigabid — says it managed to reduce compute costs by 60% within 15 minutes of deploying Granulate.

In October, as part of a new partnership with Microsoft Azure, Granulate launched a real-time continuous optimization solution for Azure cloud and hybrid environments in the Microsoft Azure Marketplace. Earlier in the year, Granulate announced support for software-as-a-service contracts in AWS Marketplace, enabling Amazon Web Services customers to prepay for Granulate based on expected usage tiers through contracts up to two years long.

Granulate claims that in the past 10 months, its 21 customers have saved over 3 billion hours of core usage, with the number of CPU cores under management rising by over 10 times to over 300,000 cores. It expects 2021 revenue to hit $5 million, up from between $1 million and $2 million in 2020.

“The pandemic created a large market opportunity for us, as companies of all shapes and sizes began implementing solutions that could reduce costs. Our unique optimization solution enables companies to achieve significant computing cost reduction and performance improvement on any infrastructure or environment with minimal effort or code changes,” CEO Asaf Ezra, who cofounded Granulate in 2018 with Tal Saig, told VentureBeat via email. “We look forward to continuing to help customers of all sizes and industries as we develop new solutions to drive further improvements in computing efficiency.”

Granulate’s series B round was led by Red Dot Capital Partners with the participation of Insight Partners, TLV Partners, and Hetz Ventures. Dawn Capital also joined as a new investor.


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