Last year, when we wrote about new investment for Israeli startup Granulate -- which applies AI to high-priority computing workloads to optimize how they travel across a customers' cloud and on-premises networks -- we noted that the network management space was going through some consolidation, with point solutions getting snapped up by platform players. Well today, that trend is touching the startup itself: chip giant Intel has announced that it is acquiring Granulate, to continue extending both its operations in Israel, and the tools that Intel provides to customers to better manage traffic on Intel-powered kit.

The acquisition had been a badly-kept secret in Granulate's home market, with a number of publications reporting that it was in the works for about a week now. Well-placed sources tell us the acquisition is a $650 million deal, although Intel and Granulate do not give an actual number in their press release today confirming the news.

The deal is expected to close in Q2 2022, and all 120 Granulate employees are expected to join Intel.

“Today’s cloud and data center customers demand scalable, high-performance software to make the most of their hardware deployments,” said Sandra Rivera, executive vice president and general manager of the Datacenter and AI Group at Intel, in a statement. “Granulate’s cutting-edge autonomous optimization software can be applied to production workloads without requiring the customer to make changes to its code, driving optimized hardware and software value for every cloud and data center customer.”

“We are building our portfolio of software optimization tools that offer flexible and scalable capabilities that allow us to meet the growing demand of the ubiquitous compute era," added Greg Lavender, chief technology officer, senior vice president and general manager of the Software and Advanced Technology Group at Intel. "Granulate’s innovative approach to real-time optimization software complements Intel’s existing capabilities by helping customers realize performance gains, cloud cost reductions and continual workload learning.”