Startup City Magazine

Wavefront Snaps $52 Million in Series B Funding

Bengaluru: Wavefront, the hosted platform for ingesting, storing, visualizing and alerting on time series data, secures $52 million in Series B funding, led by Tenaya Capital, Sutter Hill Ventures and Sequoia Capital. The capital will be utilized to boost the research & development activities in order to expand the products offerings as well as enhance the sales, marketing and support operations across the globe. The entity has experienced hyper-growth since the Series A funding of $11.5 million in February 2016 and achieved milestones like increase in aggregate metric data ingestion rate into its service by more than 10x.

Commenting on the fund raising, Pete Cittadini, President  & CEO, Wavefront says, “Cloud-enabled companies adopting Wavefront get crucially valuable insights that no other monitoring platform can offer, so their business experiences less degradation, fewer outages and more customer success. We surpassed key milestones this past year, so it became the ideal time to raise this round, and fuel our already impressive adoption into the SaaS 250, as well as into digital enterprises and e-commerce companies that are fully embracing the cloud”.

Incorporated by Dev Nag, Durren Shen, Clement Pang in 2013, Wavefront is based on stream processing approach invented at Google which allows engineers to manipulate time series data with unparalleled power. Wavefront’s metrics monitoring service unifies and processes millions of data points per second, delivering insights at the speed of thought, for enterprises at scale.

Companies with modern applications and agile infrastructure built on software, including SaaS offerings, digital enterprise platforms, consumer web platforms and IoT systems can employ Wavefront to improve its performance, availability and customer experience. The company helps software leaders like Workday, Box, Lyft, Microsoft, Intuit and Groupon to ensure superior customer experience with the combination of the most powerful query language in monitoring running against a unified, full detail, big data metric store in real-time with no limits.

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