TL; DR: Virtana, founded in 2008 as Virtual Instruments Corporation, provides a hybrid cloud optimization platform used for enabling digital transformation. The technology provides organizations with the tools they need to migrate, optimize, and manage application workloads across public-, private-, hybrid-, and multi-cloud environments. Through artificial intelligence for IT operations (AIOps), Virtana is helping organizations migrate on-premises workloads to the cloud in a cost-effective manner.
Looking to bolster mission-critical applications with improved performance, resilience, and scalability, many businesses are making the move from on-prem datacenters to cloud-based solutions.
In a study by 451 Research, 56% of organizations surveyed said they were pursuing digital transformation while retaining some aspect of their existing application software. This could mean moving the application as is to the cloud, rearchitecting parts of the app for a better fit within a cloud environment, or moving it to the cloud with minor upgrades.
Of those remaining, 31% of respondents said they were rebuilding or replacing their apps via a cloud-first approach, and 13% stated they were not deploying workloads off-premises whatsoever.
Businesses looking to leverage the cloud, either by retaining or replacing mission-critical software, typically do so in the name of optimization. But getting there is a journey fraught with risk.
That’s where the Virtana Platform comes in. Virtana Platform’s embedded intelligence leverages machine learning and advanced data analytics to deliver a deep understanding of application workloads so businesses can make data-driven decisions about their cloud migrations and their costs.
“We are codifying the expertise required to develop an effective cloud migration strategy based on the dependency and complexity analysis and associated move-groups,” said John Gentry, CTO at Virtana. “That’s one of the common themes here that I take a lot of pride in: We’re consistently productizing expertise.”
“Virtana Platform leverages Artificial Intelligence for IT Operations (AIOps) to observe workloads’ behavior prior to moving to the cloud. The cloud-agnostic Software-as-a-Service (SaaS) platform enables organizations to unify workload migration, optimization, and management across all the leading public cloud providers to meet workloads’ performance needs and avoid unexpected costs.”
Years of Experience in Mission Critical Enterprise Datacenters
Virtana was founded in 2008 as Virtual Instruments, a company focused on infrastructure monitoring and analytics for mission-critical enterprise environments.
“At that time, most performance-sensitive environments were made up of physical or virtualized compute combined with enterprise-class data storage,” Gentry said. “So our heritage is in a deep understanding of high-performance application workloads comprised of compute, network, and storage.”
By 2014, the company was producing vast amounts of data through its infrastructure monitoring and analytics tools. The next logical step was to hire a team of data scientists to decipher the data and transform it into insight.
“Back then, even before AIOps was a buzzword, we were applying AI to data,” Gentry said. “At that point, we introduced our first analytics package, offering a whole new way of visualizing the data and making sense of it. Our approach to those analytics was always very purposeful.”
From there, the company continued to expand both its coverage across virtual and physical compute, enterprise storage, and HCI while expanding on the breadth of the associated suite of analytics.
In March 2016, Virtana merged with the workload performance analytics company Load DynamiX. In 2017, Virtana rebranded DynamiX’s product suite, which uses workload simulation to optimize cost and performance, as WorkloadWisdom.
“It was an interesting marriage because Load DynamiX was in the workload profiling, simulation, and testing space,” Gentry said. “What that brought to the table was this profound understanding of the workloads running on the infrastructure. We had deep visibility into the infrastructure, so it really brought testing together with monitoring.”
An Infrastructure-Agnostic Solution
In October 2016, Virtana acquired Xangati, a performance monitoring solution for virtual and cloud infrastructure, further enhancing and expanding its monitoring solution.
In August of 2019, it also acquired Metricly, a SaaS provider focused on cloud performance monitoring and cost analysis.
“We already had embedded AI around anomaly detection, but it was being applied specifically to workload anomalies in the datacenter,” Gentry said. “Metricly had this focus on the trade-offs between performance, risk, and cost. They were applying the AI to right-sizing, enabling the ability to dial in your risk tolerance while being alerted to anomalies in the associate cost, and that is very different than some of the other cost-optimization platforms out there.”
Ultimately, the combination of Virtual Instruments, Load Dynamix, Xangati, and Metricly led to Virtana’s comprehensive strength in hybrid infrastructure monitoring, analytics, and automation solutions.
“I think of the codification of the dependency mapping, with complexity and cost analysis in the Migrate module, as the day one solution or on-ramp,” Gentry said. “And then the Optimize and Manage modules drive your cost optimization and ongoing day two management.”
Virtana’s cloud-agnostic approach also makes the company unique.
“The premise behind the Virtana Platform is that we can enable intelligent workload placement regardless of location, whether that’s on-prem or in the cloud,” Gentry said. “Maybe because you’re a Microsoft shop, Azure makes sense for your back office, or maybe Google makes sense for your clinical trials. A multicloud approach is not about moving apps between clouds. It’s about picking the right cloud for the application — a fit-for-purpose cloud.”
The Perks of an All-in-One, AI-Based Platform
Over the years, Gentry said the Virtana team has observed a distinct shift from a cloud-first approach to what he calls “cloud smart.”
“Many companies that moved aggressively to the cloud have been burned,” he said. “They moved for cost reasons, and they realize it’s not more cost-effective. And so they’re coming to us and saying, ‘let’s be purposeful.’”
Virtana certainly provides the tools customers need to take a cloud-smart approach inclusive of hybrid cloud optimization, performance management, and cost management. Gentry told us that customers appreciate Virtana’s ability to combine such tasks into a single AI-powered observability platform.
“Our fundamental differentiation is having an all-in-one platform that’s stitched together seamlessly,” he said. “So I can understand what I have on-prem. I can use that to migrate it. Once I migrate it, I can cost optimize it. And ultimately, I can manage it all in the end. If you want to do that elsewhere, you’d have to go to four different if not six different providers.”
Virtana’s years of experience with artificial intelligence (AI) and machine learning (ML) also serve as a key differentiating factor.
“In 2019, when AI, ML, and AIOps were on the tip of everybody’s tongue, we had already been doing this for five-plus years,” Gentry said. “That’s the thing about machine learning — if you’re a brand new ML company, how much learning have your algorithms done?”
A Future Focus on Flexibility
Moving forward, the Virtana team plans to provide companies with the additional option to consume the platform on-premise as a managed service.
“We hear loud and clear from the customers, who can’t move to the cloud but want all the capabilities of our SaaS platform, just on-prem,” Gentry said. “So we’re packaging up what we build in the cloud and delivering that continuous integration, continuous delivery on-prem, giving customers a real option.”