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Virtana Launches Unified Platform for Migrating, Optimizing, and Managing Hybrid and Multi-Cloud Environments

Virtana announced a unified platform for migrating, optimizing, and managing application workloads across public, private, hybrid, and multi-cloud environments.

Using artificial intelligence for IT operations (AIOps) technologies, including machine learning and advanced data analytics, the cloud agnostic Virtana Platform solves the most difficult challenges facing enterprises as they seek to leverage public clouds. The platform enables a "know before you go" approach by providing observability into which workloads to migrate. It also ensures that unexpected costs and performance degradation are avoided once workloads are operating in the cloud.

With the Virtana Platform, enterprises can make data-driven migration decisions to get it right the first and every time workloads are migrated. The unified platform also simplifies optimization and management of workloads regardless of their location in an increasingly complex IT infrastructure.

"Enterprises recognize the value of public clouds," said Kash Shaikh, President & CEO of Virtana. "Many are already moving workloads to one or more public clouds. COVID-19 has accelerated their interest. But we are still in the early innings of public cloud adoption. Many tell us they moved some of the workloads back. When we ask why, enterprises say they do not know where to start, lack tools and visibility that can help them plan and execute migrations, and find managing workloads across hybrid environments complex."

"With the Virtana Platform, we are doing the hard work of simplifying cloud migration and hybrid cloud management," added Shaikh. "The Virtana Platform provides intelligent observability with data insights to empower enterprises to know before they go."

With the new Virtana Platform, enterprises gain the benefits of a single, unified, flexible platform:

- Intelligent. Leveraging machine learning and advanced data analytics, the Virtana Platform provides enterprises with intelligent observability of application workloads before they are moved to the cloud. Insights about workloads include application dependencies, how they will perform in various cloud environments, and their underlying IT infrastructure requirements. This embedded intelligence enables enterprises to make data-driven decisions about which workloads to move and where to place them to meet performance and cost requirements. Once workloads are moved, the platform enables ongoing optimization and management by providing real-time visibility and tools for taking action.

- Unified. The Virtana Platform is cloud agnostic, extending the company's ability to unify workload migration, optimization, and management across all the leading public cloud providers. These include AWS, Azure, Google Cloud Platform, Oracle, and VMware on AWS. This cloud agnostic approach gives enterprises the freedom to choose cloud providers based on the performance and cost requirements of their workloads. It also ensures that enterprises can leverage their investment in Virtana across future public, private, hybrid, and multi-cloud environments.

- Flexible. Specific capabilities of the Virtana Platform are delivered in modules, making it easy to cost-effectively add more functionality to the platform as needed. As new modules are delivered, the Virtana Platform provides a consistent experience and seamless integration of capabilities. Over time, all existing Virtana standalone products, including VirtualWisdom, CloudWisdom, WorkloadWisdom, and Cloud Migration Readiness will be incorporated into the platform. Multiple deployment options will include SaaS, managed service, and on-premises. These options let enterprises choose how and where to deploy modules based on their performance, cost, and security requirements.

Virtana platform modules for delivery in 2021 include Migrate, Optimize, Manage, and Validate, starting with Migrate. The Migrate module accelerates and de-risks the migration of existing application workloads to public clouds.

With Migrate, enterprises can:

- Know before they go. Application discovery and mapping capabilities enable enterprises to capture existing application infrastructure sets along with key performance and operational metrics. This enables enterprises to fully understand their application workloads before migrating them to public clouds.

- Prioritize intelligently. The platform uses machine learning and advanced data analytics to ensure consistent performance, reduce risk and cost, and accelerate migration efforts. This intelligence enables identification of groups of workloads that have similar resource requirements or deep interdependencies and should be or need to be migrated together.

- Select and configure for efficiency. Rightsizing and cloud cost optimization capabilities enable enterprises to select public cloud providers that are right for their needs. They also ensure the most efficient configurations based on risk tolerance, performance requirements, and resource consumption.

- Operationalize optimization. Process tracking and reporting lets enterprises maintain full visibility as part of their standard IT operational activities. This enables continual optimization as additional transitions between public cloud providers, zones, and physical locations are required.

"The growing complexity of hybrid and multi-cloud environments is hindering digital transformations," said Shaikh. "With Virtana, enterprises can be assured that expert guidance and the Virtana Platform will be with them every step of the way."

The Latest

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...

Virtana Launches Unified Platform for Migrating, Optimizing, and Managing Hybrid and Multi-Cloud Environments

Virtana announced a unified platform for migrating, optimizing, and managing application workloads across public, private, hybrid, and multi-cloud environments.

Using artificial intelligence for IT operations (AIOps) technologies, including machine learning and advanced data analytics, the cloud agnostic Virtana Platform solves the most difficult challenges facing enterprises as they seek to leverage public clouds. The platform enables a "know before you go" approach by providing observability into which workloads to migrate. It also ensures that unexpected costs and performance degradation are avoided once workloads are operating in the cloud.

With the Virtana Platform, enterprises can make data-driven migration decisions to get it right the first and every time workloads are migrated. The unified platform also simplifies optimization and management of workloads regardless of their location in an increasingly complex IT infrastructure.

"Enterprises recognize the value of public clouds," said Kash Shaikh, President & CEO of Virtana. "Many are already moving workloads to one or more public clouds. COVID-19 has accelerated their interest. But we are still in the early innings of public cloud adoption. Many tell us they moved some of the workloads back. When we ask why, enterprises say they do not know where to start, lack tools and visibility that can help them plan and execute migrations, and find managing workloads across hybrid environments complex."

"With the Virtana Platform, we are doing the hard work of simplifying cloud migration and hybrid cloud management," added Shaikh. "The Virtana Platform provides intelligent observability with data insights to empower enterprises to know before they go."

With the new Virtana Platform, enterprises gain the benefits of a single, unified, flexible platform:

- Intelligent. Leveraging machine learning and advanced data analytics, the Virtana Platform provides enterprises with intelligent observability of application workloads before they are moved to the cloud. Insights about workloads include application dependencies, how they will perform in various cloud environments, and their underlying IT infrastructure requirements. This embedded intelligence enables enterprises to make data-driven decisions about which workloads to move and where to place them to meet performance and cost requirements. Once workloads are moved, the platform enables ongoing optimization and management by providing real-time visibility and tools for taking action.

- Unified. The Virtana Platform is cloud agnostic, extending the company's ability to unify workload migration, optimization, and management across all the leading public cloud providers. These include AWS, Azure, Google Cloud Platform, Oracle, and VMware on AWS. This cloud agnostic approach gives enterprises the freedom to choose cloud providers based on the performance and cost requirements of their workloads. It also ensures that enterprises can leverage their investment in Virtana across future public, private, hybrid, and multi-cloud environments.

- Flexible. Specific capabilities of the Virtana Platform are delivered in modules, making it easy to cost-effectively add more functionality to the platform as needed. As new modules are delivered, the Virtana Platform provides a consistent experience and seamless integration of capabilities. Over time, all existing Virtana standalone products, including VirtualWisdom, CloudWisdom, WorkloadWisdom, and Cloud Migration Readiness will be incorporated into the platform. Multiple deployment options will include SaaS, managed service, and on-premises. These options let enterprises choose how and where to deploy modules based on their performance, cost, and security requirements.

Virtana platform modules for delivery in 2021 include Migrate, Optimize, Manage, and Validate, starting with Migrate. The Migrate module accelerates and de-risks the migration of existing application workloads to public clouds.

With Migrate, enterprises can:

- Know before they go. Application discovery and mapping capabilities enable enterprises to capture existing application infrastructure sets along with key performance and operational metrics. This enables enterprises to fully understand their application workloads before migrating them to public clouds.

- Prioritize intelligently. The platform uses machine learning and advanced data analytics to ensure consistent performance, reduce risk and cost, and accelerate migration efforts. This intelligence enables identification of groups of workloads that have similar resource requirements or deep interdependencies and should be or need to be migrated together.

- Select and configure for efficiency. Rightsizing and cloud cost optimization capabilities enable enterprises to select public cloud providers that are right for their needs. They also ensure the most efficient configurations based on risk tolerance, performance requirements, and resource consumption.

- Operationalize optimization. Process tracking and reporting lets enterprises maintain full visibility as part of their standard IT operational activities. This enables continual optimization as additional transitions between public cloud providers, zones, and physical locations are required.

"The growing complexity of hybrid and multi-cloud environments is hindering digital transformations," said Shaikh. "With Virtana, enterprises can be assured that expert guidance and the Virtana Platform will be with them every step of the way."

The Latest

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...