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Pico Launches Corvil Cloud Analytics

Pico has expanded the reach and visibility of Corvil Analytics into the cloud with the launch of Corvil Cloud Analytics.

Pico’s Corvil Analytics has a 20-plus year legacy across financial services in extracting and correlating technology and transaction performance intelligence from global dynamic network environments. Corvil’s high throughput, lossless, granularly time-stamped data capture provides an incredibly rich data source that can be used for broader analytics and use cases, including trade analytics. Corvil is available across multiple environments including colocation and on-prem, and now those same attributes are available in the cloud with Corvil Cloud Analytics.

“As companies look to move real-time applications to the cloud, they struggle with visibility when utilizing existing cloud monitoring solutions,” said Stacie Swanstrom, CPO at Pico. “There is a need for deeper visibility to fill those voids, and Corvil Cloud Analytics is the solution...Corvil Cloud Analytics provides our clients with the real-time analytics required to migrate their most critical workloads to the cloud, with confidence.”

Highlights of Corvil Cloud Analytics include:

- Maximum Visibility: Measures every order, every market data tick and every packet to fill the missing gap of visibility needed to manage real-time performance in public cloud environments

- Granular Instrumentation: Provides per-packet and per-application message analytics alongside Corvil’s AppAgent to instrument internal application performance

- Corvil Analytics: Provides all functions of Corvil Analytics including network congestion analytics for public cloud infrastructure, and per-hop trading and market data analytics for cloud-hosted deployments

- Flexibility: Pay for only what is needed in the public cloud

With the launch of Corvil Cloud Analytics, and as exchanges partner with the major cloud providers to bring trading into the cloud, Corvil can now provide a single pane of glass for monitoring colocation, on-prem and cloud environments together.

“We had the vision to provide clients the same technology, visibility and rich analytics they’ve come to rely on through Corvil,” Swanstrom said. “Since Corvil Cloud Analytics is software only, this accelerates our deployments and also provides an expedited avenue for proof-of-concept use cases. It’s now easier than ever for clients to access the platform so they can see firsthand what makes Corvil an industry leader in data analytics.”

Corvil Cloud Analytics provides the highly granular, real-time Corvil visibility required to understand the cause of variable performance that continues to impact real-time applications running in the public cloud. With cloud applications, there is no hardware CapEx costs, lead times, or shipping and installation challenges. Corvil Cloud Analytics is simple to scale, easy to deploy and can be up and running in hours instead of weeks. Corvil’s visibility and intelligence is now available for businesses wanting the competitive edge in the cloud.

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In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

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 ...

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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 ...

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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.

Pico Launches Corvil Cloud Analytics

Pico has expanded the reach and visibility of Corvil Analytics into the cloud with the launch of Corvil Cloud Analytics.

Pico’s Corvil Analytics has a 20-plus year legacy across financial services in extracting and correlating technology and transaction performance intelligence from global dynamic network environments. Corvil’s high throughput, lossless, granularly time-stamped data capture provides an incredibly rich data source that can be used for broader analytics and use cases, including trade analytics. Corvil is available across multiple environments including colocation and on-prem, and now those same attributes are available in the cloud with Corvil Cloud Analytics.

“As companies look to move real-time applications to the cloud, they struggle with visibility when utilizing existing cloud monitoring solutions,” said Stacie Swanstrom, CPO at Pico. “There is a need for deeper visibility to fill those voids, and Corvil Cloud Analytics is the solution...Corvil Cloud Analytics provides our clients with the real-time analytics required to migrate their most critical workloads to the cloud, with confidence.”

Highlights of Corvil Cloud Analytics include:

- Maximum Visibility: Measures every order, every market data tick and every packet to fill the missing gap of visibility needed to manage real-time performance in public cloud environments

- Granular Instrumentation: Provides per-packet and per-application message analytics alongside Corvil’s AppAgent to instrument internal application performance

- Corvil Analytics: Provides all functions of Corvil Analytics including network congestion analytics for public cloud infrastructure, and per-hop trading and market data analytics for cloud-hosted deployments

- Flexibility: Pay for only what is needed in the public cloud

With the launch of Corvil Cloud Analytics, and as exchanges partner with the major cloud providers to bring trading into the cloud, Corvil can now provide a single pane of glass for monitoring colocation, on-prem and cloud environments together.

“We had the vision to provide clients the same technology, visibility and rich analytics they’ve come to rely on through Corvil,” Swanstrom said. “Since Corvil Cloud Analytics is software only, this accelerates our deployments and also provides an expedited avenue for proof-of-concept use cases. It’s now easier than ever for clients to access the platform so they can see firsthand what makes Corvil an industry leader in data analytics.”

Corvil Cloud Analytics provides the highly granular, real-time Corvil visibility required to understand the cause of variable performance that continues to impact real-time applications running in the public cloud. With cloud applications, there is no hardware CapEx costs, lead times, or shipping and installation challenges. Corvil Cloud Analytics is simple to scale, easy to deploy and can be up and running in hours instead of weeks. Corvil’s visibility and intelligence is now available for businesses wanting the competitive edge in the cloud.

The Latest

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

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.