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Pliant Launches Observability Automation Solution

Pliant announced the launch of an Observability Automation solution developed specifically for leading performance monitoring vendors and their customers.

The Pliant Observability Solution elevates performance monitoring for operations teams at large enterprises, carriers, and managed service provider organizations. This new offering revolutionizes how teams automate, integrate, and connect their environments. Rather than task talented and costly developers to manage performance monitoring manually, Pliant customers can build workflows quickly, efficiently, and repeatably. Featuring dozens of pre-built workflows that address common actions, the Pliant Observability solution accelerates device and data onboarding, integrates actions and outcomes, and connects the IT stack to drive rapid and repeatable observability.

“When we developed Pliant, we knew one of the most impactful use cases was going to be automating, enriching, and enhancing performance monitoring,” said Pliant co-founder and CEO Vess Bakalov. “My time as founder and CTO at SevOne demonstrated the importance of visibility and intelligence, but making them automatically actionable was a perennial challenge for customers, and that remains true today. This solution combines two powerful capabilities – modern monitoring and API-driven workflow automation – to elevate both to new heights for our customers.”

With Pliant Connect, performance monitoring customers can leverage the platform to rapidly and automatically onboard the data that powers visibility and insight, accelerating time-to-value at a fraction of the cost currently spent on services, development, and engineering resources.

With Pliant Actions, performance monitoring customers can automate the repetitive, traditionally manual alert-driven actions they receive, increasing productivity and root cause resolution. Pliant’s solution, purpose-built for leading NPM vendors, seamlessly delivers on the promise of observability unlike anything available today.

The Pliant Observability Solution offers:

- Automated device and data onboarding to increase time to value

- Automated remediation to reduce manual actions and increase productivity

- Connections for the tech stack that drive rapid and repeatable observability and root cause identification

“The complexity incurred with Infrastructure modernization has increased the demands placed on operations and engineering teams, who are being forced to do more with less,” added Chris Rohter, Vice President of Marketing at Pliant. “The dynamic nature of today’s infrastructure means more resources isn’t the answer – more automation and intelligence is. Carriers, large enterprises, and managed service providers alike need observability to function automatically, and Pliant’s solution, together with modern monitoring platforms, ensures these expectations are met.”

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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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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.

Pliant Launches Observability Automation Solution

Pliant announced the launch of an Observability Automation solution developed specifically for leading performance monitoring vendors and their customers.

The Pliant Observability Solution elevates performance monitoring for operations teams at large enterprises, carriers, and managed service provider organizations. This new offering revolutionizes how teams automate, integrate, and connect their environments. Rather than task talented and costly developers to manage performance monitoring manually, Pliant customers can build workflows quickly, efficiently, and repeatably. Featuring dozens of pre-built workflows that address common actions, the Pliant Observability solution accelerates device and data onboarding, integrates actions and outcomes, and connects the IT stack to drive rapid and repeatable observability.

“When we developed Pliant, we knew one of the most impactful use cases was going to be automating, enriching, and enhancing performance monitoring,” said Pliant co-founder and CEO Vess Bakalov. “My time as founder and CTO at SevOne demonstrated the importance of visibility and intelligence, but making them automatically actionable was a perennial challenge for customers, and that remains true today. This solution combines two powerful capabilities – modern monitoring and API-driven workflow automation – to elevate both to new heights for our customers.”

With Pliant Connect, performance monitoring customers can leverage the platform to rapidly and automatically onboard the data that powers visibility and insight, accelerating time-to-value at a fraction of the cost currently spent on services, development, and engineering resources.

With Pliant Actions, performance monitoring customers can automate the repetitive, traditionally manual alert-driven actions they receive, increasing productivity and root cause resolution. Pliant’s solution, purpose-built for leading NPM vendors, seamlessly delivers on the promise of observability unlike anything available today.

The Pliant Observability Solution offers:

- Automated device and data onboarding to increase time to value

- Automated remediation to reduce manual actions and increase productivity

- Connections for the tech stack that drive rapid and repeatable observability and root cause identification

“The complexity incurred with Infrastructure modernization has increased the demands placed on operations and engineering teams, who are being forced to do more with less,” added Chris Rohter, Vice President of Marketing at Pliant. “The dynamic nature of today’s infrastructure means more resources isn’t the answer – more automation and intelligence is. Carriers, large enterprises, and managed service providers alike need observability to function automatically, and Pliant’s solution, together with modern monitoring platforms, ensures these expectations are met.”

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.