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SIOS Technology Announces New Version of SIOS iQ

SIOS Technology announced the newest version of its SIOS iQ IT analytics platform, which harnesses the power of machine learning and deep learning analytics to optimize the performance and efficiency of VMware environments.

A new flexible, API-driven integration architecture enables SIOS iQ to integrate data from a range of sources, including application monitoring tools and data aggregation fabrics such as Splunk, Hadoop and Elasticsearch.

Providing a more comprehensive view of the IT infrastructure, SIOS iQ empowers IT to automatically and instantaneously identify and correct the root causes of application performance issues and to predict future application performance with unparalleled precision and ease-of-use.

SIOS iQ replaces the alert storms, inaccuracies, and manual effort associated with legacy tools with simple, precise, and clear recommendations for problem-solving. By applying patented machine learning/deep learning analytics to a broad range of data from across IT silos, SIOS iQ learns the patterns of behavior observed across interrelated components over time. It correlates this anomalous behavior to application performance issues and changes in the infrastructure. SIOS iQ not only detects problems earlier with greater precision and clarity than traditional tools, but also identifies the root cause of issues, reveals unknown problems, and predicts future performance issues. IT can also use SIOS iQ to look at “what if” scenarios to understand the potential impact that a planned change will have on application performance before the change is actually implemented.

“The exponential growth of modern IT infrastructures in both scale and complexity is pushing IT teams to their limits,” said Jerry Melnick, President and CEO of SIOS Technology Corp. “SIOS iQ frees IT from the daily grind of reactive problem handling to proactively operate and innovate in order to add value to their core business operations. Deep learning technology in SIOS iQ analyzes tens of thousands of real-time metrics to accurately identify the root causes of performance issues and recommend specific steps to resolve them. Advanced predictive analytics in SIOS iQ forecasts future performance challenges so IT can avoid or prevent them before they occur.”

SIOS iQ can be operated as a standalone tool to find and forecast infrastructure issues and their root cause or as a foundational platform of an enterprise analytics architecture that integrates with a wide variety of application performance monitoring tools.

In addition to the flexible machine learning architecture, this update of SIOS iQ includes the following features:

- Software Developer Kit (SDK) for Broad Integration to include data from a wider range of sources including application and network monitoring tools and data aggregation fabrics such as Splunk. This SDK enables IT to query Splunk data more easily and to apply SIOS iQ machine learning-based analysis for more precise and comprehensive insights into application performance issues.
]- Meta-analysis Provides Industry’s Most Accurate Root Cause Identification. New deep learning technique identifies patterns of incidents related to application quality of service (QoS) reducing these to a small number of recurring infrastructure behaviors underlying the problem, revealing the root cause and providing precise recommendations for fixing them using a powerful new visualization technique.

- VM Packing and Placement: Recommends placement of workloads on VMware hosts to optimize VM density without the recurring thrashing and constant rebalancing caused by traditional tools.

- Enhanced Recommendations: Provides specific steps IT admins can take to solve complex performance issues and optimize efficiency.

- ROI Savings Analysis: Identifies wasted resources including rogue VMDKs, idle and oversized VMs, snapshot waste, unnecessary software licenses and wasted labor costs.

- Service Analytics: Allows IT admins to logically group and prioritize resources according to business importance. It correlates application service alerts with abnormal infrastructure behavior for fast, precise problem solving.

SIOS iQ is available immediately.

The Latest

Two years ago, almost every customer conversation about AI started with the same questions: Which model should we use? What can it do? Is it ready for the enterprise? Today, those discussions have moved on. CIOs are far more interested in how to govern AI, integrate it with existing systems, prepare their workforce and make it part of everyday operations. The challenge is no longer to prove that AI can deliver value. It's instead about how to embed AI into the business in a way that's secure, scalable and delivers measurable outcomes ...

 

Two things happened to production incidents between 2023 and now, and they did not happen at the same speed. The first is that a class of dependency that barely existed three years ago now accounts for one incident in ten. Incidents disclosed by AI model and AI application providers rose from 1.7% of all disclosed unplanned incidents in 2023 to 10.7% in 2026 year to date, roughly a sixfold rise; that counts only incidents at AI companies themselves, so the true share is higher. The second is that the time to close an incident has not come down ...

When an AI assistant gives an incomplete or incorrect answer, teams often blame the model. They adjust prompts, switch models, increase context windows or test a new retrieval strategy. However the model may not be a problem. In many enterprise AI workflows, the problem begins inside the document-ingestion pipeline ...

If you talk to any security or observability teams right now, they're all fighting the same fire: their tooling was built to ingest X, but their sources are pumping Y and soon to be doing Z. The knee-jerk reaction is always the same: we need more platform. However, this reaction is wrong. Let me explain why, because the solution to this problem is foundational, not financial. Instead of hurling yet more money at the problem, make sure you've done what's needed upstream ...

Rapid AI adoption and the unique ways AI workloads operate is redefining the scope and structure of what these teams must deliver. This shift is forcing organizations to rethink how they manage scale, automation, and control, according to The State of SRE and Platform Engineering 2026, a new report from Dynatrace ...

AI is usually talked about as a software tool, but it also depends heavily on the network behind it. Whether a company is using AI for chatbots, automation, monitoring, analytics, or employee support, all of that information has to move across the network in a reliable and secure way. That means AI is not just an application decision. It is also an infrastructure decision. Before organizations rush into AI, they should ask a simple question: Is our network ready to support it? ...

Enterprise AI often lacks governed access to where business processes actually execute. Without that access, AI agents may be able to reason, but they cannot operate reliably across enterprise workflows. For AI agents to effectively carry out workflows, they will require integration-layer context and controls. Organizations can implement these prerequisites by providing AI with managed access to the middleware layer ...

Enterprise networks rarely behave the same way for very long. A routing adjustment in one region may unexpectedly alter application performance in another. A cloud migration may introduce hidden dependencies that go unnoticed until an outage occurs. All the while, the network is managed by several different teams, each of whom use different tool sets — and as a result, have different views of the network ... There’s usually an engineer who remembers why traffic fails over a certain way between sites, or which transparent firewall was added where. The problem is that human memory cannot scale alongside enterprise-scale networks ...

Ask an infrastructure team how confident they are in their ability to govern AI, and most will tell you they've got it handled. A recent survey of 406 IT decision-makers and platform engineering leaders found 86% expressing exactly that confidence. Ask the same group whether they have a formal written AI governance policy, and the number drops to 30%, according to Spacelift's Infrastructure Automation Report ...

In MEAN TIME TO INSIGHT Episode 27, Shamus McGillicuddy, EMA VP of Research, Network Infrastructure and Operations, and Parker Hathcock, EMA Research Director covering IT Service/Operations (ServiceOps), discuss observability unification in modern IT operations ... 

SIOS Technology Announces New Version of SIOS iQ

SIOS Technology announced the newest version of its SIOS iQ IT analytics platform, which harnesses the power of machine learning and deep learning analytics to optimize the performance and efficiency of VMware environments.

A new flexible, API-driven integration architecture enables SIOS iQ to integrate data from a range of sources, including application monitoring tools and data aggregation fabrics such as Splunk, Hadoop and Elasticsearch.

Providing a more comprehensive view of the IT infrastructure, SIOS iQ empowers IT to automatically and instantaneously identify and correct the root causes of application performance issues and to predict future application performance with unparalleled precision and ease-of-use.

SIOS iQ replaces the alert storms, inaccuracies, and manual effort associated with legacy tools with simple, precise, and clear recommendations for problem-solving. By applying patented machine learning/deep learning analytics to a broad range of data from across IT silos, SIOS iQ learns the patterns of behavior observed across interrelated components over time. It correlates this anomalous behavior to application performance issues and changes in the infrastructure. SIOS iQ not only detects problems earlier with greater precision and clarity than traditional tools, but also identifies the root cause of issues, reveals unknown problems, and predicts future performance issues. IT can also use SIOS iQ to look at “what if” scenarios to understand the potential impact that a planned change will have on application performance before the change is actually implemented.

“The exponential growth of modern IT infrastructures in both scale and complexity is pushing IT teams to their limits,” said Jerry Melnick, President and CEO of SIOS Technology Corp. “SIOS iQ frees IT from the daily grind of reactive problem handling to proactively operate and innovate in order to add value to their core business operations. Deep learning technology in SIOS iQ analyzes tens of thousands of real-time metrics to accurately identify the root causes of performance issues and recommend specific steps to resolve them. Advanced predictive analytics in SIOS iQ forecasts future performance challenges so IT can avoid or prevent them before they occur.”

SIOS iQ can be operated as a standalone tool to find and forecast infrastructure issues and their root cause or as a foundational platform of an enterprise analytics architecture that integrates with a wide variety of application performance monitoring tools.

In addition to the flexible machine learning architecture, this update of SIOS iQ includes the following features:

- Software Developer Kit (SDK) for Broad Integration to include data from a wider range of sources including application and network monitoring tools and data aggregation fabrics such as Splunk. This SDK enables IT to query Splunk data more easily and to apply SIOS iQ machine learning-based analysis for more precise and comprehensive insights into application performance issues.
]- Meta-analysis Provides Industry’s Most Accurate Root Cause Identification. New deep learning technique identifies patterns of incidents related to application quality of service (QoS) reducing these to a small number of recurring infrastructure behaviors underlying the problem, revealing the root cause and providing precise recommendations for fixing them using a powerful new visualization technique.

- VM Packing and Placement: Recommends placement of workloads on VMware hosts to optimize VM density without the recurring thrashing and constant rebalancing caused by traditional tools.

- Enhanced Recommendations: Provides specific steps IT admins can take to solve complex performance issues and optimize efficiency.

- ROI Savings Analysis: Identifies wasted resources including rogue VMDKs, idle and oversized VMs, snapshot waste, unnecessary software licenses and wasted labor costs.

- Service Analytics: Allows IT admins to logically group and prioritize resources according to business importance. It correlates application service alerts with abnormal infrastructure behavior for fast, precise problem solving.

SIOS iQ is available immediately.

The Latest

Two years ago, almost every customer conversation about AI started with the same questions: Which model should we use? What can it do? Is it ready for the enterprise? Today, those discussions have moved on. CIOs are far more interested in how to govern AI, integrate it with existing systems, prepare their workforce and make it part of everyday operations. The challenge is no longer to prove that AI can deliver value. It's instead about how to embed AI into the business in a way that's secure, scalable and delivers measurable outcomes ...

 

Two things happened to production incidents between 2023 and now, and they did not happen at the same speed. The first is that a class of dependency that barely existed three years ago now accounts for one incident in ten. Incidents disclosed by AI model and AI application providers rose from 1.7% of all disclosed unplanned incidents in 2023 to 10.7% in 2026 year to date, roughly a sixfold rise; that counts only incidents at AI companies themselves, so the true share is higher. The second is that the time to close an incident has not come down ...

When an AI assistant gives an incomplete or incorrect answer, teams often blame the model. They adjust prompts, switch models, increase context windows or test a new retrieval strategy. However the model may not be a problem. In many enterprise AI workflows, the problem begins inside the document-ingestion pipeline ...

If you talk to any security or observability teams right now, they're all fighting the same fire: their tooling was built to ingest X, but their sources are pumping Y and soon to be doing Z. The knee-jerk reaction is always the same: we need more platform. However, this reaction is wrong. Let me explain why, because the solution to this problem is foundational, not financial. Instead of hurling yet more money at the problem, make sure you've done what's needed upstream ...

Rapid AI adoption and the unique ways AI workloads operate is redefining the scope and structure of what these teams must deliver. This shift is forcing organizations to rethink how they manage scale, automation, and control, according to The State of SRE and Platform Engineering 2026, a new report from Dynatrace ...

AI is usually talked about as a software tool, but it also depends heavily on the network behind it. Whether a company is using AI for chatbots, automation, monitoring, analytics, or employee support, all of that information has to move across the network in a reliable and secure way. That means AI is not just an application decision. It is also an infrastructure decision. Before organizations rush into AI, they should ask a simple question: Is our network ready to support it? ...

Enterprise AI often lacks governed access to where business processes actually execute. Without that access, AI agents may be able to reason, but they cannot operate reliably across enterprise workflows. For AI agents to effectively carry out workflows, they will require integration-layer context and controls. Organizations can implement these prerequisites by providing AI with managed access to the middleware layer ...

Enterprise networks rarely behave the same way for very long. A routing adjustment in one region may unexpectedly alter application performance in another. A cloud migration may introduce hidden dependencies that go unnoticed until an outage occurs. All the while, the network is managed by several different teams, each of whom use different tool sets — and as a result, have different views of the network ... There’s usually an engineer who remembers why traffic fails over a certain way between sites, or which transparent firewall was added where. The problem is that human memory cannot scale alongside enterprise-scale networks ...

Ask an infrastructure team how confident they are in their ability to govern AI, and most will tell you they've got it handled. A recent survey of 406 IT decision-makers and platform engineering leaders found 86% expressing exactly that confidence. Ask the same group whether they have a formal written AI governance policy, and the number drops to 30%, according to Spacelift's Infrastructure Automation Report ...

In MEAN TIME TO INSIGHT Episode 27, Shamus McGillicuddy, EMA VP of Research, Network Infrastructure and Operations, and Parker Hathcock, EMA Research Director covering IT Service/Operations (ServiceOps), discuss observability unification in modern IT operations ...