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Boundary Introduces New APM Capabilities for Monitoring Public Cloud

Boundary has released new application monitoring capabilities for companies running on Amazon Web Services (AWS) and other public and private cloud infrastructure.

These new capabilities enable companies to get early warnings of pending application infrastructure issues that, left unchecked, would affect customer experience.

The enhanced solution will be on display this week at AWS re: Invent, Amazon’s global conference for AWS customers and partners.

Boundary’s updated service includes a proactive alerting capability that understands normal application behavior and, using advanced analytics, warns users at the earliest sign of potential problems.

Boundary has also added a Big Data store that will enable customers to stash detailed performance data for long periods, as well as a reporting component that will automatically compare historical and current performance metrics, and email the summaries to customers.

“Applications hosted in the public cloud – even more than traditional infrastructures – require constant and vigilant monitoring,” said Gary Read, CEO at Boundary. ”But because the public cloud is dynamic in nature and does not expose critical items such as topology, traditional solutions are typically out of date and too late in reporting problems.”

The new version of Boundary addresses this challenge by collecting previously unexposed data every single second, understanding the dynamic application topology, learning the normal behavior of applications on a minute-by-minute basis, and providing real-time, analytics-driven warnings on performance abnormalities.

Using the reporting capability and long-term data store, customers can examine all the metrics for prior periods to help in problem diagnosis. This way, users can resolve potential issues before customers are impacted.

“This is really important for EC2 customers, because when applications are running on a shared infrastructure, companies need to understand the impact of other users on the response time and the network,” said Read. “Early knowledge of network congestion or poor performance can help IT managers make quick decisions to move applications to other instances or availability zones on Amazon, or to a secondary cloud provider.”

“Boundary recently detected the AWS outage over two full hours before Amazon announced it and a customer of ours detected the Azure outage 15 hours before it was announced by Microsoft,” Read added. “Now we’re putting even more advanced analytic and reporting capabilities in the hands of our customers. Before traditional monitoring tools have even processed their next set of samples, Boundary has identified abnormalities in cloud infrastructure and alerted users to potential problems.”

Click here to read Gary Read's blog: Another Amazon Outage - Can Companies Prevent Damage from Cloud Outages?

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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 ...

Boundary Introduces New APM Capabilities for Monitoring Public Cloud

Boundary has released new application monitoring capabilities for companies running on Amazon Web Services (AWS) and other public and private cloud infrastructure.

These new capabilities enable companies to get early warnings of pending application infrastructure issues that, left unchecked, would affect customer experience.

The enhanced solution will be on display this week at AWS re: Invent, Amazon’s global conference for AWS customers and partners.

Boundary’s updated service includes a proactive alerting capability that understands normal application behavior and, using advanced analytics, warns users at the earliest sign of potential problems.

Boundary has also added a Big Data store that will enable customers to stash detailed performance data for long periods, as well as a reporting component that will automatically compare historical and current performance metrics, and email the summaries to customers.

“Applications hosted in the public cloud – even more than traditional infrastructures – require constant and vigilant monitoring,” said Gary Read, CEO at Boundary. ”But because the public cloud is dynamic in nature and does not expose critical items such as topology, traditional solutions are typically out of date and too late in reporting problems.”

The new version of Boundary addresses this challenge by collecting previously unexposed data every single second, understanding the dynamic application topology, learning the normal behavior of applications on a minute-by-minute basis, and providing real-time, analytics-driven warnings on performance abnormalities.

Using the reporting capability and long-term data store, customers can examine all the metrics for prior periods to help in problem diagnosis. This way, users can resolve potential issues before customers are impacted.

“This is really important for EC2 customers, because when applications are running on a shared infrastructure, companies need to understand the impact of other users on the response time and the network,” said Read. “Early knowledge of network congestion or poor performance can help IT managers make quick decisions to move applications to other instances or availability zones on Amazon, or to a secondary cloud provider.”

“Boundary recently detected the AWS outage over two full hours before Amazon announced it and a customer of ours detected the Azure outage 15 hours before it was announced by Microsoft,” Read added. “Now we’re putting even more advanced analytic and reporting capabilities in the hands of our customers. Before traditional monitoring tools have even processed their next set of samples, Boundary has identified abnormalities in cloud infrastructure and alerted users to potential problems.”

Click here to read Gary Read's blog: Another Amazon Outage - Can Companies Prevent Damage from Cloud Outages?

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 ...