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ExtraHop for AWS Released

ExtraHop announced the availability of ExtraHop for Amazon Web Services (AWS).

The ExtraHop for AWS solution extends ExtraHop’s wire data analytics platform to span on-premises and cloud environments in a single management pane, enabling IT organizations to accelerate public cloud adoption by identifying applications for migration and then optimizing their performance, availability, and security.

ExtraHop for AWS delivers visibility and diagnostics across all AWS services, including EC2, RDS, S3, ELB, Elasticache, DNS, and others.

With wire data analytics that extend beyond simple resource utilization monitoring, ExtraHop enables IT teams to predictably monitor workload performance; understand differences between AWS regions and zones; accurately track efficiency and performance-based SLAs; and ensure security with pervasive, contextual monitoring.

ExtraHop for AWS also enables IT teams to better identify applications for migration and architect them for optimal performance post-migration.

The ExtraHop for AWS solution offers a host of key capabilities:

- Strategically Architected AWS Deployments: ExtraHop for AWS gives IT teams visibility into transaction volume, variability, latency, errors, size, and other metrics for AWS regions and zones. These insights into workload characteristics enable teams to optimize AWS deployments and better manage costs based on true workload requirements.

- Optimization of AWS Database Performance: ExtraHop for AWS shows query volume, methods, errors, SQL statements, and related details, providing visibility into transaction-level database metrics and enabling IT teams to identify the best database applications to migrate to AWS. Once in AWS, ExtraHop provides granular, transaction-level metrics in RDS to identify, diagnose, and optimize query performance.

- Visibility into Elastic Load Balancing (ELB) and Auto-Scaling: ExtraHop for AWS shows real-time ELB and auto-scaling events and correlates them with the end-user experience. With ExtraHop for AWS, IT organizations can intelligently set ELB and auto-scaling policies and size instances properly, automatically scale up new EC2 instances based on transaction metrics, and detect and respond to DDoS attacks immediately to minimize impact on end users.

- Integration with AWS CloudWatch: ExtraHop for AWS supplements CloudWatch’s visibility into AWS resource utilization, automatically feeding CloudWatch standard transactional metrics such as RDS or EC2 per client transaction response times. It also ensures more-informed capacity planning and identifies which zones and regions best serve users based on the performance of the applications, end-user experience, and the supporting AWS services. Finally, it can easily be extended to instrument nearly any transactional element, and those custom metrics can also be integrated with AWS CloudWatch.

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 ...

ExtraHop for AWS Released

ExtraHop announced the availability of ExtraHop for Amazon Web Services (AWS).

The ExtraHop for AWS solution extends ExtraHop’s wire data analytics platform to span on-premises and cloud environments in a single management pane, enabling IT organizations to accelerate public cloud adoption by identifying applications for migration and then optimizing their performance, availability, and security.

ExtraHop for AWS delivers visibility and diagnostics across all AWS services, including EC2, RDS, S3, ELB, Elasticache, DNS, and others.

With wire data analytics that extend beyond simple resource utilization monitoring, ExtraHop enables IT teams to predictably monitor workload performance; understand differences between AWS regions and zones; accurately track efficiency and performance-based SLAs; and ensure security with pervasive, contextual monitoring.

ExtraHop for AWS also enables IT teams to better identify applications for migration and architect them for optimal performance post-migration.

The ExtraHop for AWS solution offers a host of key capabilities:

- Strategically Architected AWS Deployments: ExtraHop for AWS gives IT teams visibility into transaction volume, variability, latency, errors, size, and other metrics for AWS regions and zones. These insights into workload characteristics enable teams to optimize AWS deployments and better manage costs based on true workload requirements.

- Optimization of AWS Database Performance: ExtraHop for AWS shows query volume, methods, errors, SQL statements, and related details, providing visibility into transaction-level database metrics and enabling IT teams to identify the best database applications to migrate to AWS. Once in AWS, ExtraHop provides granular, transaction-level metrics in RDS to identify, diagnose, and optimize query performance.

- Visibility into Elastic Load Balancing (ELB) and Auto-Scaling: ExtraHop for AWS shows real-time ELB and auto-scaling events and correlates them with the end-user experience. With ExtraHop for AWS, IT organizations can intelligently set ELB and auto-scaling policies and size instances properly, automatically scale up new EC2 instances based on transaction metrics, and detect and respond to DDoS attacks immediately to minimize impact on end users.

- Integration with AWS CloudWatch: ExtraHop for AWS supplements CloudWatch’s visibility into AWS resource utilization, automatically feeding CloudWatch standard transactional metrics such as RDS or EC2 per client transaction response times. It also ensures more-informed capacity planning and identifies which zones and regions best serve users based on the performance of the applications, end-user experience, and the supporting AWS services. Finally, it can easily be extended to instrument nearly any transactional element, and those custom metrics can also be integrated with AWS CloudWatch.

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 ...