APM Data Gathering in Cloud Solutions

Keith Bromley
Learn more about Keysight Technologies

Application performance monitoring (APM) is important regardless of what platform you run your applications on. However, cloud environments can be particularly difficult for two reasons. First, there is an attitude that everything is taken care of for you. While some functions are taken care of for you, other functions will be "add-ons" that you need to purchase and append to your cloud instance.

Still other functions, like the collection of packet data for deep packet inspection (DPI), are not even available as part of the offering from your cloud vendor. You need to buy and install those types of capabilities separately, if you want them.

And you should want packet data. According to a Dimensional Data study, 80% of the study participants did not have the data they need to monitor public cloud environments accurately. Nearly half said that their lack of cloud visibility has led to application performance issues. Half also indicated that the tools provided by public cloud vendors were inadequate to support monitoring.

This leads to the second issue — you need the right tools to collect and analyze your cloud data. Lack of proper monitoring will typically result in compliance issues and potential security issues.

Both of these issues can be remedied with the collection of proper the data. That data can then be sent on to a data lake for storage and then analysis by DPI tools or artificial intelligence.

So, how do you collect this data?

The answer is that you will need to install some sort of packet data collection solution yourself. The trick is to make sure it copies the full packet data. Unnecessary headers or payloads can be deleted later on, but you want to capture all of it up front so that you have options. Your collection tool also needs the ability to have settings so that you capture only the specific data that you need. Otherwise, if you try to copy everything, or almost everything, you will have to pay for a lot of data storage — which will become expensive.

To be clear, we are talking about capturing the complete data packet. Summarized data, log data, etc. have their place but real problems can be missed when you only look at snippets of data. Don’t cheat yourself and find yourself in a bind down the road, invest in a good packet data capture solution upfront.

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APM Data Gathering in Cloud Solutions

Keith Bromley
Learn more about Keysight Technologies

Application performance monitoring (APM) is important regardless of what platform you run your applications on. However, cloud environments can be particularly difficult for two reasons. First, there is an attitude that everything is taken care of for you. While some functions are taken care of for you, other functions will be "add-ons" that you need to purchase and append to your cloud instance.

Still other functions, like the collection of packet data for deep packet inspection (DPI), are not even available as part of the offering from your cloud vendor. You need to buy and install those types of capabilities separately, if you want them.

And you should want packet data. According to a Dimensional Data study, 80% of the study participants did not have the data they need to monitor public cloud environments accurately. Nearly half said that their lack of cloud visibility has led to application performance issues. Half also indicated that the tools provided by public cloud vendors were inadequate to support monitoring.

This leads to the second issue — you need the right tools to collect and analyze your cloud data. Lack of proper monitoring will typically result in compliance issues and potential security issues.

Both of these issues can be remedied with the collection of proper the data. That data can then be sent on to a data lake for storage and then analysis by DPI tools or artificial intelligence.

So, how do you collect this data?

The answer is that you will need to install some sort of packet data collection solution yourself. The trick is to make sure it copies the full packet data. Unnecessary headers or payloads can be deleted later on, but you want to capture all of it up front so that you have options. Your collection tool also needs the ability to have settings so that you capture only the specific data that you need. Otherwise, if you try to copy everything, or almost everything, you will have to pay for a lot of data storage — which will become expensive.

To be clear, we are talking about capturing the complete data packet. Summarized data, log data, etc. have their place but real problems can be missed when you only look at snippets of data. Don’t cheat yourself and find yourself in a bind down the road, invest in a good packet data capture solution upfront.

Related Links

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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 ...

Cold Data, Hot Problem: Why AI Is Rewriting Enterprise Storage Strategy

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 ...

Observability at a Crossroads: AI, Economics, Complexity and the Enduring Power of Open Source

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 ...

Explainability Is the New Battleground in AI-Powered Observability

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 ...

Empowering the Human Side of the Autonomous IT Age

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 Scale Is Outpacing Infrastructure - and IT Leaders Are Running Out of Time

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.

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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 ...

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