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Honeycomb Unveils Kubernetes-Aware Observability to Solve Application Performance Mysteries

Innovative application observability for developers correlates code performance with granular cluster data for easier debugging and migrations

Honeycomb released Honeycomb for Kubernetes. This new capability enables platform engineers running Kubernetes and the developers building on it to correlate granular application issues in production code with their infrastructure layer.

Honeycomb for Kubernetes is available now to all Honeycomb users.

According to CNCF's 2022 Annual Survey, 64 percent of end users (engineers) have implemented Kubernetes in production today, and 25 percent are currently evaluating the solution. Kubernetes is furthering its momentum as the preferred environment for managing containerized applications, as it's easier to get feature changes provisioned and enables developers to build and release faster. However, while infrastructure dashboards within application performance monitoring (APM) suites are effective for platform engineers monitoring Kubernetes, they lack detailed application context relevant to developers diagnosing and resolving software issues. This correlation gap between Kubernetes application and infrastructure layers leads to increased operational backlog, strained resources, and decreased productivity.

"It's becoming clear that today's monitoring tools frequently underserve developers, and worse, they create unnecessary tension between devs and platform engineers," said Charity Majors, Co-founder and CTO of Honeycomb. "This blind spot serves as yet another glaring signal that observability platforms are better equipped than APM tools to provide comprehensive, granular context into how code behavior impacts application performance."

Honeycomb's new Kubernetes-aware observability features fill the visibility gaps in these complex systems, helping teams rule out application vs. infrastructure issues and address the potential bottlenecks between platform engineers and application developers. This equips teams with the confidence to release more often, keep migrations seamless, foster developer self-sufficiency, and elevate overall productivity.

For the team at Birdie Care, Sr. DevOps Engineer Harry Morgan says that Honeycomb's "out-of-the-box templates for metric analysis and correlation really help put together a more holistic picture of our environments, simplifying our workflow and eliminating the need to compare across observability platforms. At Birdie, it's important we can ask critical questions of our whole system, and with the improvements to their Kubernetes offering, Honeycomb is delivering a great addition to our toolkit in doing this."

Designed to enable seamless insight into applications in relation to the infrastructure they run on, Honeycomb for Kubernetes allows developers to correlate application requests with specific Kubernetes pods, nodes, or cluster configurations. It makes it easy to integrate data from Kubernetes using new instrumentation options for Kubernetes events, metrics, and trace attributes. These include OpenTelemetry as well as a low-code, language-agnostic agent for comprehensive coverage. In the Honeycomb UI, new correlation features make it easy to tie this Kubernetes context to any incident's events and reveal patterns. Honeycomb's approach to observability offers numerous advantages:

- Observability Efficiency: As teams adopt the complexity inherent in Kubernetes, Honeycomb helps observe their systems efficiently. Designed to scale painlessly with exponential telemetry from pods to nodes, Honeycomb includes unlimited custom attribute tags per event at no extra cost. This solves the budget/readiness tradeoff forced by per-host/metric billing.

- Human-Driven, AI-assisted Investigation: Because dashboards are limited to passively visualizing known cluster data, Honeycomb also offers developers a query-driven approach to surface unknown patterns, pinpointing what's wrong and how Kubernetes is involved. To help users navigate unfamiliar services, Honeycomb's Query Assistant uses generative AI to process natural English questions into relevant queries and produce immediate visual feedback on application performance.

- Kubernetes Context in OpenTelemetry: Honeycomb supports OpenTelemetry Kubernetes standards and simplifies instrumenting cluster context into application traces, enhancing the experience for teams adopting open-source instrumentation. By implementing Honeycomb and OpenTelemetry, platform teams also see less data and tool fragmentation. Various options are available to enable users on any language, node, or cluster.

As modern developer teams migrate towards distributed architecture, Honeycomb addresses that complexity with scalable and user-driven observability. The company's new solution for Kubernetes is the most recent example paving the way for a more open, efficient, and productive development ecosystem.

Honeycomb for Kubernetes works on platforms like Amazon EKS, Azure Kubernetes Service, or Google Kubernetes Engine as well as bare-metal Kubernetes distributions and Red Hat OpenShift.

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Honeycomb Unveils Kubernetes-Aware Observability to Solve Application Performance Mysteries

Innovative application observability for developers correlates code performance with granular cluster data for easier debugging and migrations

Honeycomb released Honeycomb for Kubernetes. This new capability enables platform engineers running Kubernetes and the developers building on it to correlate granular application issues in production code with their infrastructure layer.

Honeycomb for Kubernetes is available now to all Honeycomb users.

According to CNCF's 2022 Annual Survey, 64 percent of end users (engineers) have implemented Kubernetes in production today, and 25 percent are currently evaluating the solution. Kubernetes is furthering its momentum as the preferred environment for managing containerized applications, as it's easier to get feature changes provisioned and enables developers to build and release faster. However, while infrastructure dashboards within application performance monitoring (APM) suites are effective for platform engineers monitoring Kubernetes, they lack detailed application context relevant to developers diagnosing and resolving software issues. This correlation gap between Kubernetes application and infrastructure layers leads to increased operational backlog, strained resources, and decreased productivity.

"It's becoming clear that today's monitoring tools frequently underserve developers, and worse, they create unnecessary tension between devs and platform engineers," said Charity Majors, Co-founder and CTO of Honeycomb. "This blind spot serves as yet another glaring signal that observability platforms are better equipped than APM tools to provide comprehensive, granular context into how code behavior impacts application performance."

Honeycomb's new Kubernetes-aware observability features fill the visibility gaps in these complex systems, helping teams rule out application vs. infrastructure issues and address the potential bottlenecks between platform engineers and application developers. This equips teams with the confidence to release more often, keep migrations seamless, foster developer self-sufficiency, and elevate overall productivity.

For the team at Birdie Care, Sr. DevOps Engineer Harry Morgan says that Honeycomb's "out-of-the-box templates for metric analysis and correlation really help put together a more holistic picture of our environments, simplifying our workflow and eliminating the need to compare across observability platforms. At Birdie, it's important we can ask critical questions of our whole system, and with the improvements to their Kubernetes offering, Honeycomb is delivering a great addition to our toolkit in doing this."

Designed to enable seamless insight into applications in relation to the infrastructure they run on, Honeycomb for Kubernetes allows developers to correlate application requests with specific Kubernetes pods, nodes, or cluster configurations. It makes it easy to integrate data from Kubernetes using new instrumentation options for Kubernetes events, metrics, and trace attributes. These include OpenTelemetry as well as a low-code, language-agnostic agent for comprehensive coverage. In the Honeycomb UI, new correlation features make it easy to tie this Kubernetes context to any incident's events and reveal patterns. Honeycomb's approach to observability offers numerous advantages:

- Observability Efficiency: As teams adopt the complexity inherent in Kubernetes, Honeycomb helps observe their systems efficiently. Designed to scale painlessly with exponential telemetry from pods to nodes, Honeycomb includes unlimited custom attribute tags per event at no extra cost. This solves the budget/readiness tradeoff forced by per-host/metric billing.

- Human-Driven, AI-assisted Investigation: Because dashboards are limited to passively visualizing known cluster data, Honeycomb also offers developers a query-driven approach to surface unknown patterns, pinpointing what's wrong and how Kubernetes is involved. To help users navigate unfamiliar services, Honeycomb's Query Assistant uses generative AI to process natural English questions into relevant queries and produce immediate visual feedback on application performance.

- Kubernetes Context in OpenTelemetry: Honeycomb supports OpenTelemetry Kubernetes standards and simplifies instrumenting cluster context into application traces, enhancing the experience for teams adopting open-source instrumentation. By implementing Honeycomb and OpenTelemetry, platform teams also see less data and tool fragmentation. Various options are available to enable users on any language, node, or cluster.

As modern developer teams migrate towards distributed architecture, Honeycomb addresses that complexity with scalable and user-driven observability. The company's new solution for Kubernetes is the most recent example paving the way for a more open, efficient, and productive development ecosystem.

Honeycomb for Kubernetes works on platforms like Amazon EKS, Azure Kubernetes Service, or Google Kubernetes Engine as well as bare-metal Kubernetes distributions and Red Hat OpenShift.

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According to Auvik's 2025 IT Trends Report, 60% of IT professionals feel at least moderately burned out on the job, with 43% stating that their workload is contributing to work stress. At the same time, many IT professionals are naming AI and machine learning as key areas they'd most like to upskill ...

Businesses that face downtime or outages risk financial and reputational damage, as well as reducing partner, shareholder, and customer trust. One of the major challenges that enterprises face is implementing a robust business continuity plan. What's the solution? The answer may lie in disaster recovery tactics such as truly immutable storage and regular disaster recovery testing ...

IT spending is expected to jump nearly 10% in 2025, and organizations are now facing pressure to manage costs without slowing down critical functions like observability. To meet the challenge, leaders are turning to smarter, more cost effective business strategies. Enter stage right: OpenTelemetry, the missing piece of the puzzle that is no longer just an option but rather a strategic advantage ...

Amidst the threat of cyberhacks and data breaches, companies install several security measures to keep their business safely afloat. These measures aim to protect businesses, employees, and crucial data. Yet, employees perceive them as burdensome. Frustrated with complex logins, slow access, and constant security checks, workers decide to completely bypass all security set-ups ...

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Cloudbrink's Personal SASE services provide last-mile acceleration and reduction in latency

In MEAN TIME TO INSIGHT Episode 13, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses hybrid multi-cloud networking strategy ... 

In high-traffic environments, the sheer volume and unpredictable nature of network incidents can quickly overwhelm even the most skilled teams, hindering their ability to react swiftly and effectively, potentially impacting service availability and overall business performance. This is where closed-loop remediation comes into the picture: an IT management concept designed to address the escalating complexity of modern networks ...

In 2025, enterprise workflows are undergoing a seismic shift. Propelled by breakthroughs in generative AI (GenAI), large language models (LLMs), and natural language processing (NLP), a new paradigm is emerging — agentic AI. This technology is not just automating tasks; it's reimagining how organizations make decisions, engage customers, and operate at scale ...

In the early days of the cloud revolution, business leaders perceived cloud services as a means of sidelining IT organizations. IT was too slow, too expensive, or incapable of supporting new technologies. With a team of developers, line of business managers could deploy new applications and services in the cloud. IT has been fighting to retake control ever since. Today, IT is back in the driver's seat, according to new research by Enterprise Management Associates (EMA) ...

In today's fast-paced and increasingly complex network environments, Network Operations Centers (NOCs) are the backbone of ensuring continuous uptime, smooth service delivery, and rapid issue resolution. However, the challenges faced by NOC teams are only growing. In a recent study, 78% state network complexity has grown significantly over the last few years while 84% regularly learn about network issues from users. It is imperative we adopt a new approach to managing today's network experiences ...

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From growing reliance on FinOps teams to the increasing attention on artificial intelligence (AI), and software licensing, the Flexera 2025 State of the Cloud Report digs into how organizations are improving cloud spend efficiency, while tackling the complexities of emerging technologies ...