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Honeycomb Metrics Released

Honeycomb announced that the Honeycomb Metrics feature is generally available for its enterprise customers.

Organizations using Honeycomb for observability now have a new metrics capability to quickly identify and resolve system issues.

Used together—Honeycomb’s observability platform for exploring application data and Honeycomb Metrics for monitoring system data—developers now have complete visibility into how their code is behaving and performing, down to the individual request level, and down to the health of the underlying systems running their code. The sum of application behavior and system behavior determines the user’s experience, which can now be explored in its entirety, without having to switch between different tools, resulting in faster debugging workflows.

Honeycomb now gives engineering teams the best of both worlds: metrics for debugging system issues and event-based observability to debug application issues. It provides a single interface to identify and diagnose complex performance and quality issues, regardless of where they originate. Organizations using Honeycomb can gain quick insights into their application-level issues through observability and system-level issues through Honeycomb Metrics. This reduces the need to context switch and connect the dots between tools, which reduces the cognitive load for the team, and results in faster issue resolution.

“For too long, engineering teams have been forced to cobble together traditional monitoring tools and use metrics in ways that have proven ineffective in diagnosing the performance issues common in today’s complex environments,” said Christine Yen, CEO of Honeycomb. “Honeycomb Metrics, with native support for system-level metrics, together with event-driven observability at the application level, provides a best-in-class solution. It is a continuation of our commitment to help organizations across the globe boost their business performance.”

Honeycomb Metrics, available to all enterprise customers, ingests metrics data from OpenTelemetry, Prometheus, or Amazon CloudWatch. Visualizations of systems metrics are then created alongside application data visualizations. Customers can then quickly correlate or rule out the impacts underlying systems have on application performance, making it substantially faster and easier to identify the source of issues.

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Honeycomb Metrics Released

Honeycomb announced that the Honeycomb Metrics feature is generally available for its enterprise customers.

Organizations using Honeycomb for observability now have a new metrics capability to quickly identify and resolve system issues.

Used together—Honeycomb’s observability platform for exploring application data and Honeycomb Metrics for monitoring system data—developers now have complete visibility into how their code is behaving and performing, down to the individual request level, and down to the health of the underlying systems running their code. The sum of application behavior and system behavior determines the user’s experience, which can now be explored in its entirety, without having to switch between different tools, resulting in faster debugging workflows.

Honeycomb now gives engineering teams the best of both worlds: metrics for debugging system issues and event-based observability to debug application issues. It provides a single interface to identify and diagnose complex performance and quality issues, regardless of where they originate. Organizations using Honeycomb can gain quick insights into their application-level issues through observability and system-level issues through Honeycomb Metrics. This reduces the need to context switch and connect the dots between tools, which reduces the cognitive load for the team, and results in faster issue resolution.

“For too long, engineering teams have been forced to cobble together traditional monitoring tools and use metrics in ways that have proven ineffective in diagnosing the performance issues common in today’s complex environments,” said Christine Yen, CEO of Honeycomb. “Honeycomb Metrics, with native support for system-level metrics, together with event-driven observability at the application level, provides a best-in-class solution. It is a continuation of our commitment to help organizations across the globe boost their business performance.”

Honeycomb Metrics, available to all enterprise customers, ingests metrics data from OpenTelemetry, Prometheus, or Amazon CloudWatch. Visualizations of systems metrics are then created alongside application data visualizations. Customers can then quickly correlate or rule out the impacts underlying systems have on application performance, making it substantially faster and easier to identify the source of issues.

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For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

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In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...

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