August 2025 Summaries

Honeycomb for Frontend Observability is a powerful tool that significantly improves the process of diagnosing and enhancing web performance by providing real-time insights into frontend systems. Engineers can quickly identify and resolve performance issues by using Honeycomb's web SDK, which integrates frontend codebases into end-to-end traces, allowing for the detection of issues in both frontend and backend components. The platform enables users to create detailed queries to monitor Core Web Vitals (CWVs) such as Largest Contentful Paint, Cumulative Layout Shift, and Interaction to Next Paint, offering a comprehensive view of a site's health. By visualizing route-specific data, engineers can pinpoint which routes load the most JavaScript and track performance changes over time, making it easier to prioritize and address the most impactful issues. Honeycomb's integration with custom instrumentation and OpenTelemetry extends its analytical capabilities, providing granular insights into specific elements that may be contributing to poor performance, ultimately streamlining the improvement process and reducing the time spent on manual diagnostics.

Honeycomb, an observability tool originally designed for engineers, is increasingly being utilized by product management teams, like those at Moov, to gain real-time insights into system behaviors and customer interactions. While Honeycomb doesn't replace traditional product tools like Amplitude or Mixpanel, it complements them by providing context-rich, real-time answers that help in debugging and understanding user behavior, such as tracking version sprawl and performance metrics. Moov's product team, for instance, benefits from using Honeycomb to monitor frontend applications and detect anomalies, enabling them to prioritize bug fixes and provide swift customer support by tracing specific events with precision. Though not a standalone product tool, Honeycomb, when used alongside platforms like BigQuery and Looker, empowers product teams to perform detailed analyses and generate stakeholder reports, thereby addressing most of their data needs efficiently.

New Relic serves as a prominent application performance monitoring solution, offering visualization, monitoring, and troubleshooting of systems, but as businesses grow, there's a need to explore alternative observability tools that deliver greater insights and cost efficiency. The guide evaluates seven alternatives to New Relic, such as Honeycomb, Datadog, Dynatrace, Sentry, Grafana, Splunk, and SolarWinds, focusing on features, benefits, and pricing. Honeycomb offers a single source of truth through wide event storage and native OpenTelemetry support, while Datadog provides full-stack observability with extensive integrations. Dynatrace emphasizes AI-powered automation, Sentry focuses on error monitoring, Grafana excels in custom dashboards, Splunk specializes in log analytics, and SolarWinds integrates log analytics and APM tools with AI insights. A crucial aspect of selecting the right tool is compatibility with OpenTelemetry, ensuring flexibility and ease of transitioning between platforms as business needs evolve.

Honeycomb has integrated the large language model Claude into its workflows, enhancing efficiency across various departments such as development, product management, marketing, and sales, and enabling the delivery of AI-powered features to improve user experiences. To optimize and monitor Claude's usage and associated costs, Honeycomb has launched an integration suite compatible with any observability tool supporting OpenTelemetry, including a new OpenTelemetry Collector receiver available under the Apache 2.0 license. This receiver offers flexibility in recording and analyzing cost and usage data, with plans to include a one-click integration for easier deployment in the Honeycomb Telemetry Pipeline. Additionally, Honeycomb has introduced a board template for Claude usage and cost data, allowing users to break down data by API key, model, and more, transforming usage reports into actionable business intelligence for informed decision-making regarding AI investments and resource allocation.

Honeycomb has announced the general availability of its tagging feature across service level objectives (SLOs), triggers, and boards, allowing over 100 current customers to better organize and manage their observability resources. This feature addresses the common challenge of navigating a cluttered observability landscape by enabling users to categorize resources using flexible key:value pairs, which align with how teams naturally think about their workflows. Tags offer a scalable and familiar method of organization without the constraints of traditional folder structures, facilitating resource discovery and access control. The implementation of tags has already led to the creation of approximately 700 unique tags and tagging of around 8,000 resources, with a significant portion of these tags being integrated into workflows via API. The initial recommended tags include team identity, service identification, and environment classification, which provide clarity and operational safety. With continued development, Honeycomb plans to expand tag support and introduce features like smart tag suggestions and persistent tags to further enhance resource management and discovery.

Honeycomb's Error Analysis for Frontend Observability, now in public beta, offers a comprehensive tool for analyzing client-side errors in web and mobile applications without needing to write queries. This new feature enables development teams to efficiently investigate and prioritize errors by providing a trace-first workflow that integrates error tracking, performance tracing, and session analysis into a unified view. By allowing users to filter errors based on release, platform, or environment and providing detailed stack traces and session-level insights, Honeycomb helps teams quickly identify root causes and understand user impacts. Additionally, with OpenTelemetry, developers can customize error reporting by capturing specific business-level conditions, enhancing the meaningfulness and utility of error data. This streamlined approach aims to reduce the time and effort spent on diagnosing issues, thereby minimizing user disruption and improving overall system health.