The New Stack Podcast

What’s Driving the Rising Cost of Observability?

Jan 30, 2025

Christine Yen, Co-founder and CEO of Honeycomb.io, discusses the rising costs of observability in modern cloud-native systems. She highlights how traditional logging and monitoring tools struggle to cope with today's software complexities, leading to inefficiencies. Yen emphasizes the need for innovative solutions that prioritize user experience, like Service Level Objectives (SLOs). She also explores the role of AI and OpenTelemetry in addressing these challenges, showcasing the potential for enhanced insights in software management.

24:55

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Episode guests

Christine Yen

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Episode notes

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Quick takeaways

Traditional observability tools struggle with the complexity and scale of modern cloud-native systems, increasing costs and inefficiencies.

The integration of AI in observability presents both opportunities for enhanced insights and challenges with non-deterministic behaviors, necessitating advanced tracking solutions.

Deep dives

Understanding Observability Challenges

Observability has become increasingly crucial yet complex in the realm of cloud-native software development. Three primary categories of tools—logging, monitoring, and Application Performance Monitoring (APM)—each present distinct challenges. Logging tools excel in flexibility but struggle to handle the vast amounts of data in modern systems, making them inefficient for quick answers. Monitoring tools prioritize speed over flexibility, which is problematic in today's dynamic environments that require granular insights, rendering traditional approaches insufficient for effectively managing numerous microservices.

Intro

2min

Navigating Challenges in Cloud-Native Observability

4min

Navigating the Complex Costs of Modern Observability

4min

The Costly Impact of Traditional Monitoring Tools on Modern Infrastructures

2min

AI and Observability: Navigating Challenges and Opportunities

12min

Observability is expensive because traditional tools weren’t designed for the complexity and scale of modern cloud-native systems, explains Christine Yen, CEO of Honeycomb.io. Logging tools, while flexible, were optimized for manual, human-scale data reading. This approach struggles with the massive scale of today’s software, making logging slow and resource-intensive. Monitoring tools, with their dashboards and metrics, prioritized speed over flexibility, which doesn’t align with the dynamic nature of containerized microservices. Similarly, traditional APM tools relied on “magical” setups tailored for consistent application environments like Rails, but they falter in modern polyglot infrastructures with diverse frameworks.

Additionally, observability costs are rising due to evolving demands from DevOps, platform engineering, and site reliability engineering (SRE). Practices like service-level objectives (SLOs) emphasize end-user experience, pushing teams to track meaningful metrics. However, outdated observability tools often hinder this, forcing teams to cut back on crucial data. Yen highlights the potential of AI and innovations like OpenTelemetry to address these challenges.

Learn more from The New Stack about the latest trends in observability:

Honeycomb.io’s Austin Parker: OpenTelemetry In-Depth

Observability in 2025: OpenTelemetry and AI to Fill In Gaps

Observability and AI: New Connections at KubeCon

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