How Time Series Databases Work—and Where They Don’t

In my previous post, we explored why Honeycomb is implemented as a distributed column store. Just as interesting to consider, though, is why Honeycomb is not implemented in other ways. So in this post, we’re going to dive into the topic of time series databases (TSDBs) and why Honeycomb couldn’t be limited to a TSDB implementation.

Why Does Observability Feel so Expensive? (Because it Is)

In Charity's recent webinar with James Governor of RedMonk, she shared that most companies now spend somewhere between 20 and 25% of their entire infrastructure budget on their observability tools.

What Is Observability? Key Components and Best Practices

Software systems are increasingly complex. Applications can no longer simply be understood by examining their source code or relying on traditional monitoring methods. The interplay of distributed architectures, microservices, cloud-native environments, and massive data flows requires an increasingly critical approach: observability.

Observability Day San Francisco: The Future of AI and Observability Is Bright

AI and observability are no longer separate conversations—they’re deeply intertwined. Across keynotes, panels, and demos, speakers at Honeycomb's Observability Day San Francisco unpacked what that means for engineering teams today: faster insights, smarter tools, and new challenges to solve.

How Product Managers Can Benefit From Honeycomb

Observability tools like Honeycomb are built for engineers, not PM teams… but that doesn’t mean there’s no benefit to having your PMs in Honeycomb. Whether it’s debugging a weird customer issue or tracking how a feature is used in the wild, observability gives PMs something traditional product tools can’t: real-time answers with full context, down to a single user.

7 Alternatives to New Relic

New Relic is a well-known application performance monitoring (APM) solution that helps engineers visualize, monitor, and troubleshoot their systems. As businesses evolve, engineers must deliver new capabilities at greater scale and speed, often while managing an increasing number of services and systems. This naturally raises the question: what tools can help?

From Chaos to Clarity: How Honeycomb Tags Are Transforming Developer Workflows

We're thrilled to announce that Honeycomb Tags are now generally available across SLOs, triggers, and boards! Over 100 customers are already actively tagging their observability resources in Honeycomb today.

Honeycomb Named a Visionary in the 2025 Gartner® Magic Quadrant™ for Observability Platforms

Honeycomb Users Are Living in the Future, Part 1: Sampling

Is Your Observability Strategy Boardroom-Ready?

It’s The End Of Observability As We Know It (And I Feel Fine)

In a really broad sense, the history of observability tools over the past couple of decades have been about a pretty simple concept: how do we make terabytes of heterogeneous telemetry data comprehensible to human beings?

Beyond Shift Left: Engineering Leaders Increase Speed and Resilience With Observability

We recently had the privilege of hosting several industry experts and technology executives across platform strategy, SRE, and engineering enablement for breakfast at our Observability Day in London.

Dashboards, or Launchpads?

Introducing Native Mobile Support in Honeycomb for Frontend Observability

You shipped your latest release. You tested it on emulators, QA devices, and the latest OS versions. But now it’s live and running on thousands or millions of real devices, across a jungle of screen sizes, hardware specs, OS versions, and network conditions. A user reports a crash on an old Samsung device over 3G. Someone else complains the app feels “sluggish” after updating. You dig through logs. Rebuild test cases. Ping the backend team. Try to reproduce. Yet, still no answers.

Reporting CSP Errors in Honeycomb With the OpenTelemetry Collector

The HTTP Content-Security-Policy response header is used to control how the browser is allowed to load various content types. It is used to control which URLs, fonts, images, scripts, and more can be loaded…

How Much Should I Be Spending On Observability?

In last week’s piece, we talked about some of the factors that are driving costs up, both good and bad, and about whether your observability bill is (or should be) more of a cost center or an investment. In this piece, I’m going to talk more in depth about cost drivers and levers of control.

Data Strategy for SREs and Observability Teams

The idea that telemetry data needs to be managed, or needs a strategy, draws a lot of inspiration from the data world (as in, BI and Data Engineering). Your company most likely has a data team that manages the data warehouse(s), data pipelines, data sources, and reporting tools. These teams are also constantly balancing costs with their user and stakeholder needs, usability, data retention, granularity, etc. Sound familiar? That’s because if you’re working on observability data, these teams are at least several years ahead of you in addressing these tradeoffs and considerations—and can teach us quite a lot.

How Much Should I Be Spending On Observability?

In 2018, I dashed off a punchy little blog post in which I observed that teams with good observability seemed to spend around ~20-30% of their infra bill to get it. I also noted this was based on absolutely no data, only my own experiences and a bunch of anecdotes, heavily weighted towards startups and the mid-market tech sector.

Observability: It’s Every Engineer’s Job, Not Just Ops’ Problem

Observability is way more about software engineering than it is about operations. Operators are users of observability data for monitoring and alerting on systems. They’ll use that telemetry data to scale systems, or potentially debug the outside of applications. In contrast, software engineers are creators, designers, and users of observability data, and where they use that data is much wider than that of pure operators of production systems.

What Is a Telemetry Pipeline?

In a simple deployment, an application will emit spans, metrics, and logs which will be sent to api.honeycomb.io and show up in charts. This works for small projects and organizations that do not control outbound access from their servers. If your organization has more components, network rules, or requires tail-based sampling, you’ll need to create a telemetry pipeline.