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Comparisons of SAP observability tools often evaluate which platforms can see inside SAP.Today, that’s nearly all of them. Dynatrace, Datadog, New Relic and Splunk can all get SAP telemetry. None of them are likely the best choice for an SAP-centric application, and we will document why. The questions teams evaluating SAP observability solutions should consider: That last one is where most of these platforms stop, and it is the difference between observability and operations.

Datadog has been named the Company to Beat for observability platforms in the August 2026 Gartner AI Vendor Race research. Datadog has also been named a Leader in the 2026 Gartner Magic Quadrant for Observability Platforms for the sixth consecutive year. We believe that these recognitions reflect what we have been building toward for more than a decade: a single platform where teams can observe, secure, and act on everything that matters across their technology stack.

For teams building AI agents, the feedback loop should already be a familiar idea: watch how the agent behaves, find what needs improvement, ship a change, and measure the result. In theory, each turn builds on the last until the loop becomes a flywheel and your agent is getting more effective with each turn. In practice, many of us are still in reaction mode. A user reports something strange, costs spike, or an eval score drops.

Legacy systems, cloud growth, and AI are reshaping government IT. See how connected visibility helps teams respond faster and keep essential services running.

OpenTelemetry graduated from the CNCF in May 2026 as, in the foundation’s own words, the de facto observability standard. The JavaScript API package alone did 1.36 billion downloads in twelve months. That kind of win has a side effect nobody plans for. Once a wire format is everywhere, has a receiver for every source, a transform language, and an agent your platform team already operates, people start putting things on it that have nothing to do with knowing whether a service is healthy.

Learn why modern IT complexity can slow incident response and how observability, AIOps, operational context, and automation can help teams detect, investigate, and resolve issues faster.

Welcome to the third and final part of our series on AI norms and values. Parts of this doc were extracted and published separately on substack; as a whole, they describe the principles we hold pertaining to technology and AI, and the ethical commitments we make to each other and our customers. We set out to write about AI, and ended up writing about ourselves. These documents are not meant to be aspirational ones; they are derived from how we do our work every day in honeycomb.

Observability can cut alert noise, speed up incident response, reduce downtime, and give engineers more time for planned work. LogicMonitor customers have used those gains to lower costs and make better infrastructure decisions.

As agentic AI workflows gain traction within organizations, those organizations are asking how to account for their behavior while keeping costs manageable. Some are sticking with the old three pillars of observability approach: take a measurement to create a metric, record output to a log, and track serial progress with a trace. Each of these is useful, but treating them as distinct formats from the start means paying for them distinctly too. Separate storage doesn't come cheap.

Eight LLM and AI agent observability platforms compared: what each tracks, pricing and free tiers, self-hosting options, and who each is built for. Sejal Pandey works on content and growth at Last9, writing about observability, reliability, and SRE practices.

I'm investigating repeated errors in my e-commerce application, and I need to get enough context in a single Honeycomb query to piece the entire picture together. Each query returns events based on the event's WHERE clauses, but I want to know several things from outside of the event that recorded an error. Things like: Those attributes are all over the trace. That's going to make a single query tough, right? Wrong!

A critical system has to stay reliable without new failures or added downtime, and live debugging, confirming the root cause without stopping the system, is often the only way to do that. In practice, this means having runtime context: on-demand evidence generated at the point of failure rather than logging configured months earlier, which is what keeps MTBF up, MTTR, and MTTA down.

It's been one year since we issued an AI mandate inside Honeycomb, and we've been doing a lot of reflecting internally on how far we've come and how far we have yet to go.

If you are able to build it better than your vendor, then change your vendor. Not build it. Rishi builds large-scale observability systems at Last9, focusing on reliable and cost-efficient telemetry infrastructure, and writes about the practical lessons learned while operating ClickHouse, VictoriaMetrics, and OpenTelemetry in production.

Observability spend is outgrowing infrastructure budgets. What drives the cost up, how pricing models work, and a practical framework to manage it. Sejal Pandey works on content and growth at Last9, writing about observability, reliability, and SRE practices.

Sampling is a core skill that everyone who runs an observability pipeline at scale will learn. There are lots of tradeoffs within the various decisions you'll make from reducing bandwidth, CPU, and memory, to reducing costs and making the observability backend's performance better for users. Historically, there have only been three mechanisms, each with their own tradeoffs: However, there is a secret fourth option: adaptive tail sampling—which changes those tradeoffs.