Operations | Monitoring | ITSM | DevOps | Cloud

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Background jobs handle the critical work that happens outside the request path: processing payments, sending emails, generating reports, syncing data. They keep applications running smoothly, but the signals they produce look different from API endpoints. Most teams start with queue metrics—how many jobs are waiting and how quickly they complete. These metrics provide the foundation, but job health extends beyond throughput.

LangChain applications fail differently than traditional web apps. A single user request can trigger 15+ LLM calls, cost $5 in tokens, and fail silently without throwing errors. One team discovered a $12,000 OpenAI bill caused by a recursive chain with no monitoring. This guide shows how to implement observability for LangChain applications, giving you complete visibility into performance, costs, and errors before they impact your users or budget.

A service catalog gives teams a shared view of their systems—what services exist, who owns them, how dependencies are structured, and the SLAs that guide expectations. It’s an important part of development infrastructure because it helps everyone speak the same language about services. Service catalog observability builds on that foundation.

The irony isn't lost on us - observability platforms are built to be proactive about system health, yet when it comes to managing observability costs themselves, teams are forced to be reactive. Today, that changes with Cost Meter, now live in our platform. Cost Meter transforms observability spend management from a monthly billing surprise into a proactive, data-driven process with hourly aggregated metrics that give you complete visibility into your telemetry ingestion patterns.

In my previous post I made a bold, sweeping statement that APM is not - in the most specific sense - a subset of observability. Still standing by it I stand by that because words matter and - like many "monitoring engineers" (IT folks who make monitoring and observability their specialty) - I, too, bear scars from the flame-wars on Twitter back in the 2020's where we fought internecine battles over the proper definition of (and number of pillars in) “observability”.

Modern systems are wonderfully capable, but relentlessly complex. Debugging across microservices, frontends, and cloud edges often means switching between five or more tools, trying to stitch together “what changed” and “why it broke.” Honeycomb’s wide events model has proven to be a superpower for taming that complexity, by allowing you to easily observe and query end-to-end traces without worrying about how much granular data you attach to your events.

The OpenTelemetry Astronomy Shop is a widely used demonstration environment designed to illustrate the concepts and practical implementation of observability in distributed systems. Built as a microservice-based e-commerce application, the demo provides developers with a near real-world environment where they can explore how telemetry data—metrics, logs, and traces—can be collected, processed, and visualized.

Modern engineering teams face a persistent challenge: knowing when something goes wrong before their customers do. With microservices architectures sprawling across dozens or hundreds of services, creating comprehensive alerting becomes an overwhelming task. You're left playing whack-a-mole with manual alert configurations, often missing critical issues or drowning in false positives.

Modern cloud environments are highly complex and dynamic, with critical services relying on large numbers of ephemeral resources. Ensuring observability coverage across this landscape is essential for troubleshooting, maintaining reliability, optimizing performance, and enforcing security standards. But as environments grow more elaborate and their ownership more dispersed, tracking observability coverage becomes increasingly challenging.