27 articles tagged with Observability.
Cutting a log bill is not a procurement exercise. It is four decisions about what you drop at the agent, what you index, how long you keep it, and what you never send at all.
Every log platform looks affordable at proof-of-concept volume and expensive at production volume. The pricing model, not the feature list, decides which one you can live with.
journald is the default log sink on every systemd distro, and most of it runs on defaults nobody chose. Here is how to actually control it.
AIOps applies machine learning to your operational telemetry to cut alert noise, catch anomalies early, and speed up incident response.
A practitioner's guide to tracing, evaluating, and debugging LLM agents in production with the tools that actually earn their keep.
The observability market is huge and the pricing is a minefield. This is the map to the tools that matter, what each is best at, and how to avoid a runaway bill.
A practitioner's guide to tracing, cost tracking, and evaluating LLM apps in production with Langfuse, Helicone, Arize Phoenix, and LangSmith.
Datadog bills climb quietly until finance forwards the invoice. Here's the playbook we run to cut spend hard while keeping every signal that matters.
One tool is built to answer questions you didn't know you had. The other watches everything at once. Here is how they actually differ in practice.
OTel promises no lock-in, vendor agents promise zero-config depth. Here is where each one actually earns its keep once you run it in production.
Edge code runs in hundreds of PoPs, lives for milliseconds, and gives you no shell. Here's how we get logs, traces, and metrics out of it anyway.
Datadog does everything and bills you for all of it. SigNoz covers the core APM story on your own ClickHouse. Here's when the trade is worth it.
