Operations | Monitoring | ITSM | DevOps | Cloud

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.

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.

A log line arrives as plain text and leaves as a record you can query. Six steps sit between those two states. Each one adds something useful, and each one costs you time, CPU, or storage. Most teams never look at that chain until a search comes back empty. Here is what log processing does to an event, step by step: By the end you can look at your own chain. You will know what each step buys you. Six steps turn a raw log line into a searchable record.

Sensitive values reach your logs without anyone deciding they should. A debug line prints a whole request object. An error message carries the query string. A customer email address is suddenly stored in three systems. PII redaction in logs then gets treated as one setting to switch on. In practice it covers four separate treatments. The value is already inside the message before log ingestion finishes. In this blog, you will: By the end you can write a rule for each field and defend it.

Datadog’s 2026 State of AI Engineering report showed organizations’ LLM inputs swelling rapidly as context engineering expands. In March 2026, 69% of all input tokens in Datadog customer traces were for system prompts: internal instructions, policy definitions, and tool guidance providing context and guardrails around the user input. This suggests that most context engineering spend among Datadog customers is going toward optimizing repeating system prompts in heavily scaffolded agent systems.

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.

Summary This guide walks through an end-to-end Grafana and InfluxDB 3 integration using a realistic dataset you generate yourself. The tutorial covers getting data in, transforming it, connecting Grafana, and building real dashboards. Table of Contents InfluxDB and Grafana are the most common pairing in time series monitoring, and division of labor between them is simple.

If your team works with SAP HANA, the main challenge usually isn’t finding another dashboard. The real difficulty is identifying which tool can quickly help you trace vague complaints about slow transactions to their root cause. In many setups, SAP HANA monitoring is divided among native SAP interfaces, cloud monitoring, infrastructure dashboards, and the broader monitoring systems used by the rest of IT. This fragmented approach can slow down root cause analysis.

The ability to log into all corporate applications with one username and password is pretty convenient, even compared to a password manager. As a benefit, the IT department can centrally enforce one desired two-factor auth mechanism. Now we, Icinga, also provide a so-called OpenID Connect integration for single sign-on. By the end of this text you’ll know how to connect your Icinga Web instance to the ID provider of your choice.