Operations | Monitoring | ITSM | DevOps | Cloud

Incident severity levels exist for one reason: so that a responder who was asleep ninety seconds ago can decide, without debate, how many people to wake up. Everything else (the reporting, the SLA math, the quarterly review slides) is downstream of that one decision. If your scale cannot be applied in under thirty seconds by someone with partial information and no context, it is not a severity scale. It is documentation.

Every major security information and event management (SIEM) platform prices on the volume of data you send it. Microsoft Sentinel meters per gigabyte across two tiers. Splunk charges per gigabyte indexed or per compute unit. Google SecOps draws down a prepaid gigabyte credit balance. Elastic Security bills ingest plus retention, or the resources your cluster consumes. Three of the four keep their real rates quote-only. Budgeting starts with the meter.

Every so often we sit down with someone from our support team and turn their week into a blog post. This time, Roberto walks us through four things generating tickets right now: an upcoming Microsoft authentication change, how AI credit pools actually work, multi-account support in Kepler, and a one-line fix for laggy GitKraken on Linux or WSL. Here’s what’s changing and what to do about it.

In order for users to get the most out of the data being sent to Sentry, it’s important that we make it easy to find that data. Our team works on features to help users browse their data to find a particular event using search queries and filters. The search bar enables users to find their data by specifying search terms. Searching uses the Sentry Search Syntax, which can be barrier for users.

A request to upgrade a saturated circuit is easy to raise and hard to defend. The interface graph proves the link is full. It says nothing about which application, host or conversation filled it, so the spend gets approved on assumption instead of evidence. The same missing detail turns up everywhere else. Incidents run long because the cause is guessed at, capacity planning rests on estimates, and security questions arrive weeks after the traffic record expired.

ChatGPT Work has become a common starting point for data and product teams. Analysts open it to compare launch adoption across segments, diagnose a metric that moved overnight, or turn a week of scattered numbers into a readout that a leader can act on. But the moment teams ask whether their experiment actually caused an effect they’ve observed, the conversation stalls.

NetFlow is a popular network protocol for collecting metadata about traffic flows across your environment so that it can be exported for analysis and monitoring. One of the most common issues that users encounter is NetFlow duplication, which occurs when identical flow records from the same conversation are recorded from different sources. Flow duplication inflates traffic data, undermining capacity planning and making top-talker rankings unreliable.

An LLM gateway is a proxy that sits between your applications and model providers, handling routing, failover, caching, and cost controls through one API. The strongest picks in 2026: LiteLLM for self-hosted control, OpenRouter for instant multi-model access, Portkey for managed governance, and Bifrost for production-scale throughput. Enterprises spent $37 billion on generative AI in 2025, a 3.2x jump in one year, per Menlo Ventures.

It’s not the outage, it’s the stretch before you know what actually broke In short: VictoriaMetrics Enterprise support is expertise, not a ticket queue. It’s reactive by design (you reach engineers who know the stack when something breaks), with one proactive service, Monitoring of Monitoring, that watches the health of your VictoriaMetrics observability stack (metrics, logs, and traces).

Engineers ignore cloud costs because of broken feedback loops, not apathy. Learn what AI cost management is, why AEO matters more than ever, and how a cost management agent embeds accountability directly into engineering workflows. Engineers ignore cloud costs because cost data arrives too late and too disconnected from their workflow to act on.