Operations | Monitoring | ITSM | DevOps | Cloud

AI budgeting is the process of planning, allocating, and forecasting an organization's AI spend: model and API costs, AI infrastructure, tooling, and the people running it all. It differs from traditional budgeting because AI spend is usage-based, scales with product success rather than headcount, and often spans multiple providers.

Months have different numbers of days, and a monthly cost chart built on raw totals mixes that calendar difference into the trend. A 28-day February next to a 31-day March shows a 10.7% increase even when daily spend never moved. The same math works in reverse: real growth in a short month can look flat, hiding an increase worth investigating. That costs you time in two places. The first is triage.

This article will walk through various types of embedded Linux logging platforms and consider tradeoffs between logging storage media, compression, and verbosity to enhance device debugging without eating into your limited resource budget.

Every morning, before anyone on the team has looked at a dashboard, a Claude Routine has already read around 800 of the previous night’s conversations from Seer, Sentry’s AI agent for triaging and fixing errors. It flags the ones that look broken, and files tickets for anything new. By the time we sit down with coffee, the triage is mostly done.

By the time an IoT device shows up in an incident review, it has usually already done its damage. Not the dashboard-gap kind. These devices are load bearing. They sit in the control path of substations, haul trucks, pump stations and cold rooms, so when they go blind the blast radius gets measured in tripped relays, spoiled stock, and unplanned outages rather than in missing datapoints.

Tracing is one of the richest observability signals, but it's also noisy and susceptible to data bloat. In a busy system, the vast majority of traces describe the same healthy, fast, successful request over and over, so most organizations downsample their traces to cut costs. But that approach has consequences, since the sampling strategy you choose determines whether you get a faithful picture of your whole system, or just a smaller, blurrier copy of your busiest endpoints.

A customer reports that checkout failed twice before they gave up, then stops replying to the support thread. What do you actually have to work with? A timestamp, a browser string, and a description written by someone who was not looking at the console. Session replay answers the question that ticket cannot. It rebuilds that single visit in the browser and plays it back step by step, so the click that failed, the field that rejected input, and the script error that fired alongside it appear in sequence.

How many privacy requests is your organization working on right now, and how many of them are still inside their legal deadline? The answer usually lives in several places at once. Shared mailboxes hold some, web forms hold others, and legal keeps a tracker of its own. That scattering is the problem. A CCPA request carries a hard statutory deadline and a documentation duty behind it, yet privacy work is the one regulated workload in most organizations that never entered the service management system.

You may be familiar with web card skimming, where a fraudster replaces the payment iframe on an ecommerce site with a fake version designed to intercept payment details or divert a transaction. However, there’s another variation that, while not new, is something we encounter far less often. It was highlighted this week during a call from an ecommerce vendor: redirection fraud. In this scenario, the attack starts much earlier in the customer journey.

Summary A new async Rust client for InfluxDB. Built for the edge gateways, embedded systems, and high-throughput ingest pipelines where Rust already runs. Table of Contents Time series data shows up wherever the physical world meets software. A satellite constellation streams altitude, power, and thermal telemetry from every spacecraft on every pass. A factory floor running on Industry 4.0 principles instruments every line, every motor, every batch.