Operations | Monitoring | ITSM | DevOps | Cloud

To manage cloud alerts effectively, define which conditions require immediate action, include useful incident context, and route critical notifications to the engineer currently on call. Add escalation rules for unanswered alerts and test the entire path from detection to response. This process applies whether your team uses AWS, Azure, Google Cloud, or several providers. Cloud monitoring can tell you something is wrong.

Why do users at a branch complain about choppy calls and stalled apps when every WAN circuit on the dashboard still shows up? A link can be reachable without being healthy. Delay and packet loss can rise for hours on a live circuit, and a simple up-or-down check will report nothing unusual the whole time. WAN link monitoring checks the quality of each site-to-site connection by having routers send their own test packets and time the replies.

Across a same-store panel of 422 CloudZero customer organizations, AI reached 3.02% of the median company's cloud bill in September 2026, up 37 basis points from 2.66% in August, the largest monthly gain since we began tracking. The 75th percentile rose to 12.55%. The share of organizations spending at least $1,000 a month on AI barely moved, to 68.0%. Companies already spending drove the growth. Last month we called the median the least informative number in this report. September proved us wrong.

Why do tickets about a slow application keep arriving while every server, link and service check reports normal? That mismatch is the problem end user experience monitoring exists to solve. A healthy server is one thing, but the business notices something else: whether people can finish their work or complete a purchase without delays or errors. Monitoring in most IT operations groups was built to watch the systems from the inside, so it reports CPU load, memory and uptime.

Finance teams reconcile cloud spend period by period. They want each month, week, or day in its own column, so they can scan across a row and see how a service or team changed over time. Explorer’s Aggregate view already answers “what changed and by how much.” But when the question is “what did we spend in June, July, and August, line by line,” that summary needs to become a grid.

Discover how Harness Builds combines Test Intelligence, code coverage, and quality signals to help developers and AI agents validate changes faster and improve code quality. AI has changed the speed at which we can write code. Developers can move from an idea to a pull request faster, agents can work through changes autonomously, and the amount of code moving through development pipelines is only going up. But writing the code is only the beginning.

Kimi API pricing, per million tokens: the flagship Kimi K3 costs $3 input and $15 output with a $0.30 cache-hit rate and 1M context. Kimi K2.6 and the coding-tuned K2.7 Code both run $0.95/$4.00 on 256K context. Kimi K2.5 and the legacy V1 family were retired August 31, 2026.

Discover FinOps best practices tailored for banks. Optimize cloud costs while meeting compliance and security needs. Learn more. Financial institutions face unique challenges implementing FinOps due to strict regulatory compliance, security requirements, and complex legacy systems. This guide covers proven FinOps strategies for banks including cost allocation, governance frameworks, and automated controls that balance innovation with risk management while optimizing cloud spending.

An AI agent and digital certificate inventory is a record of every AI agent and digital certificate an organization runs, with what each one does, who owns it, what it connects to and when it expires or needs a review. IT and IT Asset Management (ITAM) teams build one because both kinds of asset keep critical services running with no hardware to scan, no purchase order to trace and, very often, no owner on record.

Learn how Harness and Cosmos build trusted, governed software factories with automated, end-to-end agent orchestration and pipeline security. Software factories can greatly accelerate software development, but concerns about trust, governance, and predictability are limiting how broadly organizations deploy them.