Operations | Monitoring | ITSM | DevOps | Cloud

LLM cost optimization: 7 strategies to cut inference spend

LLM cost optimization is the practice of cutting what you spend on large language models, mostly inference, without losing the quality that makes the AI worth running. The biggest levers are routing requests to cheaper models, caching repeated tokens, batching anything that can wait, trimming prompts, right-sizing models, cutting calls you do not need, and putting one gateway and cost view in front of all of it.

What is FP&A? Financial planning and analysis in the AI spend era

FP&A stands for financial planning and analysis. It is the corporate finance function responsible for budgeting, forecasting, variance analysis, and decision support. If you're asking what is FP&A in practice: FP&A teams build the annual operating plan, project revenue and expenses, explain gaps between plan and actuals, and give leadership the numbers behind strategic decisions. Accounting reports what happened. FP&A models what happens next.

Software is a team sport. Most AI tools forgot that.

Every AI coding tool ships the same promise: your developers, faster. Autocomplete in the IDE, agents in the terminal, a working prototype before lunch. And it delivers, at least for the person holding the keyboard. The problem is that most of what it takes to ship software was never a solo activity, and that is the part the market keeps skipping.

Why Every Modern Security Operation Center Needs Automation and AI

Security teams no longer face a simple monitoring problem. In most cases, they face - While cloud workloads change by the minute, identities move across applications. In general, endpoints appear outside the traditional perimeter. Meanwhile, the modern security operations center must interpret all that activity. It must also not let a genuine threat disappear inside routine noise. Although traditional processes still matter, manual triage cannot carry the entire workload anymore. In fact, analysts lose valuable investigation time if they -

Agentic AI cost: why agents burn tokens and how to control it

Agentic AI cost is what you pay to run AI agents, and it is mostly tokens. An agent does not answer once. It loops, calls tools, reads the results, and reasons again, re-sending a growing context every step. Anthropic found agents use about 4x the tokens of a chat, and multi-agent systems about 15x. You control it by capping runs, right-sizing the architecture, routing, caching, and measuring cost per task, then tying every agent to the AI ROI it produces.

AI cost monitoring: what it is, how it works, and why real-time visibility matters

AI cost monitoring is the continuous tracking of AI and LLM spend in real time, broken down by the models, features, teams, and customers generating it. It is not the same as reading the monthly bill - done well, it shows spend as it happens, flags anomalies before they become invoices, and connects every dollar to an outcome so finance can protect AI ROI instead of explaining it after the fact.

4 Cloud-Native Challenges AI SRE Is Solving in 2026 and the 3 New Ones to Look Out For

AI SRE is making real strides in resolving some of the greatest pains related to incident response, troubleshooting, and complex root cause analysis. The on-call rotation, the war room, the week-long RCA, and the ticket queue that ate a third of every platform engineer’s week all look different now than they did two years ago.