Operations | Monitoring | ITSM | DevOps | Cloud

This month, two FinOps research reports landed in close proximity. One from the FinOps Foundation — their 6th annual State of FinOps, drawing on a broad global practitioner community. One from CloudZero: FinOps in the AI Era: A Critical Recalibration, built on responses from 475 senior leaders at cloud-mature, AI-active organizations, with a focused lens on how AI is reshaping cloud cost management. Read each alone, and you get a useful snapshot for your business.

For decades, digital accessibility in state-funded higher education has largely been a "reactive" game. If a student with a visual impairment reported an issue with a tuition portal, the university would scramble to provide an accommodation. As long as the institution could show "meaningful progress" toward compliance, it was generally shielded from significant legal repercussions. That era is officially ending. The U.S.

Modern cloud environments include thousands of resources across providers, teams, and accounts. Organizations need the ability to quickly locate the right resources so that they can manage resource compliance and troubleshoot issues. When engineers need to answer questions such as which databases are still on extended support or which storage buckets lack encryption, they often have to switch consoles, use provider-specific query languages, and know obscure version strings or configuration flags.

Years in the FinOps trenches taught me one thing: The data has never been the problem. The data exists. It’s out there, scattered across provider invoices, buried in tagging gaps, locked behind dashboards that maybe three people in your org actually know how to navigate. The real problem? Nobody can get to it when they need it. Engineers ship features without understanding what they cost the business, let alone whether they improved margin.

Somewhere between a model’s first demo and its first production workload, the cost conversation changes completely. Training is a big number, but it’s a finite one. Inference isn’t. Every user interaction, every query, every API call triggers compute behind the scenes — and unlike training, inference never stops billing. That shift from one-time expense to ongoing operational cost is where inference economics begins.

Welcome to Beyond the horizon, a monthly series celebrating the people who shape Upsun’s culture, innovation, and heart. In this month’s edition, we’re featuring Ana Cidre, Director of Developer Advocacy at Upsun and one of the key voices shaping how developers experience our platform today and in the future.

Discover how cloud application slowness occurs despite healthy metrics and how unified observability helps identify hidden bottlenecks and resolve issues faster.

Today we’re announcing the CloudZero Claude Code Plugin, a new capability that puts CloudZero’s full cost intelligence model directly inside Claude Code, where engineers and technical FinOps practitioners already work. The plugin connects a Model Context Protocol (MCP) server and nine pre-packaged investigation skills to CloudZero’s cost data, covering cloud and AI spend across AWS, GCP, Azure, Snowflake, MongoDB, OpenAI, Anthropic, and more.

Cloud migration strategies range from quick rehosting to complete application refactoring. Lift and shift migration (also called rehosting) moves applications from on-premises to the cloud with minimal changes, promising speed and lower initial costs compared to other strategies. But does lift and shift actually deliver long-term savings? This guide examines when lift and shift makes sense, when it creates hidden costs, and what alternatives exist.