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AI Budgeting: Are We All Just Winging It?

Your AI budget was locked in January. Then the CEO said everyone should be building. Now AI spend is spread across 20 vendors, company credit cards, month-to-month invoices, and enterprise plans, and the board wants to know what it all returned. Emily Allen, Director of Finance & Accounting at CloudZero, shares how she's rewriting the rulebook for AI spend governance, budgeting, and forecasting. She uses CloudZero to see every dollar of AI spend by team, project, and feature.

Any agent anywhere: How to use Grafana Cloud with Claude, Cursor, and any other AI agent

AI agents are becoming part of everyday engineering workflows, helping teams write code, troubleshoot issues, and automate tasks. But to be truly useful in production, these agents need access to more than code. They also need observability data and context to understand what's happening across your systems, investigate problems, and help you take action.

AI ROI measurement: what the 23% do differently

23% of finance leaders can both allocate AI spend systematically and prove its return to a board. Only 3% of them failed to secure an AI investment, against 19% of everyone else, and they spend what their peers spend: 41% plan a FY2027 increase of 20% or more, against 38%. Measuring return alone buys nothing, because companies claiming ROI they cannot allocate report the same board-proof gap as companies claiming no return at all.

AI cost visibility: how to see AI spend as it happens (2027)

AI cost visibility is the ability to see what AI spend is buying, by person, team, feature, customer and activity, before the bill arrives. The invoice can't provide it: it records tokens and hours, not decisions. It comes from capturing each AI event as it occurs, with business context attached, and reconciling to the bill. Take 50 million input tokens and 10 million output tokens in a month. On Claude Haiku 5.5 that is $10. On Claude Fable 5.1, the same tokens cost $1,000.

Best AI for Technical Interviews When Trade-Offs Count More Than the Code

Generative AI can crank out code, yet interviewers judge how you think. CoderPad's 2026 survey found that 66 percent of hiring leads praise candidates who fix AI errors, while 56 percent reward clear trade-off explanations. Stack Overflow's 2025 Developer Survey echoes this: 66 percent of developers name AI solutions that are "almost right, but not quite" as their biggest AI frustration. Canva agrees, applauding applicants who "offer alternative approaches and weigh trade-offs thoughtfully."

Occupational Safety Tech: How Modern IoT and Sensors Are Preventing Future Worksite Toxins

Chemicals lurk in industrial air unnoticed until someone gets sick. According to estimates from the International Labour Organization, over one billion workers face hazardous substance exposure globally every year.

We Rebuilt Harness for Agents: Introducing a New Platform Experience

Hundreds of millions of deployments taught us what delivery takes. The new platform experience applies it to a world where agents do the work. A year ago, the question was whether agents could write code. That question is settled. The new question is whether they can ship it. Building an agent is easy. Running one inside a real delivery process is not. Real work runs through pipelines, approvals, data, and quality gates. Delivery has always been designed around the people doing the work.

GitHub's AI Coding Boom Exposes a Bigger Security Problem

GitHub's AI coding boom is exposing a major problem in software security. As AI coding agents generate more code and developers push commits faster than ever, can traditional DevSecOps and shift-left security practices keep up? In this episode of ShipTalk, cybersecurity leader Jake Bernardes breaks down GitHub Universe's AI coding trends and argues that shift-left security is dead in the age of AI-generated code.

Highlights from Oracle AI World 2026

Oracle AI World 2026 brought customers and partners to Las Vegas to explore how AI can support their existing business systems. At the event, Datadog participated in a session with Peloton Consulting Group and hosted a booth covering observability across Oracle Fusion Cloud Applications, Oracle Database, and Oracle Cloud Infrastructure (OCI).