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Cost Per Outcome: AI Cost Management in Harness | Harness Blog

Companies are shipping AI features at a pace cloud teams have rarely seen. New agents, new copilots, new flows powered by language models, all moving from prototype to production in weeks. The spend that comes with it is real and accelerating, and most teams are seeing it on the invoice before they see it anywhere else. The question is no longer how much you're spending on AI. It's whether each dollar is producing a real outcome, and whether you can govern that spend before the next invoice arrives.

We're releasing the financial control plane for AI spend

Gartner forecasts $2.6 trillion in global AI spend this year. Most of it lands in invoices that don’t connect dollars to the developers who spent them, the customers they served, or the features they shipped. AI billing is a mess. CloudZero is the financial control plane for AI spend. Three capabilities, available today, reveal the by-customer, feature, and developer ROI of AI: 1. Real-time Spend: Capture every dollar spent on AI, at the source. 2.

AI spend is exploding. Most companies cannot prove ROI.

Only 14% of CFOs can prove AI ROI. OpenAI’s gross margin fell from 40% to 33% in 2025, well below its 46% target. Even the AI providers cannot reliably predict what AI will cost. Companies are scaling AI faster than they can measure it: more tokens, more agents, more model calls, more spend moving through systems finance cannot yet see. Every board is asking the same question: What is this AI investment returning? Most companies cannot answer it. The ones that can will compound their advantage.

Are AI Tools Actually Improving Developer Experience? (Experts Cut Through the Hype)

AI tools are spreading across the entire software development lifecycle - but are they actually making developers more productive, or just adding noise? In this panel from Context Conference, Najla Elmachtoub (Squadformers) moderates a sharp, no-fluff conversation with Nathen Harvey (Google, DORA program), Bill Harding (GitClear), and Jeremy Castile (GitKraken) on what's really working when it comes to AI and developer experience.

The Hybrid Shift: Where Workloads Are Headed and How to Move Them

Businesses migrating from a single, public cloud provider has been the direction of travel of UK digital infrastructure for years. As far back as 2020, Barclays found that 43% of enterprise CIOs were already planning to bring workloads back from the public cloud to on-premises or private cloud infrastructure. Since then, IDC, Gartner and a host of vendor surveys have tracked an increase in this intention.

Bring Your Playwright Suite to Harness: No Rewrites, No Infrastructure, AI-Powered Triage Built In | Harness Blog

Key Takeaway: Harness AI Test Automation now runs existing Playwright suites without code changes, adds AI-powered failure triage, and integrates test results directly into build and deployment pipelines. ‍

The AI Agent Accountability Gap: Why Network Policies, API Gateways, And RBAC Are Not Enough

In The Five Pillars of AI Agent Accountability: A Diagnostic Framework for Engineering Leaders, we walked through each pillar of AI agent accountability (traceability, authorization provenance, identity and ownership, policy at scale, and human oversight) and argued that most enterprises today sit at Level 0 or Level 1 of the Accountability Maturity Model. The most common reaction we get when we share that framework is some version of: “We’re already covered. We have network policies.

Let AI Run Your Cloud Infra? Ex-VMware & SAP Architects Weigh In. (ft. TechWorld with Nana)

Can you trust AI to run your platform? AI can now spin up production infrastructure in minutes — but speed cuts both ways. In this episode, Nana(TechWorld with Nana) sits down with Doron Grinstein and Dan Wilson, two architects who built, broke, and fixed platforms at VMware and SAP, for a no-hype look at platform engineering in the age of AI.