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Cloud Asked What It Cost, AI Is Asking What It's Worth | Harness Blog

AI has quickly become one of the largest and fastest-growing enterprise expenses, exposing many of the same governance and visibility challenges organizations previously faced with cloud. Based on findings from the 2026 State of AI in FinOps report, we explore how mature organizations improve AI cost ownership, reduce waste, and build a culture focused on measurable business value.

Shipped: Catch a broken regex before it breaks your rules

Let’s say you want a Matches condition that picks up “prod”, “PROD”, and “Prod”, so you write ‘Matches: (?i)prod’ and check it in an online regex tester before publishing. It looks fine. Python accepts it, and so does JavaScript. Dimension Studio used to accept it too, and the rule is published. Then your data stops updating. The new pattern is what’s keeping it from materializing, but nothing tells you that.

Dashboards aren't (quite) dead

Historically, non-technical stakeholders would’ve had most of their data questions answered either through pre-built dashboards or by asking their Data team (or equivalent). Self-serve analytics tools went a step further by offering safe, governed datasets built by Data teams which let non-technical users dig into data without having to worry about how it joins together, how metrics like “revenue” are defined, and so on.

Who Owns Deployment Governance? Structuring Accountability in the AI Era

In this series, we have talked about how generative AI is shifting the landscape of software creation. In The New Software Creator, we explored how AI expands who can write code. In When Anyone Can Build Software, Deployment Governance Is What Keeps It Safe, we looked at why the deployment pipeline is the ultimate control point. Finally, in Security at Scale: What Changes When Everyone Can Deploy, we dug into the technical realities of patching, container hygiene, and identity management.

How to standardize app delivery across AWS, Azure, and GCP

Running workloads across AWS, Azure, and GCP is the operational reality for most enterprise engineering teams. The challenge isn't the providers themselves, it's what happens when each one accumulates its own delivery pipeline, its own security configuration, and its own environment management tooling. What starts as provider flexibility quietly becomes provider-specific complexity, multiplied across every team that ships.

Agentless Auto-Discovery Keeps Asset Records Current Across IT, OT, and Virtual-No Manual Entry Required

Manual asset entry is the hidden drain on your data center’s productivity. That one missed update causes hours of chasing spreadsheets, hunting down equipment details, and doubting if your inventory matches reality. Hyperview’s agentless asset auto-discovery flips the script, delivering real-time asset data across IT, OT, and virtual environments without the manual hassle. Keep your records current effortlessly and focus on running your data center with confidence.

Are Coding Agents Out of Control?

OpenAI just disclosed that two of its own AI models went rogue during an internal red-team test — escaping their sandbox, reaching the open internet, and hacking Hugging Face on their own. OpenAI called it an“unprecedented cyber incident.” So are autonomous coding agents already out of control? In this episode of ShipTalk — brought to you by Harness — hosts Martin Reynolds and Adam Arellano break down the story that reads like science fiction, then get to the harder truth underneath it. In the same week, OpenAI, Anthropic, and Google all shipped repository-wide coding agents within 24 hours of each other.

Harness + Devin IDE: Automate Governance and Delivery for the Agentic Era

As AI software engineers like Cognition's Devin accelerate code production, downstream delivery, and governance processes must keep pace. In this video, see how Harness closes the gap by providing autonomous oversight for autonomous code. Watch a step-by-step demonstration of Devin fixing a real defect in a broken banking application while the Harness platform stands between the fix and production to ensure complete safety and validation.

Best 7 GPU VPS Provider for Machine Learning (ML) and AI

There is no reason to buy a GPU. That's unless you train your model or do serious image/video manipulation. A GPU server costs several times more than a CPU VPS for the same month. "Nine out of ten requests for a GPU server for AI actually need a mid-size CPU VPS. They are serving a model, not training one. Match the hardware to the task at hand. Save money. Don't compromise on performance.".