|
By Emily Allen
Every finance person I know lives in the same loop approaching the end of the month, quarter, or fiscal year. Leadership wants to know where the financials will land (most times before the close has occurred). CS wants customer margins. Someone on the People team needs each department’s AI spend for an OKR review, and they need it quickly to make business decisions. Each answer sits in a different tool or a different spreadsheet, and I bounce across all of them several times a day.
|
By Kaitlin Woo
You are mid-investigation, chasing a spike or pulling a number for finance, and you hit a term or a workflow you need to look up. You should not have to lose your place to find an answer. Guide lives in a fixed spot in the left sidebar, always one click away. It opens a panel on the right side that sits beside your page instead of covering it. Your chart, filters, and time range stay exactly where they were. Nothing gets rebuilt and you keep the thread of what you were investigating.
|
By David Aponovich
Budget season is starting and most finance teams are finding the AI line is the most evasive line on the page. You lived through the year. AI spend came in higher than planned and moved in ways nobody could foresee or forecast. And when the board asked what it produced, the honest answer probably was “we’re working on it.”
|
By Kevin Lamb
People run Codex on their own laptops. When Codex is signed in with a ChatGPT subscription, OpenAI’s own admin console shows who used it and how much: messages and credits. What it doesn’t show is what any of that usage was for, or how it compares to what your team spent on other AI tools. The CloudZero desktop agent for macOS installs on a Mac, sees the traffic from AI coding tools, and prices what those tools use.
|
By Doug Bonderud
AI is expensive because the model bill is only part of the cost. Three components set the floor: model subscriptions, per-token API pricing, and infrastructure. Three more make it move: adapting models to your business, catching and fixing errors, and rising energy and datacenter costs. Efficiency doesn't fix it, because cheaper AI gets used more, not less. Businesses are willing to spend on AI. Research from Deloitte found that in 2025, 85% of organizations increased their AI investments.
|
By Kaitlin Woo
Months have different numbers of days, and a monthly cost chart built on raw totals mixes that calendar difference into the trend. A 28-day February next to a 31-day March shows a 10.7% increase even when daily spend never moved. The same math works in reverse: real growth in a short month can look flat, hiding an increase worth investigating. That costs you time in two places. The first is triage.
|
By Lyne Carolyne
AI budgeting is the process of planning, allocating, and forecasting an organization's AI spend: model and API costs, AI infrastructure, tooling, and the people running it all. It differs from traditional budgeting because AI spend is usage-based, scales with product success rather than headcount, and often spans multiple providers.
|
By Kevin Lamb
If your engineering teams run on ServiceNow, incidents are where they get work done. Putting cost work into an incident gives it the same path to resolution as any other work item your team handles. When a cost anomaly arrives as an incident, your teams route it, assign it, and resolve it on their usual SLAs. When a savings recommendation arrives as an incident, an engineer owns it and acts on it. Now you can send either straight into ServiceNow.
|
By Doug Bonderud
A strong AI business case ties a specific goal to a measured outcome and a fully-loaded cost. Most fail because they skip one of the three: no clear mandate, an over-broad "AI fixes everything" scope, or a cost estimate that ignores adaptation and error-correction. Build it in six steps: define goals, identify uses, break work into tasks, evaluate models, assess total cost, then launch and refine. Most companies are now spending on AI. Far fewer can show what they got back.
|
By Djo Lopez
You shipped a skill. It worked. You closed the tab. That’s the whole problem. Model choice is a decision you make once, at the moment you’re least equipped to make it: before the skill is even authored. Then you never revisit it, because the skill stopped being interesting the day you got it working. So go back and check. Here’s how.
- August 2026 (27)
- July 2026 (42)
- June 2026 (35)
- May 2026 (21)
- April 2026 (31)
- March 2026 (30)
- February 2026 (23)
- January 2026 (19)
- December 2025 (28)
- November 2025 (25)
- October 2025 (29)
- September 2025 (27)
- August 2025 (25)
- July 2025 (23)
- June 2025 (18)
- May 2025 (21)
- April 2025 (22)
- March 2025 (17)
- February 2025 (18)
- January 2025 (15)
- December 2024 (17)
- November 2024 (18)
- October 2024 (14)
- September 2024 (16)
- August 2024 (17)
- July 2024 (28)
- June 2024 (18)
- May 2024 (14)
- April 2024 (15)
- March 2024 (14)
- February 2024 (13)
- January 2024 (13)
- December 2023 (9)
- November 2023 (14)
- October 2023 (10)
- September 2023 (11)
- August 2023 (10)
- July 2023 (8)
- June 2023 (5)
- May 2023 (9)
- April 2023 (8)
- March 2023 (6)
- February 2023 (11)
- January 2023 (9)
- December 2022 (6)
- November 2022 (10)
- October 2022 (9)
- September 2022 (4)
- August 2022 (7)
- July 2022 (9)
- June 2022 (8)
- May 2022 (9)
- April 2022 (8)
- March 2022 (11)
- February 2022 (6)
- January 2022 (9)
- December 2021 (5)
- November 2021 (6)
- October 2021 (7)
- September 2021 (8)
- August 2021 (3)
- July 2021 (3)
- June 2021 (2)
- May 2021 (3)
- April 2021 (5)
- March 2021 (3)
- February 2021 (5)
- January 2021 (4)
- December 2020 (7)
- November 2020 (4)
- October 2020 (4)
- September 2020 (3)
- August 2020 (4)
- July 2020 (2)
- June 2020 (1)
- May 2020 (1)
- March 2020 (3)
- February 2020 (1)
- January 2020 (5)
- November 2019 (2)
- October 2019 (1)
- September 2019 (6)
- August 2019 (2)
- July 2019 (2)
- June 2019 (4)
- May 2019 (2)
- March 2019 (1)
- December 2018 (3)
- November 2018 (4)
- October 2018 (5)
With CloudZero you get insights about your applications and systems, helping you manage operations at a scale that you’ve never had before. Our platform provides you with insights about every piece of your system, including the real cost of resources, resource utilization, reserved capacity and cost center efficiency.
With the accurate and trusted data provided by CloudZero you can minimize or eliminate under utilized resources, visualize costs for easy comprehension and oversee the entire software lifecycle. Nothing is out of view when using CloudZero’s Observability platform. From regional views to individual resources, you have insights at every level to help you keep your systems running smoothly.
How do we do it?
- Collect and Normalize: CloudZero’s platform starts by collecting the data from your CloudWatch, CloudTrail, VPC Flowlogs, Lambda Data Events and Billing Data from every AWS account you connect. This part of the platform is isolated in its own account for security and has read-only access to the accounts you connect.
- Populate the Stream: All of the data collected is normalized and the events, resources, statistics and billing data are organized into data streams which allow our platform to perform real-time analytics on all the data collected.
- Find Meaning: Our algorithms take in the normalized data and perform complex analytics sifting through all the data to filter noise and enhance signal. We use Machine Learning on a large scale to learn what is valuable to surface.
- Visualize Everything: The application provides opinionated visualizations of the insights determined by the platform’s AI. From regional system maps to single resources to cost of service broken down by team, CloudZero’s platform provides true observability to everyone in your organization.
Observability for Everyone. Add cost as a first-class metric and understand the financial effect of operational decisions.