Operations | Monitoring | ITSM | DevOps | Cloud

Kinesis is an Amazon Web Services (AWS) product that collects, processes, and analyzes streaming data in real-time. It can process streaming video, audio, IoT data, application logs, and other data as it arrives from thousands of unique sources, unlike technologies like Hadoop, which utilize batch processing (waiting for a complete dataset to arrive before processing and analyzing it).

Most SaaS companies aren’t spending too much on AI. They’re just completely screwing up how they price it. You feel the budget pressure. The OpenAI and Anthropic bills keep climbing. Finance is starting to twitch. So the instinct is to cut. Trim back experiments. Cap usage. Beg your team to “optimize.” You can’t cost-cut your way out of a pricing failure though. And most of the time, that’s all this is — a pricing failure.

When you’re a global leader in AI-assisted hiring, speed matters. Not just in matching candidates to jobs, but in making the engineering and financial decisions that keep your platform running efficiently. For HireVue, fragmented infrastructure, manual processes, and sprawling spreadsheets turned cloud cost management into a time-consuming spelunking expedition.

Although container-related technology existed before 2013, Docker revolutionized and propelled it into the mainstream. Using Docker, developers could automatically create containers from application source code, share libraries, and reuse containers. Docker enables you to track container image versions, roll back to an earlier iteration, and track who built a specific one. You can even upload only the deltas between two versions.

If you want to align green awareness with bottom-line impact, start by looking at your cloud waste. Not just as a budget problem, but also as wasted energy, because that’s exactly what it is. AI, especially, is a mounting factor. Deloitte’s Tech Trends 2025 report highlights the growing energy demands of large AI models, warning that electricity use in data centers could soon rival that of entire nations like Sweden or Germany.

July brought good news, if you’re in the mood for some fresh new opportunities to learn about cost optimization and streamline your FinOps strategy. Check out these updates, hot off the press: Enjoy!

If you’ve ever stared at your cloud bill and thought, “How on earth did this get so crazy?” — you’re not alone. Especially when AI workloads come into play, those GPU costs can feel like a runaway train. The good news? It doesn’t have to be that way. The magic happens when you’ve got someone from every team that cares about smart growth (FinOps, AI/ML, product, engineering, whatever) all in one room, looking at the same set of numbers.

At CloudZero’s most recent company retreat, we held an investor panel, where representatives from four of the VC firms investing in CloudZero fielded questions from our team. Unsurprisingly, a good deal of the conversation revolved around AI. A standout moment from this panel came when one investor described a vibe coding session he’d done about a month prior. “Vibe coding,” for the uninitiated, means using AI to build an application without writing any actual code yourself.

Cloud services have become an indispensable resource for businesses seeking agility, scalability, and innovation. However, with this increased reliance on the cloud comes the challenge of managing and optimizing costs effectively. For Chief Financial Officers (CFOs), understanding and tracking cloud cost metrics is crucial to maintaining financial health and ensuring strategic investments yield the desired returns.

When cloud costs spike, compute is often the culprit. Using Azure Spot Instances could cut your compute costs by up to 90%. But Spot VMs come with trade-offs, including unpredictable evictions and capacity constraints. And that makes them tricky to use without the right strategy and visibility. In this guide, we will share how to make them work for you.