Operations | Monitoring | ITSM | DevOps | Cloud

Some teams won’t touch DeepSeek because it’s Chinese. Others are quietly running pilots and rethinking how much reasoning and context they actually need, or can afford. For SaaS teams staring down runaway AI costs, DeepSeek’s mix of open-source freedom, massive context windows, and token rates 10–30X cheaper than OpenAI or Anthropic is tough to ignore. However, DeepSeek pricing comes with cache hits, cache misses, off-peak discounts, that September pricing shift, and more.

Anthropic’s Claude is one of the most powerful and developer-friendly large language models (LLMs) available. But as usage grows, so does cost. Here’s the reality: A single unoptimized development loop or unmonitored QA job can multiply costs 10x overnight. Most teams experimenting with Claude lack the visibility and guardrails needed to prevent runaway costs, especially once usage moves from R&D into production.

Organizations face increasingly complex cloud environments — from hybrid clouds to multi-cloud deployments — where costs can quickly spiral without real-time visibility and intelligent controls. This is why setting clear goals for cloud cost optimization is necessary to keep your organization proactive. The key to success lies not just in setting goals, however, but in ensuring those goals are clear, realistic, and supported by continuous measurement and actionable insights.

When OpenAI surged into the spotlight with ChatGPT, not everyone inside the company agreed on the path forward. In 2021, a group of senior researchers broke away. They had concerns about safety, transparency, and the direction of AI development. They went on to found Anthropic. And their answer to ChatGPT was Claude. Anthropic’s mission is for openness now. Yet, Claude’s pricing can feel as mysterious as the model weights behind the scenes.

Cloud costs keep rising, and most companies are struggling to contain it. That’s where today’s finance teams can step up their game, not only as a professional opportunity but as a leading protagonist on the cloud cost optimization stage. A bit of background first: Global public cloud spending is projected by Gartner to exceed $720 billion in 2025. That’s up from nearly $600 billion in 2024. And a lot of that is sheer, unmitigated waste.

With 240 distinct services, ranging from compute to storage to networking and content delivery — each offered at different price points — choosing the right AWS service requires meticulous consideration.. By default, AWS services are available on-demand and you pay a monthly bill for services used. However, the on-demand pricing model can get expensive if you use a lot of services and deploy a fleet of instances.

Measuring and monitoring unit economics can help your SaaS brand make informed business and engineering decisions. But how do you get that data, and what exactly are SaaS unit economics? We’ll cover exactly what SaaS unit economics are, metrics you should monitor, how to calculate your unit economics, and the tools you can use to be successful.

I’ve seen firsthand how persona-centric FinOps creates realized savings through synergy. I’m a Certified AWS Solutions Architect, FinOps Engineer, and Customer Success leader who’s had the joy of turning cloud confusion into clarity. I’ve added a customer story below — but hold up, we’ve got onboarding optimizing to do.

Containers and microservices are revolutionizing how distributed applications are built, run, and optimized. They enable apps to be highly scalable. You can also isolate some areas for updates and patches without shutting down the entire application or service. Yet, managing containers and microservices at scale can be tricky. That’s where a container management platform like Kubernetes comes in – or, as you’ll see below, where the top Kubernetes alternatives shine.

Two years ago, a “state-of-the-art” AI model could write decent copy or summarize a meeting transcript. Today, the top AI models can generate working code, analyze video in real time, and reason through complex scenarios. For SaaS teams, these changes represent a strategic crossroads. Choose the right model and you unlock new revenue streams, slash time-to-market, and wow your users.

One of the biggest benefits of the cloud is that it gives engineering teams the freedom to deploy and iterate applications quickly. Unlike traditional IT environments where engineers require a series of approvals before embarking on projects, in the cloud, engineers can choose from several managed services and deploy them at the click of a button. This means your team can innovate faster and respond quickly to market demands.

AI is surging into products. And the invoices are exploding with it. The key question is no longer, “How much did we spend?” It’s now: “Was it worth it?” That shift, from totals to value, is at the heart of FinOps. The FinOps community defines the practice as bringing financial accountability to the cloud, so teams make tradeoffs with clear business context. In plain English, measure value per dollar, then optimize the system and not just the bill.

Amazon SageMaker makes it easy to prepare data for machine learning (ML) and then train, deploy, and modify ML models. SageMaker is a fully managed service that automates much of the ML lifecycle. So, if you want a single partner to help you through all stages of your Artificial Intelligence (AI) lifecycle, SageMaker might be the answer. Perhaps more important for this post is the promise that Amazon SageMaker can reduce your machine learning model costs. But does SageMaker pricing reflect this?

AI adoption isn’t just accelerating, it’s compounding. From GPT-5 to Claude to Llama and beyond, engineering teams are integrating diverse LLMs across products, experiments, and services. And finance teams are now grappling with a new kind of cloud complexity: token-based economics and volatile inference costs, often spread across multi-model, multi-cloud, and multi-region architectures. The modern FinOps stack needs to keep up. CloudZero was built for this moment.

I’ve spent most of my career building and optimizing cloud, on-prem, and data platforms for growing companies. It’s been an amazing journey so far. Through it all, FinOps has become more than just a methodology for me (Fred FinOps didn’t just come from my love of the Flintstones, though I do appreciate a good cartoon). It’s a community, a discipline, a tribe I’ve come to call home. Lately, some tough questions have kept me up at night: These challenges got me thinking.

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.