Operations | Monitoring | ITSM | DevOps | Cloud

If you are running containers on AWS, you need a secure place to store and share your images. Amazon ECR offers a managed registry that handles image storage, scanning, permissions, and versioning without extra configurations. In this guide, you’ll learn what Amazon ECR is, how it works, its features, real-world benefits, and pricing. We will also introduce you to a cost intelligence approach to keeping ECR costs under control.

Software engineers use engineering performance metrics to make informed decisions about their products, features, processes, and even their dev teams. In addition, measuring lets you know if you’re on track to meet your engineering goals. With so many tasks, data, and other information to monitor, how do you choose the right metrics to track? We’ll share that and more in this guide.

Choosing the right tool to manage your infrastructure can shape how fast your team moves and how reliable your systems become. Two names appear in almost every conversation: Ansible and Terraform. Both help you define, manage, and scale your environment. But they solve different problems and work in very different ways. One focuses on configuration. The other focuses on provisioning. Both are powerful. Both are widely used. And both can work together in the right stack.

Virtual machines often represent the largest line item in a cloud bill. And for Google Cloud users, the Google Compute Engine (GCE) accounts for a large share of overall spend. GCE offers rich flexibility: you can choose specific machine types, scale up or down instantly, and match compute to load. But understanding how the pricing works is critical before you can unlock full value. On the surface, GCE looks simple. You pay for vCPU, memory, storage, and network.

If you’re running a SaaS business today, you’ve probably noticed the alarms never really stop. Logs. Alerts. Tickets. They pile up faster than many teams can triage them. Add multiple clouds, microservices, and AI-driven workloads, and suddenly, your “always-on” infrastructure feels like it’s always on fire. AIOps platforms promise to connect dots that human teams struggle to see fast enough. For engineers, these include surfacing root causes and outwitting outages.

Cloud and AI spending is exploding, and every dollar counts. As companies race to innovate, they also face growing pressure to prove that their cloud investments are delivering real business value. That’s why CloudZero pioneered the Cloud Efficiency Rate (CER) metric, a unifying metric for quantifying cloud business value.

“Every engineering decision is a cost decision,” notes Ben Johnson, co-founder and CTO of Obsidian Security. That’s the reality of building modern SaaS products in the cloud. But as Ben points out, the answer isn’t to make engineers think long and hard about every dollar they spend. “You don’t want your team hesitating to solve risky technical problems because a choice might add $100 to the bill.

Security used to be something teams added at the end of a release cycle. Engineering pushed code fast. Security teams reviewed it later. But this flow only worked when the software moved slowly. Modern cloud environments broke the old security model. Containers, microservices, APIs, and infrastructure as code now change too fast for security to sit outside delivery workflows.

Every time I step on set with a guest for FinOps In Full Bloom, I’m anticipating the lightbulb moments I know will pop up during the podcast. These are the conversations that reveal how curiosity and collaboration can spark real transformation in the cloud.

In the cloud computing industry, we hear the word “scaling” a lot. We talk about scaling up resources to meet demand, scaling our teams, and scaling our platforms. What tends to get lost is whether your costs are scaling in proportion to the value you’re delivering. If those two metrics don’t move in tandem, it’s likely you’re leaving money on the table. It’s not enough to simply use the cloud.

If you’ve already worked through cloud cost optimization strategies, the fundamentals aren’t new. CloudZero’s State of Cloud Cost report shows that cloud cost optimization is now a priority for most organizations. We’ve also covered these foundations in depth, including how cloud cost optimization works in practice and how FinOps teams approach cost accountability. What’s less discussed is what happens next. Cloud environments don’t stand still. Architectures change.

FinOps teams have been expected to understand millions of dollars in cloud and AI spend using tools that a handful of (usually technical) specialists can operate. Dashboards, filters, exports, and SQL have been the norm. That era is over. CloudZero is now bringing AI directly into the FinOps workflow so anyone in the business can ask natural-language questions about cloud and AI spend, and get accurate answers back from the platform.

Imagine this. A product team rolls out a change to improve reliability. The deployment succeeds. Traffic grows. Weeks later, cloud costs increase, and the finance team asks what changed. No one can point to a single decision or owner. This situation is common in cloud environments. Infrastructure scales automatically, and costs are shaped by technical choices made across engineering, data, and product teams. Most organizations review cloud spending after it has already occurred. Ownership is unclear.

This year’s AWS re:Invent was a blur of GPUs, LLMs, and infrastructure roadmap reveals — but for those listening between the keynotes, another story was unfolding. Between hallway chats, booth conversations, and live polls, a signal emerged from the noise: FinOps is growing up. Mature cloud teams aren’t just managing costs — they’re asking smarter, more strategic questions about value, forecasting, and engineering accountability.

As an engineer, engineering leader, or CTO, your architectural choices shape how fast your team builds products and how efficiently you manage technology costs. Your architecture determines how much control you have over data, infrastructure, and customization. The Software-as-a-Service (SaaS) model is one of the most common ways to deliver software reliably to users anywhere.

On a recent episode of FinOps In Full Bloom, host Thalia Elie sat down with Anderson Oliveira, a Senior FinOps Account Manager at CloudZero. With more than two decades in IT and deep FinOps expertise, Anderson brought clarity, humor, and a refreshingly human perspective to the conversation. Their chat covered everything from visibility and budgets to cultural friction and how to shift teams from resistance to results. Here are 13 insights and takeaways every FinOps-minded leader should hear.

In our increasingly online world, managing cloud, AI, and other tech spend has shifted from a good idea to an absolute necessity. But even when cost management is a priority, how do you get busy development teams and engineers actively engaged in the new practices? New initiatives are often viewed as more work on the team’s plate, which is an understandable deterrent to adoption. That leaves FinOps proponents struggling to get others on board.

AI should speed up innovation, not inflate your cloud bill. But today, the biggest GenAI challenge for SaaS teams isn’t model quality; it’s cost. And increasingly, that cost comes from AI cost sprawl. That’s not because anyone is doing something wrong, but because AI operates differently from the cloud services we’ve all spent a decade learning how to manage.

When a SaaS company scales, something curious happens. The cloud bill grows. One team swears it’s Kubernetes. Another blames the Black Friday promo. But when you’re unsure whether that increase is tied to healthy SaaS growth or simply overspending, your margins are already at risk. That gap between what’s spent and what’s understood is where indirect costs live. Yet these costs rarely show up in dashboards. Well, until it’s too late.

Welcome to December’s edition of CloudZero’s Cloud Economics Pulse — your monthly read on how cloud spend is shifting across providers, services, and AI workloads. No surprises here — November continued the quiet reshaping trend we’ve seen all year. Compute softened, data layers grew, and AI/ML hit its highest share yet. AWS extended its lead, Azure and GCP nudged upward, and the emerging “AI layer” of providers continued to take shape.

A few months back, a backend team at a fast-growing SaaS company shipped what seemed like a harmless feature. Just a simple request validation layer. No new service. No major dependencies. No architectural shock. Yet two months later, their cloud costs had climbed 38% without any significant increase in traffic, storage, or compute load. What they’d missed was that the validation layer triggered a fan-out pattern.

New Relic provides full-stack observability and monitoring. It provides almost every type of system monitoring on a single platform. This includes monitoring tools for infrastructure, application performance monitoring (APM), synthetics, user, log, mobile, network, and Kubernetes components. DevOps, security, and business professionals use these capabilities to detect anomalies, analyze root causes, and fix software performance issues.

AI adoption is exploding, but margins aren’t. In fact, an MIT analysis reports that 95% of organizations have yet to see measurable ROI from GenAI. This gap becomes obvious as soon as teams push a model into production and usage begins to scale. For most workloads, the pressure comes after training. Every message, call, query, completion, or retrieval triggers compute behind the scenes. That real-time execution is what AI inference is all about.

Machine learning pipelines are getting heavier by the day. From model training to large-scale inference and data preprocessing, compute demands are scaling faster than teams can manage. Kubernetes clusters groan under unpredictable job spikes. Static infrastructure wastes money when workloads slow down. The result? Organizations are perpetually chasing flexibility, automation, and cost efficiency. AWS has quietly built a solution to establish that balance.

Ask a SaaS team how they measure cloud efficiency, and you’ll hear familiar things. Total cloud spend. Average cost per customer. Maybe a breakdown of spend by service. All useful, but rather wobbly. Now ask, “What does it cost you to serve one more customer?” That’s when the room goes quiet. And that’s often where cloud economics gets really wobbly. Because that number, your marginal cost, is what actually determines your margins. Not your total cloud bill.

The AI landscape today feels a lot like the early days of the cloud: exciting, fast-moving, and completely fragmented. Every week, engineering teams are experimenting with dozens of large language models (LLMs) from providers like OpenAI, Anthropic, Google, Mistral, Meta, and beyond. They’re tweaking prompts, testing model performance, swapping context windows, and even running multiple models in parallel to figure out which one works best for each unique use case.

Happy Holidays, and happy almost new year from us at CloudZero! November is always a busy time! In case you missed the headlines, here’s what happened last month in relation to cloud and AI costs: Get the details below!

One year ago, at AWS re:Invent, we launched CloudZero Advisor, a free, standalone AI assistant that enables anyone to ask questions about cloud spend in plain language. It was the first experiment of its kind in FinOps, a chance to see what people really wanted to know when cost data finally became conversational. Over the past year, Advisor has become a learning engine.