Operations | Monitoring | ITSM | DevOps | Cloud

Explore IaaS, PaaS, and SaaS cloud models to accelerate your shipping. This guide defines each, compares their benefits and drawbacks, and introduces a modern approach to get IaaS power with PaaS simplicity.

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?

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).

Applying DORA principles to improve software delivery and operational performance with Google Cloud We’re thrilled to announce that Elastic has been honored with the 2025 Google Cloud DORA Award for Architecting for the Future with AI. Google Cloud DORA awards recognize organizations that have demonstrated significant advancements by applying DORA principles to improve their software delivery and operational performance with Google Cloud.

I’ve previously covered how eG Innovations AIOps-powered monitoring benefits those working with Digital Workspaces or leveraging APM; today, I’ll cover how those same AI-powered capabilities benefit those supporting cloud hosted architectures and workloads.

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.

Serverless platforms like Azure Functions and Azure Container Apps make it easier to scale your applications without managing infrastructure. But successful serverless apps require thoughtful planning. They must be designed to account for cold starts, unpredictable scaling behavior, and ephemeral compute lifecycles, all while ensuring secure data handling and end-to-end observability across highly distributed components.