Operations | Monitoring | ITSM | DevOps | Cloud

The cloud, as we know it today, was created as recently as 2006. For most of its lifespan since then, companies have been throwing money at cloud services with abandon. The competitive edge gained by having the newest, best, and most powerful tools at their disposal made it worthwhile for companies to spend ever-increasing amounts without too much worry.

The past few years have seen the incredible rise of cloud-native AI start-ups, many of them born during the pandemic. These companies emerged agile, experimental, and ready to scale. But as their ambitions grow and their AI models become more complex, they face a critical crossroads: how to manage infrastructure sustainably while continuing to innovate at speed. In the early days, public cloud services were the obvious choice.

Microsoft Azure Data Factory is a cloud-based data integration service provided by Microsoft Azure. It enables you to create, manage, and automate data workflows that move and transform data from different sources to various destinations. Essentially, ADF allows you to design, orchestrate, and manage data pipelines, making it easier to work with large volumes of data across on-premises and cloud environments.

If your team is experimenting with Google’s latest Gemini models, you’ve probably noticed the pricing can get murky. And like any cloud service, usage-based billing means those features can quickly rack up your SaaS costs. Free tiers fade fast. Token usage gets fuzzy. And before long, your GenAI bill feels more like a guessing game than a budget line item.

Master Azure infrastructure management with this comprehensive guide. Learn the four critical pillars—governance, cost control, security, and operations—and discover the essential native and third-party tools needed to scale your cloud strategy effectively.

SSL certificates keep websites and apps secure, but in AWS, Azure, and Google Cloud Platform (GCP), misconfigurations or expirations can still cause services to go offline. Why do these failures happen, and how can you prevent them?

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.

Good news – Early Warning Signals are now available in Google Chat! This means you’ll get real-time alerts about possible outages—before providers officially acknowledge them—delivered right where your team is already chatting and collaborating.

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.