Operations | Monitoring | ITSM | DevOps | Cloud

Today, organizations looking to build AI products and services using large language models (LLMs), agentic AI, and generative AI often start by investing in artificial intelligence as a service (AIaaS), also known as cloud AI. AIaaS provides a scalable, flexible, and cost-effective way for businesses of all sizes to access advanced AI technologies without the need for extensive in-house expertise or infrastructure.

An email server gets blacklisted when it's identified as a potential source of spam, malware, or suspicious activity. Blacklists use automated systems and user reports to flag servers that violate mailing or security standards. Once listed, legitimate messages may bounce, land in spam folders, or never reach recipients at all. Understanding why this happens is essential to prevent future listings and protect the sender's reputation.

For a very long time now, IT leaders have relied on the “throw more bodies at it” strategy: when ticket volumes rise, headcount follows. That model no longer works. Hybrid work, SaaS sprawl, and cloud complexity have made human-only scaling unsustainable. The enterprises winning today aren’t scaling with headcount. They’re scaling with autonomous ITSM agents: AI-driven specialists that resolve tickets instantly, escalate only when needed, and keep operations running 24/7.

AI in networking isn’t theoretical anymore. It’s here, reshaping how we operate. At the ONUG AI Networking Summit, we saw firsthand how agent systems are moving from hype to hands-on reality, from secure automation to data-driven root cause analysis. The future of NetOps isn’t dashboards and tickets — it’s intelligent agents, observability, and measurable business outcomes.

If you're anything like your peers, you probably blinked in April and found yourself a month away from Black Friday when you opened your eyes. Much like a shopper desperately scrambling to pull together gift lists for their loved ones, many engineering teams find themselves rushing to ensure their systems can handle the biggest shopping day of the year.

The speed and reliability of deploying backend and full-stack applications are usually a concern for development teams. Fortunately, Railway is a developer-friendly platform that allows you to deploy apps with limited configuration. It is also quick, easy to use, and has reasonable defaults. Now, imagine pairing that with CircleCI, one of the strongest continuous integration platforms available.

In part one of this series, we learned how to implement batch updates and advanced inserts in Ecto to dramatically improve database performance. But implementing these optimizations is only the first step. Ensuring they continue to work effectively in production requires professional monitoring and observability. This guide will show you how to use AppSignal for Elixir to monitor your Ecto application's performance when dealing with batch data operations.

At 2:07 a.m., one of the core production nodes went down. CPU usage spiked, latency shot through the roof, and requests began timing out across the cluster. Monitoring tools lit up instantly. Datadog dashboards turned red, Prometheus fired alerts, and a webhook pushed incident payloads into Jira. Everything worked exactly as designed. Except no one responded. The alert chain fired flawlessly through machines, but the right human never saw it because it was sent via an automated phone call.

SaaS, AI, and technology spending today looks like a more intense version of how it was a decade ago when everyone first migrated to the cloud. The mentality was and, at some companies, still is to build build build and worry about controlling expenditures and optimizing costs later. We’re seeing astronomical amounts of money being raised by brand-new AI businesses that barely even existed a couple of years ago. More than $145 billion has been raised for U.S.

Every finance team knows this pain. The cloud bill continues to grow, but the numbers don’t quite add up. Engineering swears their usage reports are accurate. But when you ask, “Which product or customer drove last month’s 18% cost increase?” things go quiet. That silence usually means one thing. Inaccurate cost allocation. Native cloud cost allocation tools from AWS, Azure, and Google Cloud can help. However, they often stop at the average this or average that layer.