Operations | Monitoring | ITSM | DevOps | Cloud

If your SaaS product handles 1 million chatbot interactions per month, Amazon Lex alone could cost between $4,000 and $7,500. That range assumes current Amazon Lex V2 pricing of about $0.00075 per text request and $0.004 per speech request. Multiply the requests by the rate, and you’re done. Or are you? Conversational AI services rarely behave that neatly in production — and that includes AWS Lex. Amazon Lex is AWS’s conversational AI service for building chatbots.

In 2025, many teams built strong FinOps foundations: These practices created visibility and control. Now it’s time to elevate. FinOps in Action is a three-part series focused on applying that foundation in real engineering scenarios. Each post highlights a different persona and shows how to move from visibility to operational discipline. Today, we focus on Engineering. Engineering teams influence cost through architecture decisions, scaling policies, and workload design.

I’ve been staring at Claude Code telemetry for the past few weeks, and I keep noticing the same thing: most teams drop it into their environment, say “it’s amazing,” and have absolutely no idea what it’s actually doing at the system level. That’s fine for a personal dev tool. It’s not fine when you’ve rolled it out to 50 engineers.

Your apps are slow. Users are complaining. You're staring at a dashboard trying to figure out what broke and when. Sound familiar? This is the reality of reactive network monitoring. By the time someone opens a ticket, the issue has already been affecting performance for minutes, sometimes hours. A network health check flips that script. Instead of chasing problems after the fact, you're catching them before users ever notice.

In the modern cloud infrastructure landscape, we don’t have a data problem; we have an actionable interpretation gap. Engineering teams are often drowning in metrics that describe a crisis without providing a clear path to remediation. Traditional FinOps, SRE, and DevOps work has become a reactive loop of dashboard-watching and manual firefighting.

Session Replay. Structured logs. AI monitoring. Automatic OpenTelemetry tracing. Feature flag tracking. If you haven't seen these in your Sentry dashboard, your SDK version is probably the reason. Whether you're on @sentry/react, @sentry/nextjs, @sentry/vue, @sentry/angular, @sentry/sveltekit, or any other @sentry/* package, they all version together. When we say v10, we mean all of them.

For more than a decade, organizations invested in tools and telemetry with the belief that more visibility would create more control. Monitoring expanded across cloud, application, network, and infrastructure layers. Observability platforms entered the mainstream. Automation tools promised faster detection and improved coordination. Yet despite these advancements, incidents are not easier to understand. War rooms still fill with conflicting interpretations. Signals generate more questions than answers.

This is a guest post from Kellyn Gorman. Kellyn Gorman is a Database and AI Advocate and Engineer at Redgate She's the previous director of Data and AI at Silk, and the Oracle SME in Azure at Microsoft. With a robust background in cloud technology and a passion for promoting its merits and potential, I am thrilled to spearhead conversations and actions that help shape the future of this industry.

Your first NOG meeting doesn't have to be daunting. Here's what to expect, how to prepare, and how to make the most of every session and conversation. Co-authored by Rob Parker and Gavin Tweedie If you’re trying to optimize your organization’s peering and Internet Exchange (IX) traffic engineering as your network grows, you may be searching for more ways to improve your network or customer experience.

Every year, NVIDIA GTC offers a glimpse into the future of computing. But this year felt different. The conversations from the past few days point to something bigger than faster GPUs or larger models. The industry is shifting its mindset entirely. GTC 2026 made it clear that the goalposts for AI haven't just moved, they’ve been uprooted. We’re past the point of talking about "faster chips." Everything points to a total shift in the industry's DNA.