Operations | Monitoring | ITSM | DevOps | Cloud

When I was working at Shopify, Black Friday and Cyber Monday were our Superbowl. We initiated code-freeze weeks before to make sure merchants wouldn't have any unexpected issues during one of the most important times of the year. Sometimes, though, you need to ship updates last minute. Picture this: It's Black Friday Eve, 11:47 PM. You've just deployed a new /sale page with 50+ products at discounted prices. Marketing is about to email 500,000 subscribers. Everything tested fine with your sample data.

When you’re debugging a tricky issue in a distributed system, “what changed?” is often the most important question. You add logs, you capture data, you redeploy, and suddenly your browser is full of open tabs, copied JSON blobs, and screenshots of log lines. Comparing behavior between two requests, two users, or two releases turns into a manual, error-prone chore. Lightrun Snapshots were built to fix the data collection side of that story.

At Coralogix, we’re constantly looking to evolve the measurements we take to better understand the efficiency of our infrastructure. We constantly assess and investigate sources of cost in our cloud infrastructure, to ensure we’re getting the best return on investment. This activity, often referred to as FinOps, is becoming a cornerstone of engineering teams.

The energy at Microsoft Ignite this year was electric. AI was everywhere, and the possibilities are limitless. As developers and operations teams explore what AI can do, one thing became clear: the future isn’t about switching between tools. It’s about intelligent agents working together to help humans solve problems faster. At PagerDuty, we’re building on that excitement.

Open source software is known for its ability to lower IT costs. But in 2025, affordability is only part of the story. A new Linux Foundation report, The strategic evolution of open source, reveals that open source has evolved from a tactical cost-saving measure to a mission-critical infrastructure supporting enterprise-grade investments, and delivering stronger business outcomes as a result.

You don’t have to choose between capturing every signal and keeping costs predictable. Modern observability stacks blend full-fidelity storage (time series or columnar systems like ClickHouse and Apache Druid), tail-based sampling for heavy traffic, and tiered storage (hot/warm/cold with S3-backed archives). This gives you full-fidelity incident forensics with the day-to-day cost profile of a sampled setup.

This year’s KubeCon underscored a real shift: AI SRE has gone mainstream. Of course, it’s not a surprise. Teams from high-growth startups to Fortune 500s are running more complex, cloud-native systems, shipping more AI-generated code, and facing rising expectations. Downtime is absolutely not an option and the work for on-call SREs has become unsustainable. The question isn’t whether AI SRE helps. It’s which one you can trust in production.

As the year winds down, my inbox is overflowing with Black Friday offers and festive greetings. It’s that time when Thanksgiving and the run-up to December holidays remind us to pause and appreciate what truly matters. Yet, in my recent conversations with IT leaders in financial services, I’ve noticed something: the time and calm need to do this feels elusive.