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Investors and enterprises are finally asking the question they'd been avoiding: which software companies will survive the AI revolution, and which will be made obsolete by it? The answer is becoming clear. Companies that serve as the system of record, the authoritative source of truth that AI itself depends on, are essential.

Autonomous IT only works when observability gives it the context to act with confidence. On any given day, a mid-size enterprise generates tens of thousands of alerts across on-prem infrastructure, multiple clouds, SaaS tools, Internet dependencies, and AI workloads. Most of them don’t need a human. A few of them do. Telling the difference, fast enough to matter, is exactly where IT teams are losing ground.

Cloud rightsizing matches resources to actual workload demand. Learn to find overprovisioned instances, cut waste, and automate savings.

You’ve masked your database. You’re confident you’ve covered all your PII. But not all of it lives where you’d expect it to. AI Classification, Redgate's data scanning capability, automatically catches sensitive data before it reaches your developers, your test environments, or your auditors.

If you're running Databricks workloads, you've probably asked yourself these types of questions: How much is this costing me? Why did that job fail last night? Why are my dashboard queries suddenly slow? We've been there, too. Databricks is fantastic for data engineering, ML, and analytics. But once you start running jobs, pipelines, and SQL queries at scale, you need a way to keep tabs on what's happening. That's why we built the Databricks integration for Grafana Cloud.

Modern site reliability engineering challenges stem from the difficult requirement of confirming why complex systems fail in ways staging cannot replicate. While observability tools signal failures, and AI SREs reason over data, they leave observability gaps regarding the actual state of running code. By utilizing runtime context, teams capture live execution data to accelerate production debugging, resolving incidents in minutes without requiring manual redeploy cycles.

“Garbage in, garbage out.” Everyone who has ever worked in computing or other data-adjacent fields has heard this adage at least once. This phrase or acronym (GIGO) reflects the fundamental concept in both computing and data governance that the quality of your data is the critical determinant of successful results in any system, regardless of whether your focus is IT or OT.

If you’re running Netdata collectors that connect to databases, APIs, or other authenticated services, there’s a good chance you have passwords sitting in plain-text configuration files right now. It works, but it’s the kind of thing that makes security teams nervous and makes credential rotation painful. Every password change means editing config files and restarting collectors.

Affordable hosting has always been a puzzle. Heroku made deploying Rails apps simple, but with Salesforce at the helm, active development has stalled. Many developers are left wondering what comes next, locked into a platform that is no longer moving forward. Chris, the founder of GoRails, felt that same frustration. That is why he built Hatchbox. Hatchbox handles your deployments, runs on servers you own, and keeps costs predictable. No dyno management, no add-on sprawl.