Operations | Monitoring | ITSM | DevOps | Cloud

Cost Optimization for AI Workloads: From Visibility to Control

ITOps teams can achieve cost management of AI workloads with an observability platform that connects AI usage and performance with cloud spend for clear visibility and predictability. Behind the buzz around artificial intelligence, or AI, many companies are discovering the hidden and compounding costs of AI adoption.

How LogicMonitor Delivers AI Cost Optimization

LogicMonitor delivers AI cost optimization by unifying infrastructure telemetry, AI-specific signals, and cloud financial data into a single workflow, so teams can move from visibility to continuous, operationalized cost control. In Cost Optimization for AI Workloads: From Visibility to Control, we explored why AI workloads introduce new layers of cost complexity—from GPU-heavy compute and token-based pricing to distributed infrastructure that obscures true spend.

Is Generative AI Eroding Our Ability to Think?

In aviation, there's a well-documented issue known as "automation addiction." As cockpit systems became more advanced, pilots gradually shifted from actively flying aircraft to supervising automated controls. Everything worked smoothly-until a system malfunctioned. Investigations revealed a troubling pattern: even experienced pilots sometimes struggled with basic manual maneuvers. Their hands remembered less because their brains had practiced less.

Should You Use AI for Business Contracts?

AI is creeping into almost every corner of business life. It drafts emails, builds presentations, analyses data, and even creates marketing campaigns, So, it is hardly surprising that some companies have started using it to draft business contracts too. At first glance, this might sound like an efficient and sensible use of resources. Faster turnaround. Lower cost. Instant templates. But when it comes to legal agreements, speed and convenience are not always the priority.

AI-Driven Automated Testing for Oracle Applications

As enterprises continue to change rapidly, businesses depend on Oracle-based ecosystems to track their finances, supply chains, HR, and customer operations. With the increase of digital transformation in companies, these environments continue to become more complex. As a result, manual testing is no longer enough for maintaining pace with ongoing updates, integrations and customizations that occur within an organization's systems. This is where AI-powered automated testing for Oracle applications revolutionizes how quality assurance is approached.

The AI infrastructure gap: why agents fail on fragmented stacks

The initial hype of AI agents is hitting a hard reality: a clever prompt is not a production strategy. As organizations move from experimentation to operationalizing AI in 2026, a systemic bottleneck has emerged: It is not the model's intelligence; it is the model’s context and its access to the right tools. When an AI agent lacks access to live, grounded platform data, it guesses.

Use AI to turn any JSON API into a dashboard in minutes with the Infinity data source plugin and Grafana Assistant

The internet is full of fascinating data just waiting to be visualized and queried. And with the latest update to Grafana Cloud, you can start doing it in minutes. Through public APIs, you can access information about global earthquake activity, weather forecasts, music catalogs, and millions of other datasets. And then there's all the data that sits inside company APIs, partner services, and internal platforms that power everyday products and operations.