Compare cloud GPU pricing across AWS, Azure, and GCP for AI workloads. See H100 and A100 costs per hour, hidden cost drivers, and how to track real GPU spend.
We look at the hidden economics of elastic scaling for AI inference, the scaling decisions that affect your cost per inference, and what you can do now to optimize your AI ROI.
The faster you ship with AI, the wider your test coverage gaps get. Chunk scans your codebase, finds what's untested, writes the tests, and opens a PR.
AI adoption is at an all-time high, withover 70 percent of organizations are using AI in at least one core function. Despite the high rate of AI adoption, many operational teams continue to have difficulty answering the question 'Is AI actually benefiting our business?' The challenge lies in the gap between AI systems and actual business results. Bridging the gap requires aligning operational AI with revenues, customers, and growth metrics. Here are actionable steps to transform AI from a technical tool into a measurable business contributor.
Modern businesses are under constant pressure to move faster, reduce costs, and stay compliant in a shifting regulatory landscape. Financial operations sit at the center of that pressure. Tasks like invoicing, reconciliation, reporting, and forecasting have traditionally required heavy manual effort. That is starting to change. Autonomous technologies are stepping in to handle routine processes, reduce errors, and free teams to focus on higher value work.
At PagerDuty, we believe operational excellence and social impact are inseparable. As AI rapidly transforms how nonprofits operate, our AI and agentic technology empower mission-driven teams to automate complexity and focus their limited resources on what matters most: delivering reliable services that create meaningful impact at scale.
I've been building a multi-agent research system. The idea is simple: give it a controversial technical topic like "Should we rewrite our Python backend in Rust?", and three agents work on it. An Advocate argues for it, a Skeptic argues against, and a Synthesizer reads both briefs blind and produces a balanced analysis. Each agent has its own model, its own tools, its own system prompt. It worked great in testing. Then I noticed the Synthesizer kept producing analyses that leaned heavily toward one side.