Best practices for modern infrastructure, cloud platforms, and DevOps processes.
26 articles, frameworks, and resources
The Cloudflare crash on November 18th, coupled with recent AWS and Azure outages, reveals an uncomfortable truth: our industry's consolidation has created catastrophic single points of failure at scale. It's time for CTOs to rethink resiliency architecture.
Track your infrastructure costs normalized by active users. Essential for understanding unit economics and scaling efficiently.
Track how often your team deploys to production. A key DORA metric that indicates your team's ability to deliver value continuously.
Track the percentage of failed requests. Critical for reliability, user experience, and incident detection.
Strategic cloud provider comparison. Cost, services, hiring, enterprise features, and when to choose AWS, GCP, or Azure for your business.
Strategic comparison of microservices and monolithic architectures. Team size, complexity, deployment, costs, and when to choose each approach.
AI is moving from model selection to compute-and-agents as the primary architectural and business constraint. CTOs are being pushed to treat AI infrastructure—chips, data centers, multicloud networking, and agent platforms—as a strategic system, not a commodity.
CTOs are being pulled into a new operating model where AI (especially agents) accelerates change, while resilience and cost-aware observability become the gating factors for safely scaling that change.
Observability is shifting from a DevOps toolchain add-on to a consolidated, data-centric platform capability—driven by cost pressure, M&A, and the operational complexity introduced by AI and multicloud
The pattern this week
The center of gravity is moving from building AI features to operating AI systems: agent frameworks, evaluation metrics, and observability are converging into an ‘AI operations stack’ that CTOs must standardize now.
Agentic AI is moving from copilots to production control loops: vendors are pitching autonomous SRE and AI-native observability, investors are backing closed-loop remediation platforms, and boards are hiring AI-focused CTOs to operationalize these capabilities.
AI is becoming an operational discipline: regulation is pushing formal safety disclosure and fast incident reporting while the engineering toolchain shifts toward standardized evaluation metrics an...
AI is consolidating into a platform era: distribution marketplaces, capital-scale infrastructure bets, and a new engineering stack—agentic observability, guardrails, and AI-native architecture—that will reshape how CTOs design, operate, and govern their systems.
AI is being operationalized as a first-class production workload: governance is moving into architecture frameworks, companies are building internal agent execution platforms, and engineering orgs ...
AI infrastructure is rapidly transforming organizational structures and economic strategies, as companies adapt to the demands of advanced AI technologies and associated hardware.
AI infrastructure is rapidly becoming a central focus for multiple companies, driven by partnerships and technological advancements. This trend is reshaping how organizations approach their AI strategy.
This week brought a wave of insights around AI infrastructure and the pressing need for cloud resiliency. As organizations ramp up their AI capabilities, CTOs are increasingly focused on how to scale their infrastructure while ensuring reliability.
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