Operations | Monitoring | ITSM | DevOps | Cloud

Since Anthropic announced Project Glasswing and the capabilities of Claude Mythos Preview, and OpenAI announced GPT-Cyber – my calendar has looked the same every day: Back-to-back calls with CISOs, AppSec leads, and security architects. And every call starts with the same question.

Together AI pricing ranges from $0.10 to $9.00 per million tokens. Compare all models, GPU rates, free tier details, and practical cost optimization strategies. Written for engineering leads, platform teams, and FinOps practitioners evaluating open-source inference providers.

Grafana Cloud pricing starts free and scales by metrics, logs, and traces. Compare all tiers, real-world costs, managed options, and ways to optimize. Written for DevOps engineers, SREs, platform teams, and FinOps practitioners managing observability spend.

Compare LLM API pricing across OpenAI, Anthropic, Google, DeepSeek, and Mistral in 2026. Full pricing tables, hidden cost breakdowns, and proven strategies to cut AI spend. Written for engineering leads, platform teams, and FinOps practitioners evaluating or optimizing production AI costs.

If you integrate DX NetOps with external dashboards, reporting engines, or IT service management tools, you likely rely on our API framework. We are currently migrating this framework from OData 2 to OData 4. This transition requires you to update your existing integrations so they continue to function properly. Let me walk you through exactly what is changing, how to identify your active API queries, and the specific adjustments you need to make to your setup.

Oh Dear keeps a watchful eye on your websites: uptime, performance, SSL certificates, broken links, DNS, cron jobs. If something can quietly break, we're already checking it for you. Today we're connecting that data to a new place: your AI assistant. We just shipped an MCP integration. If you use Claude, Cursor, or any other client that speaks the Model Context Protocol, you can now ask questions like "any broken links on my site?" or "when does my certificate expire?" in plain language.

Your dataset has hundreds of attributes. Some are self-explanatory: http.response.status_code, server.address. Others are not: meta.refinery.reason, dataset.slug, sli.latency_target_ms. If you don't know what an attribute means, you can't write a good query. And if an AI agent doesn't know what it means, it guesses.

Are your AI systems giving answers your teams cannot trust? Most enterprises deploy LLMs expecting reliable outputs, but the results often feel inconsistent or incomplete. The problem is the missing structure behind it. Enterprise data is usually fragmented across multiple systems, teams, and tools. Your AI does not understand how customers, products, policies, and operations connect. Without that context, it fills gaps with assumptions, which leads to unreliable results.

Have you tried using AI at work and felt it works well for small tasks, but not beyond that? It can handle simple things like creating a summary, writing a draft, or answering a question. This works because the task is clear. But most tasks are not that simple. They involve multiple steps. One step depends on another. Data comes from different systems, and some decisions need checks before moving ahead. This is where a single AI system starts to struggle.

This blog is based on the webinar, “Panel Discussion: Understanding the importance of GPUs for AI success”. You can watch the full recording by clicking here! Cheap GPUs don't kill AI startups. Cheap thinking about GPUs does. In 2026, the teams burning through runway fastest aren't the ones who can't afford compute; they're the ones measuring the wrong thing and scaling the wrong way.