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Track OpenAI Spend: Explain Where Your OpenAI Budget Goes

Jan 30, 2026 By Lyne Carolyne In CloudZero

The inevitable happened. A while back, Gartner projected that in 2026, 30–50% of all new SaaS product features would use LLM inference. That meant OpenAI-style costs would become a standard part of SaaS COGS. Today, OpenAI has become one of the most operationally significant line items for SaaS companies. But for many teams, this creates an uncomfortable gap. Engineering sees OpenAI as a fast path to innovation.

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Building with the InfluxDB 3 MCP Server & Claude

Jan 30, 2026 By Suyash Joshi In InfluxData

InfluxDB 3 Model Context Protocol (MCP) server lets you manage and query InfluxDB 3 (Core, Enterprise, Dedicated, Serverless, Clustered) using natural language through popular LLM tools like Claude Desktop, ChatGPT Desktop, and other MCP-compatible agents. The setup is straightforward. In this article, we will focus on setting up InfluxDB 3 Enterprise using Docker with Claude Desktop.

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How we cut our NLQ agent debugging time from hours to minutes with LLM Observability

Jan 30, 2026 By Florent Le Gall In Datadog

This article is part of our series on how Datadog engineering teams use LLM Observability to build, monitor, and improve AI-powered systems. At Datadog, we’re always looking for ways to make complex data easier to explore.

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Keeping Frontier Models Reliable at Mistral AI with Rootly

Jan 30, 2026 By Rootly In Rootly

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How Agentic AI is Redefining Network Operations

Jan 30, 2026 By Dallon Robinette In Selector

For much of the past decade, many of the most ambitious ideas in artificial intelligence lived primarily in research papers, labs, and long-term roadmaps. Agentic AI was no exception. The concept of AI systems capable of reasoning, planning, and acting autonomously was widely discussed but largely theoretical. But earlier this month, Gartner released its report The Future of NetOps Is Agentic, reflecting a growing consensus that this has changed. What was once conceptual is now becoming operational.

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Context engineering: The missing layer for trusted AI in financial services

Jan 30, 2026 By Karen Mcdermott In Elastic

Financial services AI demands more than models and prompts. Context engineering provides real-time, governed, and explainable intelligence with Elastic serving as the foundational context layer. Artificial intelligence in financial services is no longer constrained by model capability. The real bottleneck is context.

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PIM Systems in the Age of AI: Real Benefits for Businesses

Jan 30, 2026 By OpsMatters In OpsMatters

Modern companies and brands compete across multiple channels: websites, marketplaces, social media, and apps, while customers expect accurate, detailed, and personalized product information instantly. Managing product data manually is no longer sustainable. Product Information Management (PIM) systems, once reserved for large companies, are now essential for businesses of all sizes. The global PIM market reached $14.4 billion in 2024 and is expected to grow to $33.4 billion by 2033 (IMARC Group). This growth reflects the urgent need for centralized product data management.

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N-able Ushers in New Era of Business Resilience Fueled by AI

Jan 29, 2026 By N-able In N-able

For today's SMBs, AI capabilities from N-able turn resilience into a built-in, everyday business function.

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Refactor Safely with AI: Using MCP and Traffic Replay to Validate Code Changes

Jan 29, 2026 By Matthew LeRay In Speedscale

So as software engineers using AI coding assistants, we’re quickly learning of a new anti-pattern: Hallucinated Success. You give your agent (e.g. Claude via terminal or various IDE code assistants) the command “refactor the billing controller.” The agent happily complies, churning out nice clean code. The agent even goes so far as to write a new unit test suite that passes at 100%. You integrate it. Your test suites pass. Your production code breaks. Why?

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Webinar Recap: What It Really Takes To Make AI Profitable

Jan 29, 2026 By Keith MacKenzie In CloudZero

Right now, 48% of organizations say they’re being asked to measure or report on AI-related costs. The problem is that they’re still figuring out how to do it. That was a very telling stat from a recent CloudZero webinar on AI and profitability, and speaks loudly to the reality that many organizations are still struggling to get a grasp on AI spend which our data shows to be rising sharply as a part of total spend in recent months.

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