Maddy Yeazel of IBM Cloudability joins Hunter Willis to break down tokenomics: why tokens change forecasting, attribution, and ownership for FinOps teams.

If you were anywhere near a mortgage in 2006, you might remember the NINJA loan. The acronym stood for No Income, No Job, No Assets, and it sat at the far end of subprime lending...basically THE riski...

Setting a CloudWatch alarm for token consumption over a threshold is one of the fastest governance wins you can put in place. Your Amazon Bedrock spend is driven by tokens, and tokens can spike in minutes. A runaway agent, a misconfigured retry loop, or a single user pointing an automation at your most expensive model can burn through thousands of dollars before your monthly bill ever shows it.

Evaluating a CloudHealth alternative? Our 2026 guide compares VMware Aria, Thalaxo Cloud, and others for multi-cloud FinOps, helping you choose the best tool.

Enterprises are not choosing a single frontier AI provider; they are licensing all of them. As of early 2026, 81% of Global 2000 enterprises run three or more model families, and OpenAI, Anthropic, and Google Gemini together account for roughly 88 to 89% of enterprise LLM usage and spend. Drawing on

Densify is a predictive analytics engine that removes the guesswork from optimizing cloud and container environments.

Efficiency doesn't reduce total cloud spend, it unlocks explosive consumption. Here is how Jevons Paradox impacts Amazon Bedrock and Graviton workloads, and how to govern it with unit economics.

Learn how to build a flexible compute stack on Google Cloud to manage capacity wisely, reduce idle resources, and support AI workloads.

JAWS-UG 栃木 オフライン # 10 -秋の登壇祭り-の登壇資料です https://jawsug-tochigi.connpass.com/event/406770/

Every engineering team loves building on Amazon Web Services (AWS) for its scalability and rich toolset. However, as cloud environments expand, so do monthly costs. Unoptimized architectures, idle resources, and legacy infrastructure choices often cause AWS bills to spiral out of control. Reducing your AWS bill does not mean compromising performance or reliability. By adopting modern design patterns, you can optimize resource usage, improve scalability, and significantly lower operational expenses.

Il y a un an, Microsoft expliquait que Windows allait devenir un « OS agentique ». La formule avait été si mal reçue par les utilisateurs que le patron de Windows, Pavan Davuluri, avait fini par fermer les réponses sous sa publication. Douze mois plus tard, Satya Nadella reprend l'idée, mais en changeant de support : ce n'est plus Windows qui devient un système d'exploitation pour l'IA, c'est Copilot qui devient un système d'exploitation pour le travail…

Cloud infrastructure enables elasticity, but unmonitored architectures quickly incur high costs and performance bottlenecks. This technical guide outlines core strategies to optimize AWS workloads: right-sizing compute with Graviton processors, automating data lifecycles using Amazon S3 storage tiers, and decoupling services with SQS and EventBridge. Learn proactive FinOps governance, budget alerting, and anomaly detection to build scalable, resilient, and cost-effective cloud systems.

We won **Best Use of Snowflake** at **ELEMENTX 2026** , an **MLH Hack Day** hosted by the Department of CSE, Integral University, Lucknow! 🥳 Taking the top spot also came with a **Machine Learning Kit** 🛠️ and a **.xyz domain** 🌐 from XYZ. Definitely something we're super happy to receive! 😊 ## 🚨 The $9,000/Minute Problem In enterprise IT and FinOps, downtime can cost upwards of **$9,000 per minute**. When an incident happens, the information needed to understand it is often scattered across runbooks, documentation, historical tickets and system records. Engineers can spend significant time searching through all of this before figuring out what went wrong. We took on **Problem Statement 02: "From Signal to Decision"** to tackle this. ## 💡 We Built ResolveIQ **ResolveIQ** is an AI-powered incident copilot that brings relevant incident information together and helps engineers understand: * 🔎 What happened? * 🧠 Why did it happen? * ⚡ What should we do next? It retrieves relevant context from runbooks and historical incidents, then generates a structured **Root Cause Analysis (RCA)** with supporting source references. ## ❄️ Snowflake + AI For the **Best Use of Snowflake** category, we integrated **Snowflake Cortex AI** into our hackathon implementation to analyze incident context and generate cited RCA results. We also added a privacy layer for sensitive information and an incident command center for SLA tracking and remediation. > The public repository has since evolved, and the current implementation uses **Groq AI** for LLM inference while retaining the core RAG and incident-resolution workflow. ## 🧠 How It Works The core workflow is a RAG pipeline: 📄 Runbooks + Historical Tickets ↓ Document Processing ↓ Sentence Transformers ↓ FAISS Search ↓ Relevant Context ↓ AI Analysis ↓ 🧠 Root Cause Analysis ↓ ⚡ Next Steps The current project works with 7 PDF runbooks and 10 historical incident tickets, using `all-MiniLM-L6-v2` embeddings and FAISS for semantic retrieval. ## 🖥️ The Command Center We didn't want ResolveIQ to be just another chatbot. The interface includes: * 🚨 Live incident stream * ⏱️ SLA countdowns * 🔎 AI search * 📊 Evidence and source references * 🧠 Diagnostic and knowledge modes * 🛠️ Remediation terminal This lets engineers move from investigating an incident to understanding the evidence and deciding what to do next. ## ⚙️ Tech Stack **AI:** Snowflake Cortex AI, Groq **Backend:** FastAPI **RAG:** LangChain, FAISS, Sentence Transformers **Frontend:** React, Vite, Tailwind CSS ## 🧩 What We Learned The biggest takeaway for me was that building an AI application isn't just about choosing an LLM. A lot of the engineering work went into retrieving the right context, grounding responses in source material, handling sensitive data and turning AI output into something actually useful for engineers. ## 🤝 The Team This was definitely a team effort. Really grateful to have built this with Arshil Masood, Kamran Rizvi and Ayushmaan Vaibhav. 💙 We bounced ideas around, worked through problems, fixed what broke and kept pushing until we had something we could confidently put in front of the judges. ## 🏆 Final Result 🥇 **Best Use of Snowflake** — ELEMENTX 2026 Huge thanks to Major League Hacking, Snowflake, XYZ and the Department of CSE, Integral University for making the experience possible. Definitely a hackathon we'll remember. 🚀 # hackathon #ai #snowflake #react