DevOps AI Tools: Root Cause Analysis + eBPF + Clickhouse
Watch Coroot’s Root Cause Analysis AI pinpoint the exact cause of an incident and suggest fixes in seconds.
Operations | Monitoring | ITSM | DevOps | Cloud
The Place Where Modern Operations & Technology Come Together
AI adoption is messy. Here's how engineering leaders are taming the chaos.
There's a moment every engineering leader hits when implementing AI where they realize that no one really knows what they're doing. Not your competitors. Not the consultants. Not even the executives pressuring you to show results yesterday. Everyone is figuring this out in real time, and beneath the confident vendor pitches and LinkedIn thought leadership, the truth is messier than anyone wants to admit.
AI & FinOps: The New Power Duo Driving Modern Profitability
FinOps teams have been expected to understand millions of dollars in cloud and AI spend using tools that a handful of (usually technical) specialists can operate. Dashboards, filters, exports, and SQL have been the norm. That era is over. CloudZero is now bringing AI directly into the FinOps workflow so anyone in the business can ask natural-language questions about cloud and AI spend, and get accurate answers back from the platform.
Reinvent Your SQL Coding With dbForge AI Assistant in Seconds
Want to see how dbForge AI Assistant works? This quick video shows how the Assistant generates SQL, explains it, fixes errors, optimizes queries, and converts natural language to SQL. No manual coding needed Works inside multiple dbForge products.
Discover how to build AI-augmented applications with enterprise-grade security
IT leaders want AI that moves the needle without blowing up risk, cost, or changing control. Your teams need a path to productize AI features on top of existing apps, connect safely to external models, and satisfy audit requirements without slowing delivery. Those are the core buying criteria we hear from IT middle management: buy over build, predictable outcomes, and a strong compliance posture.
Training Foundation Models on a Trillion Data Points with Apache Iceberg
Training an AI foundation model on over a trillion data points sounds impossible without hitting your production systems. Here's how Datadog did it with Apache Iceberg for their time series forecasting model TOTO. The key challenge: extracting massive historical observability data (metrics spanning years) and running incremental preprocessing pipelines without overwhelming production services. Iceberg solved this by providing schema governance, consistency guarantees, and seamless integration with ML tools like Ray and PyTorch.
Leading Open Source Teams w/ Daniel Roe
In this episode, Daniel Roe, Lead Maintainer of the Nuxt framework, discusses his journey from studying law and theology to leading a major open-source framework. He explains Nuxt's unique governance and how Nuxt manages contributions through volunteer-driven work, LLM-powered issue triage, and creating welcoming spaces for newcomers to open source. This week, our chat touches on a variety of topics including.
The Role of Intelligent Platforms in Streamlining Enterprise Operations
In today's fast-paced business environment, organizations face increasing pressure to operate efficiently while managing complex workflows. Enterprises are no longer able to rely solely on traditional methods to handle operations, data, and decision-making processes. This is where intelligent platforms come into play, providing tools and systems that can automate tasks, streamline processes, and support more strategic decision-making.
How Forward-Looking Institutions are Benefiting from Agentic AI
Today’s higher education institutions operate complex digital ecosystems that were unimaginable a decade ago. Behind every college lies a portal of interconnected systems for registration, financial aid, course management, and campus services. The students using those systems are digital natives who can order food in seconds on their phones or have packages delivered the same day they order them.
How Inkeep Monitors Their AI Agent Framework with SigNoz
AI agents are fundamentally different beasts to monitor compared to traditional applications. A single user request can trigger a cascade of 10+ internal operations: sub-agent transfers, tool executions, LLM calls, API requests, each with unpredictable latency and failure modes. When something goes wrong (and with LLMs, things go wrong in creative ways), you need to see the entire execution flow to debug effectively.