gogoai.dev — Our Blog https://gogoai.dev A curated discovery feed for SRE, AIOps, Platform, DevOps, and Cloud Ops engineers. News, products, jobs, events, CFPs, and community content in one fast feed. en The Quiet Defeat of Vector Databases: How Knowledge Graphs Became the Backbone of Enterprise AI https://gogoai.dev/blogs/quiet-defeat-of-vector-databases 08a86f01-2d9c-45e8-a13a-ea1105cbaf9a Fri, 10 Jul 2026 10:47:23 GMT Why teams running real enterprise AI workloads are quietly migrating from vector databases to knowledge graphs — and what it means for cost, latency, and model size. airagknowledge-graphsvector-databasesenterprise-ai The Future Economics of Enterprise AI Deployment https://gogoai.dev/blogs/future-economics-of-enterprise-ai-deployment 0b76c0de-7519-4506-9c83-71af179b6253 Fri, 10 Jul 2026 10:47:22 GMT Frontier API access is a structurally expensive, strategically fragile bet at scale. The winning architecture minimizes tokens consumed per unit of business value. aifinopsenterprise-aiinferencecost-optimization How to serve LLMs in production: a guide to GPU memory, KV cache, and sizing https://gogoai.dev/blogs/how-to-serve-llms-in-production 837c28f9-dac8-4ab7-9911-c16a3d7a5992 Fri, 10 Jul 2026 10:47:21 GMT Weights, KV cache, and engine overhead all share VRAM. A practical guide to sizing a GPU for multi-user LLM serving — with the formula and worked examples. llmgpukubernetesinferencevllm Anatomy of an LLM request: prefill, decode, latency, and cost https://gogoai.dev/blogs/anatomy-of-an-llm-request a512a080-0254-44d3-bccb-e6180db638fc Fri, 10 Jul 2026 10:47:20 GMT Every LLM request splits into prefill (compute-bound, sets TTFT) and decode (memory-bound, sets TPOT). Understanding both is how you control latency and cost. llminferencelatencycost-optimizationvllm Building LLM applications: RAG, agents, tool calling, and reasoning https://gogoai.dev/blogs/building-llm-applications 08d2124d-2e16-4a8c-8143-292794edf539 Fri, 10 Jul 2026 10:47:19 GMT RAG, tool calling, and agents are the three building blocks. Default to the simplest architecture that works — a 5-step agent costs 5x and fails 5x more. llmragagentstool-callingai LLM serving cheat sheet: GPUs, models, vLLM flags, and cost levers https://gogoai.dev/blogs/llm-serving-cheat-sheet 71cdae37-62ba-4dca-b972-0180c9bcf1ed Fri, 10 Jul 2026 10:47:18 GMT A single-page reference for serving LLMs in production: vocabulary, sizing formulas, GPU and model tables, essential vLLM flags, and cost levers ranked by impact. llmgpuvllminferencereference Common mistakes when serving LLMs in production: a field guide https://gogoai.dev/blogs/llm-serving-common-mistakes cad83ac6-8336-4b8c-a4b0-03a0ae1b4ba7 Fri, 10 Jul 2026 10:47:17 GMT 24 real production LLM mistakes — from deploying Ollama for multi-user to unbounded conversation history — each with the fix. Read it before you ship. llmsreinferencevllmproduction