18 posts
LLM monitoring is the discipline that makes a production language-model system's quality, cost, and performance visible through logging, tracing, and alerts. What to log and how to measure it.
The strongest levers to control LLM cost in production: token economics, prompt caching, semantic cache and model routing, illustrated with 2026 pricing moves.
Building RAG is easy, proving it reliable is hard. Retrieval/generation metrics, reference-free evaluation with RAGAS, OpenTelemetry spans, and cost-per-successful-output.
Inference is now 55-80% of AI cost. Model routing, caching, quantization, and the metric that matters: cost-per-successful-output. An LLM FinOps framework and the Turkey FX context.
2026 RAG is no longer linear. Adaptive routing, the agentic retrieve-reason-retrieve loop, five production patterns, and the highest-ROI intervention: hybrid retrieval + reranker.
Observability is now a production prerequisite. Tracing vs evaluation, hallucination detection, LangSmith/Langfuse/MLflow, and self-hosting options for KVKK.
LLM observability is now a production requirement. Tracing, cost, and quality monitoring with OpenTelemetry GenAI; 90% savings with semantic caching; a KVKK-compliant content-logging guide.
Cutting LLM costs 38-68% with semantic caching, model tiering, and token telemetry. The practical TokenOps playbook and observability stack.
What is LLM logging and why is it risky under KVKK? Personal data in logs, data masking, retention period, access control, and a compliance checklist in this comprehensive guide.
Agentic workflows make 50-200 calls per task; cheap tokens become expensive tasks. Cut cost 30-50% with caching, routing and observability.
How token optimization, model routing and semantic caching cut cost. In LLMOps, cost is now a first-class metric.
The OpenTelemetry GenAI Semantic Conventions standardized LLM tracing. A guide to building an observability pipeline for production LLM systems with token, cost, quality and KVKK balance.
OpenTelemetry GenAI conventions make LLM and agent systems observable in production. Track tokens, cost, latency, and quality while escaping vendor lock-in.
The AI gateway is the control plane for all your LLM traffic: model routing, semantic cache, observability, PII redaction, and a KVKK-compliant architecture.
Enterprise AI architecture is not just about selecting a large language model. A reliable AI system requires data pipelines, model infrastructure, API integrations, security controls, observability, workflow orchestration, human approval mechanisms and governance layers. This guide explains how to design production-ready enterprise AI systems from a strategic and technical perspective.
As AI agent systems become more common, one of the most important architectural questions is whether to use a single powerful agent or distribute tasks across multiple specialized agents. Many teams assume multi-agent systems are automatically more advanced, leading to unnecessary complexity. Others force truly separable workflows into a single agent and lose quality, control, and scalability. This guide compares single-agent and multi-agent architectures across technical, operational, cost, security, observability, coordination, and governance dimensions, and explains how to choose the right architecture for the right enterprise problem.
Building a reliable AI agent is not just about giving a large language model access to tools. Production-grade quality depends on how the agent chooses tools, plans multi-step tasks, manages memory, decides when to involve humans, and how the entire execution flow is observed and governed. This guide explains tool calling, planning, and memory from an enterprise systems perspective, and presents a practical architecture for reliable agentic AI with state management, human-in-the-loop design, observability, security, and governance.
Production-grade AI systems require far more than choosing a model or framework. Real success depends on how well orchestration, deployment, observability, evaluation, security, and governance layers work together. This guide compares the core layers of the AI engineering stack, explains what each layer is responsible for, where teams make the wrong architectural decisions, and how organizations can build a more reliable and scalable AI operating model.