This is the layer that closes the distance between a demo and production. It runs from model serving options and latency optimization to token cost management, monitoring and logging, evaluation pipelines and version migrations.
What is model serving? A guide to the serving layer: from managed APIs to self-hosted inference servers like vLLM, TGI and Ollama; batching, streaming, concurrency, throughput and latency trade-offs.
An integration-delay experience from the field. Why do legacy system APIs, data access permissions, missing test environments and data-mapping exceptions push project timelines?
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 AI agenda floods in with dozens of announcements a day. This guide teaches you to separate signal from noise, filter out hype, and evaluate sources — step by step.
What is LLM latency? Time to first token (TTFT), per-token time, streaming, and user perception: the components that determine response time and acceptable thresholds.
What is LLMOps? The operations discipline of taking a language model application to production and keeping it reliable with monitoring, evaluation, versioning, and cost control.
The strongest levers to control LLM cost in production: token economics, prompt caching, semantic cache and model routing, illustrated with 2026 pricing moves.
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.
Token prices dropped 80% but bills grow. How to control cost with routing, caching, batching, and prompt discipline — plus a CFO-ready FinOps framework.
The three layers of cutting the LLM bill: model, system and application. Prompt caching, model routing, batching and Turkey-specific FX and KVKK risks.
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.
AI consulting or in-house team? A comprehensive comparison of external AI consulting versus building an in-house team on cost, risk, speed, and knowledge transfer, plus a hybrid model and decision matrix.
How is AI consultant selection done? Consultant types, a 12-question evaluation checklist, reference checks, red flags, and proposal evaluation 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.
Why is the PoC-to-production transition hard? The reasons AI pilots fail to go live, a production-readiness checklist, architecture layers, MLOps, and a step-by-step transition plan in this comprehensive guide.
How token optimization, model routing and semantic caching cut cost. In LLMOps, cost is now a first-class metric.
What is MLOps? MLOps (Machine Learning Operations) is the engineering discipline that ties developing, deploying, monitoring, and updating machine learning models into a single repeatable process. This guide: a clear definition, why MLOps is needed, how it works, model deployment, CI/CD, model monitoring, MLOps vs DevOps, the LLMOps difference, GDPR, and FAQs.
What is LLMOps? The discipline of running large language models in production: MLOps difference, prompt management, evaluation, LLM observability, cost optimization, guardrails, and a maturity model.
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.
Prompt caching cuts input token cost up to 90% on Anthropic and 50% on OpenAI. I cover both approaches, when to pick which, and cache-hit design patterns.
OpenTelemetry GenAI conventions make LLM and agent systems observable in production. Track tokens, cost, latency, and quality while escaping vendor lock-in.
I walk through how I cut a production LLM bill in half, sometimes to a fifth: prompt caching, model routing, self-hosted quantization and the observability that makes it all visible. With a Turkey and KVKK lens, concrete cost math and a tactics table.
Grouped by format, newest first within each group.
LLMOps is the engineering discipline that covers the development, deployment, monitoring, evaluation and cost management of LLM-powered applications — extending classic MLOps with prompt versioning, eval-driven CI and observability tailored for non-deterministic systems.