12 posts
The strongest levers to control LLM cost in production: token economics, prompt caching, semantic cache and model routing, illustrated with 2026 pricing moves.
In self-hosted LLMs, bill and latency come from the serving layer. Manifold throughput via PagedAttention, continuous batching, speculative decoding, and quantization.
The July 2026 LLM API price table, a TCO framework, and cost-cutting levers. Why cheapest isn't always right, plus the KVKK/data-residency dimension.
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.
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.
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.
The AI gateway is the control plane for all your LLM traffic: model routing, semantic cache, observability, PII redaction, and a KVKK-compliant architecture.
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.
Evaluating large language models in enterprise environments cannot be limited to benchmark scores or impressive demos. In production, the real question is not how intelligent a model appears, but how accurate, safe, cost-sustainable, and controllable it is. Accuracy alone is not enough; safety, compliance, human review, guardrails, latency, total cost of ownership, auditability, and behavioral consistency must all be considered together. This guide explains how enterprises should structure LLM evaluation across four core dimensions—accuracy, safety, cost, and control—using systematic eval design, test sets, risk classification, operational metrics, and governance principles.