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Turkish CMS Builder Slashes AI Costs With Caching and Routing

Alesta WEB, a Turkish software company, has detailed its approach to building a news CMS that integrates multiple large language models. Their strategy involves significant cost reductions, achieving approximately 95% savings on AI inference expenses. This was accomplished through a combination of caching, batch processing, and cascade routing techniques. AI

Summary written by gemini-2.5-flash-lite from 1 source. How we write summaries →

IMPACT Demonstrates practical infrastructure optimizations for reducing AI inference costs in content management systems.

RANK_REASON The cluster describes a specific product implementation and infrastructure optimization for a news CMS, rather than a core AI model release or significant industry-wide event.

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COVERAGE [1]

  1. Mastodon — mastodon.social TIER_1 · alestaweb ·

    🦋 Hello Fediverse — Alesta WEB here. We build news CMS and e-commerce software in Turkey since 2005, now powering 200+ active news sites. Just published our fir

    🦋 Hello Fediverse — Alesta WEB here. We build news CMS and e-commerce software in Turkey since 2005, now powering 200+ active news sites. Just published our first Dev.to deep-dive: "Building a Multi-LLM News CMS with PHP 8.2" How we cut AI inference costs by ~95% using cache + ba…