{"id":43525,"date":"2026-07-22T14:01:12","date_gmt":"2026-07-22T11:01:12","guid":{"rendered":"https:\/\/fatihsoysal.com\/blog\/prompt-caching-nasil-calisir-ve-llm-maliyetlerini-dusurur-mu\/"},"modified":"2026-07-22T14:01:12","modified_gmt":"2026-07-22T11:01:12","slug":"prompt-caching-nasil-calisir-ve-llm-maliyetlerini-dusurur-mu","status":"publish","type":"post","link":"https:\/\/fatihsoysal.com\/blog\/prompt-caching-nasil-calisir-ve-llm-maliyetlerini-dusurur-mu\/","title":{"rendered":"Prompt Caching Nas\u0131l \u00c7al\u0131\u015f\u0131r ve LLM Maliyetlerini D\u00fc\u015f\u00fcr\u00fcr m\u00fc?"},"content":{"rendered":"<h2>Prompt Caching Nas\u0131l \u00c7al\u0131\u015f\u0131r ve LLM Maliyetlerini D\u00fc\u015f\u00fcr\u00fcr m\u00fc?<\/h2>\n<p>Prompt caching, b\u00fcy\u00fck dil modellerinde tekrarlayan girdileri \u00f6nbelle\u011fe alarak API maliyetlerini ve gecikmeyi radikal bi\u00e7imde d\u00fc\u015f\u00fcren kritik bir teknolojidir.<\/p>\n<p>Yapay zeka sistemlerini \u00fcretim ortam\u0131nda (production) \u00f6l\u00e7eklendirirken geli\u015ftiricilerin ve \u015firketlerin kar\u015f\u0131la\u015ft\u0131\u011f\u0131 en b\u00fcy\u00fck iki engel, y\u00fcksek token maliyetleri ve yan\u0131t s\u00fcrelerindeki gecikmelerdir. \u00d6zellikle b\u00fcy\u00fck dil modelleri (LLM) ile \u00e7al\u0131\u015f\u0131rken her istekte devasa sistem talimatlar\u0131n\u0131, uzun PDF belgelerini, veritaban\u0131 \u015femalar\u0131n\u0131 veya binlerce sat\u0131rl\u0131k kod bloklar\u0131n\u0131 tekrar tekrar modele g\u00f6ndermek hem b\u00fct\u00e7eyi zorlar hem de sistem performans\u0131n\u0131 ciddi \u015fekilde d\u00fc\u015f\u00fcr\u00fcr. \u0130\u015fte tam bu noktada, yapay zeka mimarisinde son d\u00f6nemin en b\u00fcy\u00fck yeniliklerinden biri olan prompt caching (istem \u00f6nbellekleme) mekanizmas\u0131 devreye girmektedir.<\/p>\n<p>Ancak bu teknoloji her senaryoda mucizeler yarat\u0131r m\u0131? Hangi durumlarda maliyetleri %80 ila %90 oran\u0131nda d\u00fc\u015f\u00fcr\u00fcrken, hangi durumlarda hi\u00e7bir tasarruf sa\u011flamaz hatta ek maliyet \u00e7\u0131karabilir? Bu rehberde, prompt caching teknolojisinin alt\u0131nda yatan teknik mant\u0131\u011f\u0131, \u00e7al\u0131\u015fma mekanizmas\u0131n\u0131, sa\u011flay\u0131c\u0131lar\u0131n fiyatland\u0131rma politikalar\u0131n\u0131 ve ger\u00e7ek d\u00fcnyada maliyet optimize etme stratejilerini ayr\u0131nt\u0131l\u0131 bir \u015fekilde inceleyece\u011fiz.<\/p>\n<h2>Prompt Caching Nedir ve Neden Bu Kadar Pop\u00fcler Hale Geldi?<\/h2>\n<p>Prompt caching, bir LLM API&#8217;sine g\u00f6nderilen uzun metin veya token dizilerinin sunucu taraf\u0131nda ge\u00e7ici olarak saklanmas\u0131 ve sonraki isteklerde bu verilerin yeniden i\u015flenmeden do\u011frudan bellekten okunmas\u0131 i\u015flemidir. Bu i\u015flem, Transformer mimarisindeki dikkat (attention) mekanizmas\u0131n\u0131n gerektirdi\u011fi a\u011f\u0131r matematiksel hesaplama maliyetini ortadan kald\u0131rmay\u0131 hedefler.<\/p>\n<p>Geleneksel bir LLM API \u00e7a\u011fr\u0131s\u0131nda, istemci her yeni istek att\u0131\u011f\u0131nda model t\u00fcm girdi metnini (input tokens) ba\u015ftan sona analiz etmek zorundad\u0131r. \u00d6rne\u011fin, 50.000 token uzunlu\u011funda bir hukuki s\u00f6zle\u015fmeyi veya teknik dok\u00fcmantasyonu modele y\u00fckledi\u011finizi varsayal\u0131m. Bu dok\u00fcman \u00fczerinden modele s\u0131rayla 10 farkl\u0131 soru sordu\u011funuzda, model her bir soruda o 50.000 tokenl\u0131k metni tekrar tekrar ba\u015ftan sona i\u015fler. Dolay\u0131s\u0131yla, siz asl\u0131nda ayn\u0131 sabit metin i\u00e7in 10 defa tam girdi \u00fccreti \u00f6dersiniz ve her defas\u0131nda modelin metni yeniden anlamland\u0131rmas\u0131n\u0131 beklersiniz.<\/p>\n<p>Oysa prompt caching mant\u0131\u011f\u0131 devreye girdi\u011finde, bu 50.000 tokenl\u0131k sabit k\u0131s\u0131m ilk istekte bir kez i\u015flendikten sonra sunucunun belle\u011finde \u00f6nbelle\u011fe al\u0131n\u0131r. Sonraki 9 sorunuzda model, sabit dok\u00fcman\u0131 tekrar okumak yerine \u00f6nbellekten saliseler i\u00e7inde y\u00fckler ve sadece yeni sordu\u011funuz k\u0131sa soruyu i\u015fler. Bunun sonucunda hem hesaplama maliyetleriniz dramatik bir \u015fekilde d\u00fc\u015fer hem de ilk token \u00fcretme s\u00fcresi (Time to First Token &#8211; TTFT) inan\u0131lmaz derecede h\u0131zlan\u0131r.<\/p>\n<h2>Prompt Caching Teknik Olarak Nas\u0131l \u00c7al\u0131\u015f\u0131r?<\/h2>\n<p>Teknik boyutta prompt caching, do\u011frudan Transformer mimarisinin kalbinde yer alan Anahtar-De\u011fer (Key-Value veya KV) \u00f6nbellek yap\u0131s\u0131na dayan\u0131r. Bir dil modeli metni i\u015flerken, her bir token i\u00e7in dikkat (attention) matrisleri \u00e7er\u00e7evesinde Key (Anahtar) ve Value (De\u011fer) vekt\u00f6rleri hesaplar. Bu vekt\u00f6rler, modelin kelimeler aras\u0131ndaki anlamsal ba\u011flar\u0131 kurmas\u0131n\u0131 sa\u011flayan temel verilerdir.<\/p>\n<h3>KV Cache ve \u00d6nek E\u015fle\u015fmesi (Prefix Matching)<\/h3>\n<p>Prompt caching teknolojisinin temelinde &#8220;\u00f6nek e\u015fle\u015fmesi&#8221; (prefix matching) kural\u0131 yatar. Yapay zeka sa\u011flay\u0131c\u0131lar\u0131n\u0131n (Anthropic, OpenAI, Google) sunucular\u0131, istemciden gelen yeni bir iste\u011fi ald\u0131\u011f\u0131nda mant\u0131ksal olarak \u015fu ad\u0131mlar\u0131 izler:<\/p>\n<ul>\n<li><strong>Token Dizisi Kontrol\u00fc:<\/strong> Sistem, gelen istemin en ba\u015f\u0131ndaki token dizisini inceler.<\/li>\n<li><strong>\u00d6nbellek Havuzu Sorgusu:<\/strong> Sunucu, bu spesifik token \u00f6nekinin daha \u00f6nce hesaplanm\u0131\u015f KV matrislerinin bellek havuzunda (RAM\/VRAM) bulunup bulunmad\u0131\u011f\u0131n\u0131 kontrol eder.<\/li>\n<li><strong>KV Matrisi Y\u00fckleme (Cache Hit):<\/strong> E\u011fer birebir e\u015fle\u015fen bir \u00f6nek bulunursa, \u00f6nceden hesaplanm\u0131\u015f KV matrisleri belle\u011fe do\u011frudan y\u00fcklenir. Model bu k\u0131sm\u0131 ba\u015ftan hesaplama ihtiyac\u0131 duymaz.<\/li>\n<li><strong>Dinamik Token \u0130\u015fleme:<\/strong> Model, yaln\u0131zca \u00f6nbelle\u011fe al\u0131nan \u00f6nekten sonra gelen yeni (dinamik) tokenlar i\u00e7in dikkat matrislerini hesaplar.<\/li>\n<\/ul>\n<p>Bu \u00e7al\u0131\u015fma prensibi nedeniyle, \u00f6nbelle\u011fe al\u0131nmas\u0131 istenen verilerin mutlaka istemin (prompt) en ba\u015f\u0131nda yer almas\u0131 \u015fartt\u0131r. E\u011fer istemin ba\u015flang\u0131c\u0131ndaki tek bir karakter veya token bile de\u011fi\u015firse, \u00f6nek e\u015fle\u015fmesi k\u0131r\u0131larak &#8220;Cache Miss&#8221; (\u00f6nbellek \u0131skalamas\u0131) meydana gelir ve sistem t\u00fcm metni ba\u015ftan i\u015flemek zorunda kal\u0131r.<\/p>\n<h2>B\u00fcy\u00fck Dil Modellerinde Maliyet Analizi ve Fiyatland\u0131rma Mant\u0131\u011f\u0131<\/h2>\n<p>Yapay zeka sa\u011flay\u0131c\u0131lar\u0131, sunucu y\u00fcklerini azaltt\u0131\u011f\u0131 ve altyap\u0131 verimlili\u011fini art\u0131rd\u0131\u011f\u0131 i\u00e7in geli\u015ftiricileri \u00f6nbellek kullan\u0131m\u0131na te\u015fvik eder. Bu do\u011frultuda olduk\u00e7a cazip fiyatland\u0131rma modelleri sunmaktad\u0131rlar. Ancak maliyet hesab\u0131n\u0131 do\u011fru yapabilmek i\u00e7in &#8220;\u00d6nbellek Yazma&#8221; (Cache Write) ve &#8220;\u00d6nbellek Okuma&#8221; (Cache Read) kavramlar\u0131n\u0131 anlamak son derece \u00f6nemlidir.<\/p>\n<p>A\u015fa\u011f\u0131daki tabloda, pop\u00fcler bir LLM API servisindeki standart ve \u00f6nbellek tabanl\u0131 token maliyetlerinin oranlar\u0131 g\u00f6sterilmektedir:<\/p>\n<table>\n<thead>\n<tr>\n<th>\u0130\u015flem T\u00fcr\u00fc<\/th>\n<th>Tahmini Maliyet Oran\u0131<\/th>\n<th>A\u00e7\u0131klama<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Standart Girdi Token\u0131<\/td>\n<td>%100 (Baz Fiyat)<\/td>\n<td>\u00d6nbellekleme olmadan i\u015flenen standart metin tokenlar\u0131.<\/td>\n<\/tr>\n<tr>\n<td>\u00d6nbellek Yazma (Cache Write)<\/td>\n<td>%125 (Baz Fiyata G\u00f6re)<\/td>\n<td>Metnin ilk kez i\u015flenip \u00f6nbelle\u011fe kaydedilmesi esnas\u0131ndaki maliyet.<\/td>\n<\/tr>\n<tr>\n<td>\u00d6nbellek Okuma (Cache Read)<\/td>\n<td>%10 (%90 \u0130ndirimli)<\/td>\n<td>Sonraki isteklerde \u00f6nbellekten okunan sabit token maliyeti.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>Yukar\u0131daki tablodan da anla\u015f\u0131laca\u011f\u0131 \u00fczere, bir metni \u00f6nbelle\u011fe yazman\u0131n ilk maliyeti standart fiyattan %25 daha y\u00fcksek olabilir. Fakat ayn\u0131 metin ikinci kez okundu\u011funda %90 indirim uygulan\u0131r. Dolay\u0131s\u0131yla, bir \u00f6nbellek blo\u011funa en az 2 veya daha fazla kez istek at\u0131ld\u0131\u011f\u0131 anda net kar elde edilmeye ba\u015flan\u0131r. \u0130stek say\u0131s\u0131 artt\u0131k\u00e7a toplam girdi maliyetlerindeki tasarruf oran\u0131 %85-90 seviyelerine kadar ula\u015f\u0131r.<\/p>\n<h2>Prompt Caching Ne Zaman Ger\u00e7ekten Tasarruf Sa\u011flar?<\/h2>\n<p>\u00d6nbellekleme mimarisi her yaz\u0131l\u0131m senaryosu i\u00e7in otomatik bir \u00e7\u00f6z\u00fcm de\u011fildir. Hatta yanl\u0131\u015f kurgulanan sistemlerde maliyetleri d\u00fc\u015f\u00fcrmek yerine art\u0131rabilir. Bu nedenle hangi durumlarda kullan\u0131lmas\u0131 gerekti\u011fini iyi analiz etmek gerekir.<\/p>\n<h3>Y\u00fcksek ROI (Yat\u0131r\u0131m Getirisi) Sa\u011flayan Senaryolar<\/h3>\n<ul>\n<li><strong>Uzun Sistem Talimatlar\u0131 ve Rol Tan\u0131mlar\u0131:<\/strong> M\u00fc\u015fteri temsilcisi botlar\u0131nda veya karma\u015f\u0131k ajanlarda (AI Agents) kullan\u0131lan y\u00fczlerce sat\u0131rl\u0131k kural setleri, rol tan\u0131mlar\u0131 ve Few-Shot \u00f6rnekleri.<\/li>\n<li><strong>RAG ve Dok\u00fcman Analiz Sistemleri:<\/strong> Kullan\u0131c\u0131n\u0131n geni\u015f bir PDF belgesi, \u015firket i\u00e7i veri taban\u0131 veya teknik bir k\u0131lavuz hakk\u0131nda ard\u0131 ard\u0131na sorular sordu\u011fu uygulamalar.<\/li>\n<li><strong>Kod Taban\u0131 (Codebase) \u0130nceleme Ara\u00e7lar\u0131:<\/strong> Bir projenin t\u00fcm kaynak kodunun isteme eklenip, a\u015famal\u0131 olarak hata ay\u0131klama veya refactoring istendi\u011fi durumlar.<\/li>\n<li><strong>\u00c7ok Turlu (Multi-Turn) Sohbet Sistemleri:<\/strong> Konu\u015fma ge\u00e7mi\u015finin her yeni mesajda b\u00fcy\u00fcyerek tekrar modele g\u00f6nderildi\u011fi uzun sohbet oturumlar\u0131.<\/li>\n<\/ul>\n<h3>Prompt Caching Kullanman\u0131n Mant\u0131ks\u0131z Oldu\u011fu Senaryolar<\/h3>\n<ul>\n<li><strong>Tek Seferlik K\u0131sa \u0130stekler:<\/strong> Kullan\u0131c\u0131n\u0131n tek bir soru sorup yan\u0131t ald\u0131\u011f\u0131 ve devam\u0131n\u0131n gelmedi\u011fi arama motoru benzeri yap\u0131lar.<\/li>\n<li><strong>Dinamik De\u011fi\u015fen \u00d6nekler:<\/strong> \u0130stemin en ba\u015f\u0131nda her defas\u0131nda de\u011fi\u015fen tarih, zaman, rastgele kullan\u0131c\u0131 ID&#8217;si veya anl\u0131k borsa verisi gibi dinamik \u00f6\u011felerin bulundu\u011fu tasar\u0131mlar.<\/li>\n<li><strong>D\u00fc\u015f\u00fck Trafikli ve Seyrek Kullan\u0131lan Uygulamalar:<\/strong> \u00d6nbellek \u00f6mr\u00fc (Time To Live &#8211; TTL) dolmadan \u00f6nce ikinci bir iste\u011fin gelmedi\u011fi, \u00f6nbelle\u011fin s\u00fcrekli so\u011fudu\u011fu (cache expire) durumlar.<\/li>\n<\/ul>\n<h2>Uygulamal\u0131 Kod \u00d6rne\u011fi: Python ile Prompt Caching Entegrasyonu<\/h2>\n<p>A\u015fa\u011f\u0131daki Python kod \u00f6rne\u011finde, bir yapay zeka API&#8217;si \u00fczerinden uzun bir \u015firket politikas\u0131n\u0131n nas\u0131l \u00f6nbelle\u011fe al\u0131naca\u011f\u0131 ve bu \u00f6nbellek \u00fczerinden nas\u0131l tasarruf sa\u011flanaca\u011f\u0131 g\u00f6sterilmektedir.<\/p>\n<pre><code>import os\nimport anthropic\n\n# API istemcisini ba\u015flatma\nclient = anthropic.Anthropic(api_key=os.environ.get(\"ANTHROPIC_API_KEY\"))\n\n# \u00d6nbelle\u011fe al\u0131nacak sabit ve uzun metin (\u00d6rn: \u015eirket \u0130ade ve Garanti Politikas\u0131)\nlarge_system_instruction = \"\"\"\n\u015eirket \u0130ade Politikas\u0131 K\u0131lavuzu v4.2...\n[Burada 5.000 ila 20.000 token uzunlu\u011funda detayl\u0131 \u015firket kurallar\u0131 yer almaktad\u0131r]\n...\n\"\"\"\n\n# \u0130lk \u0130stek: \u00d6nbelle\u011fe Yazma (Cache Write) ger\u00e7ekle\u015fir\nresponse_1 = client.beta.prompt_caching.messages.create(\n    model=\"claude-3-5-sonnet-20041022\",\n    max_tokens=300,\n    system=[\n        {\n            \"type\": \"text\",\n            \"text\": large_system_instruction,\n            \"cache_control\": {\"type\": \"ephemeral\"} # \u00d6nbellekleme i\u015fareti\n        }\n    ],\n    messages=[\n        {\"role\": \"user\", \"content\": \"\u00dcr\u00fcn sat\u0131n al\u0131nd\u0131ktan 20 g\u00fcn sonra iade edilebilir mi?\"}\n    ]\n)\n\nprint(\"1. Yan\u0131t:\", response_1.content[0].text)\nprint(\"1. \u0130stek Token Kullan\u0131m\u0131:\", response_1.usage)\n\n# \u0130kinci \u0130stek: \u00d6nbellekten Okuma (Cache Read) ger\u00e7ekle\u015fir\nresponse_2 = client.beta.prompt_caching.messages.create(\n    model=\"claude-3-5-sonnet-20041022\",\n    max_tokens=300,\n    system=[\n        {\n            \"type\": \"text\",\n            \"text\": large_system_instruction,\n            \"cache_control\": {\"type\": \"ephemeral\"} # Birebir ayn\u0131 \u00f6nek\n        }\n    ],\n    messages=[\n        {\"role\": \"user\", \"content\": \"Kargo \u00fccreti iade ediliyor mu?\"}\n    ]\n)\n\nprint(\"2. Yan\u0131t:\", response_2.content[0].text)\nprint(\"2. \u0130stek Token Kullan\u0131m\u0131:\", response_2.usage)\n<\/code><\/pre>\n<p>Yukar\u0131daki kod blo\u011funda g\u00f6r\u00fcld\u00fc\u011f\u00fc \u00fczere, <code>cache_control<\/code> parametresi ile sistem talimat\u0131 \u00f6nbelle\u011fe i\u015faretlenmi\u015ftir. \u0130lk istek g\u00f6nderildi\u011finde API sunucusu metni i\u015fler ve belle\u011fe yazar. \u0130kinci istek at\u0131ld\u0131\u011f\u0131nda ise <code>large_system_instruction<\/code> de\u011fi\u015fkeni de\u011fi\u015fmedi\u011fi i\u00e7in API do\u011frudan \u00f6nbellekten okuma yapar ve token girdi \u00fccretinde %90 tasarruf sa\u011flar.<\/p>\n<h2>\u0130leri D\u00fczey Stratejiler: \u00d6nbellek Verimlili\u011fini Art\u0131rma Y\u00f6ntemleri<\/h2>\n<p>Prompt caching altyap\u0131s\u0131ndan maksimum d\u00fczeyde yararlanmak i\u00e7in yaz\u0131l\u0131m ve istem mimarinizi bu yap\u0131ya uyumlu \u015fekilde tasarlaman\u0131z gerekir. Ba\u015far\u0131l\u0131 bir optimizasyon i\u00e7in \u015fu \u00fc\u00e7 temel stratejiyi uygulayabilirsiniz:<\/p>\n<ul>\n<li><strong>Statik ve Dinamik Verileri Kesin Olarak Ay\u0131r\u0131n:<\/strong> \u0130stem tasar\u0131m\u0131nda en sabit, hi\u00e7 de\u011fi\u015fmeyen verileri (Sistem Kurallar\u0131, Dok\u00fcmanlar, \u00d6rnekler) daima istemin en \u00fcst\u00fcne yerle\u015ftirin. Tarih, kullan\u0131c\u0131 ad\u0131, oturum bilgisi ve anl\u0131k sorular gibi de\u011fi\u015fken verileri ise en alt k\u0131sma ekleyin.<\/li>\n<li><strong>Mod\u00fcler \u00d6nbellek Bloklar\u0131 Olu\u015fturun:<\/strong> Sisteminizde birden fazla sabit veri seti varsa (\u00f6rne\u011fin hem \u00fcr\u00fcn katalo\u011fu hem de kullan\u0131c\u0131 k\u0131lavuzu), bunlar\u0131 tek bir devasa metin yapmak yerine ayr\u0131 mod\u00fcler bloklar halinde \u00f6nbelle\u011fe i\u015faretleyin. B\u00f6ylece bir dok\u00fcman g\u00fcncellendi\u011finde di\u011fer dok\u00fcmanlar\u0131n \u00f6nbelle\u011fi bozulmaz.<\/li>\n<li><strong>\u00d6nbellek Is\u0131tma (Cache Warm-up) Tekni\u011fi:<\/strong> Y\u00fcksek trafik bekledi\u011finiz saatlerin hemen \u00f6ncesinde, sistem \u00f6nbelleklerini dolduracak otomatik &#8220;\u0131s\u0131nma&#8221; istekleri g\u00f6nderin. Bu sayede sisteminize gelen ilk ger\u00e7ek kullan\u0131c\u0131lar bile do\u011frudan \u00f6nbellekten okuma yaparak ultra h\u0131zl\u0131 yan\u0131tlar al\u0131r.<\/li>\n<\/ul>\n<h2>Ger\u00e7ek D\u00fcnya Vaka Analizi: E-Ticaret Asistan\u0131nda %72 Maliyet D\u00fc\u015f\u00fc\u015f\u00fc<\/h2>\n<p>Konuyu somutla\u015ft\u0131rmak ad\u0131na ger\u00e7ek bir e-ticaret platformunun m\u00fc\u015fteri hizmetleri yapay zeka botu senaryosunu inceleyelim. Bu platform g\u00fcnl\u00fck ortalama 40.000 m\u00fc\u015fteri sorgusuna yan\u0131t vermektedir. Botun arkas\u0131ndaki istem yap\u0131s\u0131; t\u00fcm iade \u015fartlar\u0131n\u0131, kargo politikalar\u0131n\u0131, garanti ko\u015fullar\u0131n\u0131 ve \u00fcr\u00fcn SSS dok\u00fcman\u0131n\u0131 i\u00e7ermekte olup toplam 12.000 token uzunlu\u011fundad\u0131r.<\/p>\n<p>\u00d6nbellekleme \u00f6ncesinde ve sonras\u0131ndaki maliyet tablosu \u015fu \u015fekilde ger\u00e7ekle\u015fmi\u015ftir:<\/p>\n<ul>\n<li><strong>Prompt Caching \u00d6ncesi Durum:<\/strong> G\u00fcnl\u00fck 40.000 istek x 12.000 girdi token\u0131 = G\u00fcnl\u00fck 480 Milyon Token i\u015fleme. Standart girdi fiyat\u0131 \u00fczerinden g\u00fcnl\u00fck maliyet yakla\u015f\u0131k $1,440 (Ayl\u0131k ~$43,200).<\/li>\n<li><strong>Prompt Caching Sonras\u0131 Durum:<\/strong> Sistem dok\u00fcman\u0131 \u00f6nbelle\u011fe al\u0131n\u0131r. \u0130steklerin %95&#8217;i \u00f6nbellekten okuma (Cache Read) olarak ger\u00e7ekle\u015fir. G\u00fcnl\u00fck toplam maliyet yakla\u015f\u0131k $403 seviyesine geriler (Ayl\u0131k ~$12,090).<\/li>\n<\/ul>\n<p>Bu senaryoda \u015firket, kod mimarisini de\u011fi\u015ftirmeden sadece prompt caching entegrasyonu ger\u00e7ekle\u015ftirerek ayl\u0131k $31,110 tutar\u0131nda devasa bir b\u00fct\u00e7e tasarrufu sa\u011flam\u0131\u015f, ayr\u0131ca ortalama yan\u0131t s\u00fcresini (latency) 2.8 saniyeden 0.9 saniyeye d\u00fc\u015f\u00fcrm\u00fc\u015ft\u00fcr.<\/p>\n<h2>S\u0131k\u00e7a Sorulan Sorular<\/h2>\n<p>Prompt caching teknolojisi hakk\u0131nda yaz\u0131l\u0131m geli\u015ftiricilerin en \u00e7ok merak etti\u011fi sorular ve yan\u0131tlar\u0131 a\u015fa\u011f\u0131dad\u0131r:<\/p>\n<p><strong>Prompt caching kullanmak yapay zeka yan\u0131tlar\u0131n\u0131n kalitesini de\u011fi\u015ftirir mi?<\/strong><br \/>\nHay\u0131r, prompt caching modelin \u00fcretti\u011fi yan\u0131tlar\u0131n kalitesini veya do\u011frulu\u011funu hi\u00e7bir \u015fekilde etkilemez. KV matrisleri matematiksel olarak birebir ayn\u0131 oldu\u011fu i\u00e7in modelin verece\u011fi \u00e7\u0131kt\u0131 mant\u0131ksal olarak \u00f6zde\u015ftir.<\/p>\n<p><strong>\u00d6nbelle\u011fe al\u0131nan veriler ne kadar s\u00fcre bellekte kal\u0131r?<\/strong><br \/>\n\u00c7o\u011fu yapay zeka sa\u011flay\u0131c\u0131s\u0131nda varsay\u0131lan \u00f6nbellek \u00f6mr\u00fc (TTL) 5 dakika ile 10 dakika aras\u0131ndad\u0131r. Ancak ilgili \u00f6nbellek blo\u011funa yeni bir istek geldik\u00e7e bu s\u00fcre otomatik olarak s\u0131f\u0131rlan\u0131r ve uzar. Trafik devam etti\u011fi s\u00fcrece \u00f6nbellek s\u0131cak kal\u0131r.<\/p>\n<p><strong>\u00d6nbellekleme i\u015flemi veri g\u00fcvenli\u011fi riski yarat\u0131r m\u0131?<\/strong><br \/>\nHay\u0131r, \u00f6nbelle\u011fe al\u0131nan veriler yaln\u0131zca sizin organizasyonunuza veya API anahtar\u0131n\u0131za (API Key) \u00f6zel g\u00fcvenli izolasyon alanlar\u0131nda saklan\u0131r. Ba\u015fka bir kullan\u0131c\u0131n\u0131n veya organizasyonun sizin \u00f6nbellek verilerinize eri\u015fmesi m\u00fcmk\u00fcn de\u011fildir.<\/p>\n<p><strong>\u00d6nbellek \u00f6zelli\u011finin aktif olmas\u0131 i\u00e7in minimum bir token s\u0131n\u0131r\u0131 var m\u0131d\u0131r?<\/strong><br \/>\nEvet, yapay zeka sa\u011flay\u0131c\u0131lar\u0131n\u0131n b\u00fcy\u00fck \u00e7o\u011funlu\u011funda \u00f6nbellekleme mekanizmas\u0131n\u0131n tetiklenebilmesi i\u00e7in minimum token s\u0131n\u0131r\u0131 bulunur (\u00d6rne\u011fin Anthropic i\u00e7in en az 1024 token, OpenAI i\u00e7in en az 1024 token). Bu s\u0131n\u0131r\u0131n alt\u0131ndaki k\u0131sa metinlerde \u00f6nbellekleme \u00e7al\u0131\u015fmaz.<\/p>\n","protected":false},"excerpt":{"rendered":"Prompt Caching Nas\u0131l \u00c7al\u0131\u015f\u0131r ve LLM Maliyetlerini D\u00fc\u015f\u00fcr\u00fcr m\u00fc? Prompt caching, b\u00fcy\u00fck dil modellerinde tekrarlayan girdileri \u00f6nbelle\u011fe alarak&hellip;","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"csco_page_header_type":"","csco_page_load_nextpost":"","csco_page_subscribe_form":"","csco_page_contact_form":"","footnotes":""},"categories":[1],"tags":[],"class_list":{"0":"post-43525","1":"post","2":"type-post","3":"status-publish","4":"format-standard","6":"category-genel","7":"cs-entry","8":"cs-video-wrap"},"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v20.5 (Yoast SEO v25.3.1) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Prompt Caching Nas\u0131l \u00c7al\u0131\u015f\u0131r ve LLM Maliyetlerini D\u00fc\u015f\u00fcr\u00fcr m\u00fc?<\/title>\n<meta name=\"description\" content=\"Prompt caching, b\u00fcy\u00fck dil modellerinde tekrarlayan girdileri \u00f6nbelle\u011fe alarak API 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