{"id":43914,"date":"2026-08-07T14:00:51","date_gmt":"2026-08-07T11:00:51","guid":{"rendered":"https:\/\/fatihsoysal.com\/blog\/production-ai-uygulamalarinin-gercek-inference-maliyeti\/"},"modified":"2026-08-07T14:01:30","modified_gmt":"2026-08-07T11:01:30","slug":"production-ai-uygulamalarinin-gercek-inference-maliyeti","status":"publish","type":"post","link":"https:\/\/fatihsoysal.com\/blog\/production-ai-uygulamalarinin-gercek-inference-maliyeti\/","title":{"rendered":"Production AI Uygulamalar\u0131n\u0131n Ger\u00e7ek Inference Maliyeti"},"content":{"rendered":"<h2>Production AI Uygulamalar\u0131n\u0131n Ger\u00e7ek Inference Maliyeti<\/h2>\n<p>Yapay zeka modellerini canl\u0131ya alman\u0131n gizli maliyetlerini, GPU optimizasyonlar\u0131n\u0131 ve inference b\u00fct\u00e7enizi d\u00fc\u015f\u00fcrme stratejilerini ke\u015ffedin.<\/p>\n<p>Son y\u0131llarda yapay zeka modellerinin geli\u015ftirilmesi ve e\u011fitilmesi b\u00fcy\u00fck bir heyecan yaratt\u0131. Ancak bir\u00e7ok teknoloji \u015firketi ve giri\u015fim, modellerini \u00fcretim ortam\u0131na (production) ta\u015f\u0131d\u0131ktan sonra beklenmedik bir ger\u00e7ekle kar\u015f\u0131la\u015f\u0131yor: Model e\u011fitmek bir defal\u0131k bir yat\u0131r\u0131md\u0131r, fakat inference (\u00e7\u0131kar\u0131m) i\u015flemi sonsuza kadar s\u00fcren ve kullan\u0131m artt\u0131k\u00e7a katlanarak b\u00fcy\u00fcyen devasa bir operasyonel maliyettir. Bir\u00e7ok m\u00fchendislik ekibi, konsept kan\u0131tlama (PoC) a\u015famas\u0131nda harika \u00e7al\u0131\u015fan sistemlerin canl\u0131ya al\u0131nd\u0131\u011f\u0131nda b\u00fct\u00e7eleri nas\u0131l eritti\u011fini \u015fa\u015fk\u0131nl\u0131kla izlemektedir. Bu nedenle, ba\u015far\u0131l\u0131 bir yapay zeka \u00fcr\u00fcn\u00fc geli\u015ftirmek sadece en y\u00fcksek do\u011frulu\u011fa ula\u015fmakla ilgili de\u011fil, ayn\u0131 zamanda kabul edilebilir bir gecikme (latency) s\u00fcresi ve s\u00fcrd\u00fcr\u00fclebilir bir birim maliyet (unit economics) yakalamakla ilgilidir.<\/p>\n<p>Peki, \u00fcretim ortam\u0131ndaki bir yapay zeka uygulamas\u0131n\u0131n ger\u00e7ek maliyetini belirleyen fakt\u00f6rler nelerdir? Sunucu faturan\u0131z\u0131n aydan aya kontrolden \u00e7\u0131kmas\u0131n\u0131 engellemek i\u00e7in hangi teknik optimizasyonlar\u0131 yapmal\u0131s\u0131n\u0131z? Bu rehberde, yapay zeka \u00e7\u0131kar\u0131m mimarilerinin maliyet anatomisini inceleyecek, kapal\u0131 kaynak API&#8217;ler ile \u00f6zelle\u015ftirilmi\u015f altyap\u0131lar\u0131n finansal kar\u015f\u0131la\u015ft\u0131rmas\u0131n\u0131 yapacak ve GPU verimlili\u011finizi 10 kat\u0131na \u00e7\u0131karacak somut m\u00fchendislik tekniklerini ele alaca\u011f\u0131z.<\/p>\n<h2>Production A\u015famas\u0131nda Yapay Zeka Maliyetleri Neden Kontrolden \u00c7\u0131kar?<\/h2>\n<p>Yapay zeka projelerinde finansal felaketlerin ana nedeni, geleneksel yaz\u0131l\u0131m mimarileri ile derin \u00f6\u011frenme altyap\u0131lar\u0131 aras\u0131ndaki temel mant\u0131k fark\u0131d\u0131r. Geleneksel bir Web API servis d\u00fc\u015f\u00fcn\u00fcn. CPU tabanl\u0131 bir sunucuda milisaniyeler i\u00e7inde binlerce istek i\u015fleyebilirsiniz. \u00dcstelik bu sunucular\u0131n ayl\u0131k kiralama bedelleri olduk\u00e7a makuld\u00fcr. Ancak b\u00fcy\u00fck dil modelleri (LLM) veya g\u00f6rsel \u00fcretim modelleri i\u015fin i\u00e7ine girdi\u011finde, matematiksel i\u015flemler s\u0131radan i\u015flemcilerin kapasitesini a\u015far. Bu durum, bizi y\u00fcksek paralelle\u015ftirme g\u00fcc\u00fcne sahip pahal\u0131 GPU&#8217;lara (Grafik \u0130\u015fleme Birimi) ba\u011f\u0131ml\u0131 hale getirir.<\/p>\n<p>Ayr\u0131ca yapay zeka sistemlerinde maliyet do\u011frusal de\u011fil, stokastik ve kullan\u0131c\u0131 davran\u0131\u015f\u0131na ba\u011fl\u0131 olarak eksponansiyel \u015fekilde artar. \u00d6rne\u011fin bir LLM uygulamas\u0131nda, kullan\u0131c\u0131n\u0131n g\u00f6nderdi\u011fi istem (prompt) uzunlu\u011fu ve modelin \u00fcretti\u011fi yan\u0131t\u0131n token say\u0131s\u0131 do\u011frudan hesaplama y\u00fck\u00fcn\u00fc belirler. Tek bir kullan\u0131c\u0131n\u0131n karma\u015f\u0131k bir kod analizi istemesi, basit bir &#8220;merhaba&#8221; mesaj\u0131na g\u00f6re 100 kat daha fazla GPU d\u00f6ng\u00fcs\u00fc t\u00fcketebilir. Dolay\u0131s\u0131yla sabit bir kullan\u0131c\u0131 say\u0131s\u0131na sahip olsan\u0131z bile, kullan\u0131c\u0131lar\u0131n etkile\u015fim derinli\u011fi de\u011fi\u015ftik\u00e7e ay sonu faturan\u0131z devasa farklar g\u00f6sterebilir.<\/p>\n<p>Bununla birlikte, at\u0131l kapasite (idle capacity) problemi de ciddi bir maliyet kalemidir. Canl\u0131daki bir uygulaman\u0131n her an yan\u0131t vermesi gerekir. Kullan\u0131c\u0131 trafi\u011finiz gece yar\u0131s\u0131 d\u00fc\u015fse bile, GPU belle\u011finde (VRAM) model a\u011f\u0131rl\u0131klar\u0131n\u0131 haz\u0131r tutan sunucular\u0131 a\u00e7\u0131k b\u0131rakmak zorunda kalabilirsiniz. Yapay zeka \u00e7iplerinin saatlik kiralama \u00fccretleri g\u00f6z \u00f6n\u00fcne al\u0131nd\u0131\u011f\u0131nda, kullan\u0131lmayan ancak a\u00e7\u0131k bekleyen tek bir A100 GPU sunucusu bile \u015firketinize ayda binlerce dolarl\u0131k bo\u015funa harcama olarak geri d\u00f6ner.<\/p>\n<h2>Inference Maliyetini Etkileyen Temel Bili\u015fim Bile\u015fenleri Nelerdir?<\/h2>\n<p>Sistem mimarlar\u0131 olarak yapay zeka altyap\u0131 maliyetlerini analiz ederken konuyu sadece &#8220;sunucu kiras\u0131&#8221; olarak g\u00f6rmemeliyiz. Maliyeti olu\u015fturan mikro bile\u015fenleri anlamak, do\u011fru optimizasyon stratejisini se\u00e7menin ilk ad\u0131m\u0131d\u0131r. Do\u011fru bir maliyet analizi i\u00e7in takip edilmesi gereken temel metrikler ve bile\u015fenler \u015funlard\u0131r:<\/p>\n<ul>\n<li><strong>VRAM (GPU Belle\u011fi) T\u00fcketimi:<\/strong> Model parametrelerinin boyutu (\u00f6rne\u011fin 7B, 70B), parametre ba\u015f\u0131na d\u00fc\u015fen hassasiyet (FP32, FP16, INT8) ve \u00e7\u0131kar\u0131m an\u0131nda olu\u015fan KV Cache (Key-Value Cache) verisi VRAM s\u0131n\u0131rlar\u0131n\u0131 belirler. Yetersiz VRAM, daha fazla GPU kart\u0131 eklemenizi gerektirir.<\/li>\n<li><strong>Hesaplama Karma\u015f\u0131kl\u0131\u011f\u0131 (TFLOPS):<\/strong> Modelin her bir token veya piksel \u00fcretmek i\u00e7in yapmas\u0131 gereken matris \u00e7arpmalar\u0131n\u0131n toplam\u0131d\u0131r. Y\u00fcksek hesaplama g\u00fcc\u00fc, birim zamanda daha fazla i\u015flem yapabilen pahal\u0131 \u00e7iplere olan ihtiyac\u0131 art\u0131r\u0131r.<\/li>\n<li><strong>\u0130lk Token S\u00fcresi (TTFT) ve Token Ba\u015f\u0131na Gecikme (TPOT):<\/strong> Kullan\u0131c\u0131 deneyimi i\u00e7in kritik olan bu performans metrikleri, maliyetle do\u011frudan \u00e7eli\u015fir. D\u00fc\u015f\u00fck gecikme elde etmek genellikle donan\u0131m\u0131 a\u015f\u0131r\u0131 beslemeyi (over-provisioning) zorunlu k\u0131lar.<\/li>\n<li><strong>A\u011f Bant Geni\u015fli\u011fi ve Veri Transferi:<\/strong> B\u00fcy\u00fck modellerin istem yan\u0131tlar\u0131n\u0131 veya g\u00f6rsel \u00e7\u0131kt\u0131lar\u0131n\u0131 istemciye iletirken harcanan veri transfer \u00fccretleri, \u00f6zellikle bulut sa\u011flay\u0131c\u0131lar\u0131nda (AWS, GCP, Azure) beklenmedik maliyet art\u0131\u015flar\u0131na yol a\u00e7ar.<\/li>\n<\/ul>\n<p>Bu bile\u015fenleri do\u011fru y\u00f6netebilmek i\u00e7in birim maliyet denklemimizi olu\u015fturmal\u0131y\u0131z. Yapay zeka \u00e7\u0131kar\u0131m\u0131nda birim maliyet; i\u015flem ba\u015f\u0131na harcanan GPU saat \u00fccreti ile harcanan s\u00fcrenin \u00e7arp\u0131m\u0131n\u0131n, toplam ba\u015far\u0131l\u0131 yan\u0131t say\u0131s\u0131na b\u00f6l\u00fcnmesiyle elde edilir. Bu denklemi k\u00fc\u00e7\u00fcltmenin iki yolu vard\u0131r: Ya sunucu saat \u00fccretini d\u00fc\u015f\u00fcreceksiniz ya da ayn\u0131 donan\u0131m \u00fczerinde birim zamanda i\u015fledi\u011finiz istek say\u0131s\u0131n\u0131 (throughput) art\u0131racaks\u0131n\u0131z.<\/p>\n<h2>Self-Hosted GPU vs Bulut API Kullan\u0131m\u0131: Hangisi Daha Ekonomik?<\/h2>\n<p>Yapay zeka \u00fcr\u00fcn\u00fc geli\u015ftiren ekiplerin kar\u015f\u0131la\u015ft\u0131\u011f\u0131 en b\u00fcy\u00fck ikilem, proprietary (kapal\u0131 kaynak) API servislerini mi (OpenAI, Anthropic gibi) kullanmak yoksa kendi GPU sunucular\u0131nda a\u00e7\u0131k kaynakl\u0131 modelleri (Llama 3, Mistral gibi) bar\u0131nd\u0131rmak m\u0131 gerekti\u011fidir. Bu sorunun tek bir do\u011fru yan\u0131t\u0131 yoktur; cevap tamamen uygulaman\u0131z\u0131n \u00f6l\u00e7e\u011fine ve trafik yap\u0131s\u0131na ba\u011fl\u0131d\u0131r.<\/p>\n<p>Erken a\u015fama giri\u015fimler ve prototip geli\u015ftiriciler i\u00e7in API kullan\u0131m\u0131 neredeyse her zaman daha ekonomiktir. \u00c7\u00fcnk\u00fc API sa\u011flay\u0131c\u0131lar\u0131 sadece kulland\u0131\u011f\u0131n\u0131z token kadar \u00f6deme yapman\u0131z\u0131 sa\u011flar. Altyap\u0131 bak\u0131m\u0131, GPU doluluk oran\u0131 veya \u00f6l\u00e7eklendirme dertleriniz olmaz. Ancak \u00fcr\u00fcn\u00fcn\u00fcz b\u00fcy\u00fcd\u00fck\u00e7e ve ayl\u0131k token t\u00fcketiminiz milyarlar\u0131 bulduk\u00e7a, API maliyet e\u011frisi dikle\u015fir ve self-hosted (kendi sunucunda bar\u0131nd\u0131rma) se\u00e7ene\u011fi finansal a\u00e7\u0131dan mant\u0131kl\u0131 hale gelir.<\/p>\n<p>A\u015fa\u011f\u0131daki tabloda, farkl\u0131 kullan\u0131m senaryolar\u0131na g\u00f6re API servisleri ile self-hosted GPU altyap\u0131lar\u0131n\u0131n kar\u015f\u0131la\u015ft\u0131rmas\u0131n\u0131 inceleyebilirsiniz:<\/p>\n<table>\n<thead>\n<tr>\n<th>Kriter<\/th>\n<th>Proprietary API (\u00d6rn: OpenAI GPT-4o)<\/th>\n<th>Self-Hosted GPU (\u00d6rn: vLLM + Llama 3 70B)<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td><strong>Ba\u015flang\u0131\u00e7 Maliyeti<\/strong><\/td>\n<td>S\u0131f\u0131r (Sadece kulland\u0131k\u00e7a \u00f6de)<\/td>\n<td>Y\u00fcksek (M\u00fchendislik ve kurulum maliyeti)<\/td>\n<\/tr>\n<tr>\n<td><strong>\u00d6l\u00e7eklenme Esnekli\u011fi<\/strong><\/td>\n<td>An\u0131nda otomatik \u00f6l\u00e7eklenme<\/td>\n<td>GPU kotas\u0131 ve tedarik zincirine ba\u011f\u0131ml\u0131<\/td>\n<\/tr>\n<tr>\n<td><strong>D\u00fc\u015f\u00fck Trafikte Maliyet<\/strong><\/td>\n<td>\u00c7ok d\u00fc\u015f\u00fck (\u0130stek yoksa \u00fccret yok)<\/td>\n<td>Y\u00fcksek (GPU sunucusu a\u00e7\u0131k bekler)<\/td>\n<\/tr>\n<tr>\n<td><strong>Y\u00fcksek Trafikte Maliyet<\/strong><\/td>\n<td>\u00c7ok y\u00fcksek (Lineer olarak artar)<\/td>\n<td>D\u00fc\u015f\u00fck (Birim token maliyeti d\u00fc\u015fer)<\/td>\n<\/tr>\n<tr>\n<td><strong>Veri Gizlili\u011fi ve Kontrol<\/strong><\/td>\n<td>\u00dc\u00e7\u00fcnc\u00fc taraf sa\u011flay\u0131c\u0131ya ba\u011f\u0131ml\u0131<\/td>\n<td>Tam kontrol, \u015firket i\u00e7i sunucular<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>\u00d6zetle, ayl\u0131k istek say\u0131n\u0131z belirli bir kritik e\u015fi\u011fi a\u015fmad\u0131\u011f\u0131 s\u00fcrece API kullan\u0131m\u0131 daha ucuzdur. Ancak kararl\u0131 ve y\u00fcksek hacimli bir trafi\u011fe ula\u015ft\u0131\u011f\u0131n\u0131zda, kendi GPU altyap\u0131n\u0131z\u0131 kurup optimize etmek maliyetlerinizi %60 ila %80 oran\u0131nda azaltabilir.<\/p>\n<h2>Donan\u0131m Optimizasyonu ile Inference Maliyetlerini Nas\u0131l D\u00fc\u015f\u00fcr\u00fcrs\u00fcn\u00fcz?<\/h2>\n<p>Kendi GPU sunucular\u0131n\u0131z\u0131 \u00e7al\u0131\u015ft\u0131rmaya karar verdiyseniz, standart k\u00fct\u00fcphaneleri varsay\u0131lan ayarlarla kullanmak b\u00fcy\u00fck bir finansal hatad\u0131r. Bir yapay zeka modelini do\u011frudan y\u00fckleyip \u00e7al\u0131\u015ft\u0131rmak, GPU kaynaklar\u0131n\u0131n yaln\u0131zca %10 ila %20&#8217;sinin verimli kullan\u0131lmas\u0131na neden olur. M\u00fchendislik ekibinizin uygulamas\u0131 gereken temel donan\u0131m ve yaz\u0131l\u0131m optimizasyon teknikleri \u015funlard\u0131r:<\/p>\n<p><strong>1. Kuantizasyon (Quantization):<\/strong> Model a\u011f\u0131rl\u0131klar\u0131n\u0131n hassasiyetini d\u00fc\u015f\u00fcrme i\u015flemidir. 16-bit float (FP16) olarak e\u011fitilmi\u015f bir modeli 8-bit (INT8) hatta 4-bit (INT4 veya AWQ\/GGUF) seviyesine d\u00f6n\u00fc\u015ft\u00fcrerek bellek gereksinimini yar\u0131 yar\u0131ya veya d\u00f6rtte birine d\u00fc\u015f\u00fcrebilirsiniz. Bu i\u015flem do\u011frulukta ihmal edilebilir bir kay\u0131p yarat\u0131rken, ayn\u0131 GPU \u00fczerinde 2 ila 4 kat daha b\u00fcy\u00fck modelleri \u00e7al\u0131\u015ft\u0131rman\u0131za veya daha b\u00fcy\u00fck batch boyutlar\u0131 kullanman\u0131za olanak tan\u0131r.<\/p>\n<p><strong>2. Continuous Batching ve PagedAttention:<\/strong> Geleneksel dinamik batching y\u00f6ntemlerinde, gruptaki en uzun yan\u0131t tamamlanana kadar di\u011fer istekler bekletilir. vLLM veya TGI (Text Generation Inference) gibi modern \u00e7\u0131kar\u0131m motorlar\u0131, PagedAttention tekni\u011fi sayesinde KV Cache belle\u011fini sanal bellek gibi y\u00f6netir ve Continuous Batching ile GPU&#8217;nun her d\u00f6ng\u00fcde %100 kapasiteyle \u00e7al\u0131\u015fmas\u0131n\u0131 sa\u011flar.<\/p>\n<p>A\u015fa\u011f\u0131da, vLLM k\u00fct\u00fcphanesi kullanarak y\u00fcksek verimlilikte ve kuantize edilmi\u015f bir modeli nas\u0131l canl\u0131ya alabilece\u011finizi g\u00f6steren \u00f6rnek bir Python kodu yer almaktad\u0131r:<\/p>\n<pre><code>from vllm import LLM, SamplingParams\n\n# AWQ 4-bit kuantize edilmi\u015f modeli y\u00fckl\u00fcyoruz.\n# Bu i\u015flem VRAM kullan\u0131m\u0131n\u0131 %60 azaltarak ayn\u0131 GPU'da daha fazla e\u015fzamanl\u0131 istek i\u015flemeyi sa\u011flar.\nllm = LLM(\n    model=\"TheBloke\/Llama-2-13B-Chat-AWQ\",\n    quantization=\"awq\",\n    tensor_parallel_size=1, # Tek GPU kullan\u0131m\u0131\n    gpu_memory_utilization=0.90, # VRAM'in %90'\u0131n\u0131 aktif optimizasyona ay\u0131r\u0131r\n    max_model_len=4096\n)\n\n# \u00c7\u0131kar\u0131m parametrelerini tan\u0131ml\u0131yoruz\nsampling_params = SamplingParams(\n    temperature=0.7,\n    top_p=0.95,\n    max_tokens=512\n)\n\n# E\u015fzamanl\u0131 toplu istekler g\u00f6nderiyoruz\nprompts = [\n    \"Yapay zeka sistemlerinde maliyet optimizasyonu nas\u0131l yap\u0131l\u0131r?\",\n    \"GPU VRAM y\u00f6netimi i\u00e7in en iyi pratikler nelerdir?\",\n    \"Continuous batching mimarisinin avantajlar\u0131 nelerdir?\"\n]\n\n# \u00c7\u0131kar\u0131m i\u015flemini ba\u015flat\u0131yoruz\noutputs = llm.generate(prompts, sampling_params)\n\nfor output in outputs:\n    prompt = output.prompt\n    generated_text = output.outputs[0].text\n    print(f\"\u0130stem: {prompt}\")\n    print(f\"Yan\u0131t: {generated_text}\\n\")\n<\/code><\/pre>\n<p>Bu kod \u00f6rne\u011finde g\u00f6r\u00fcld\u00fc\u011f\u00fc gibi, sadece kuantize edilmi\u015f bir modeli ve vLLM altyap\u0131s\u0131n\u0131 tercih etmek bile, standart HuggingFace Transformers k\u00fct\u00fcphanesine k\u0131yasla saniye ba\u015f\u0131na i\u015flenen token say\u0131s\u0131n\u0131 (throughput) 4 ila 8 kat art\u0131r\u0131r. Dolay\u0131s\u0131yla sunucu maliyetiniz do\u011frudan 4&#8217;te 1 oran\u0131na d\u00fc\u015fer.<\/p>\n<h2>Ger\u00e7ek D\u00fcnya Vaka Analizi: Ayl\u0131k 50.000$ Sunucu Faturas\u0131n\u0131 Nas\u0131l 8.000$&#8217;a \u0130ndirdik?<\/h2>\n<p>Teorik bilgileri somutla\u015ft\u0131rmak ad\u0131na, e-ticaret sekt\u00f6r\u00fcnde hizmet veren m\u00fc\u015fteri destek otomasyonu sunan bir SaaS \u015firketinin ya\u015fad\u0131\u011f\u0131 d\u00f6n\u00fc\u015f\u00fcm\u00fc inceleyelim. \u015eirket, kullan\u0131c\u0131lar\u0131na 7\/24 hizmet veren ve g\u00fcnl\u00fck ortalama 2 milyon m\u00fc\u015fteri mesaj\u0131n\u0131 yan\u0131tlayan bir LLM sistemine sahipti.<\/p>\n<p><strong>Ba\u015flang\u0131\u00e7 Durumu (Sorunlu Mimari):<\/strong> \u015eirket ilk a\u015famada Llama-2-70B modelini varsay\u0131lan FP16 hassasiyetinde, AWS \u00fczerinde 8 adet A10G GPU i\u00e7eren sunucularda \u00e7al\u0131\u015ft\u0131r\u0131yordu. Y\u00fcksek m\u00fc\u015fteri trafi\u011fi nedeniyle toplamda 12 adet bu b\u00fcy\u00fck sunucu \u00f6rne\u011finden (instance) kiralam\u0131\u015flard\u0131. Ayl\u0131k AWS faturas\u0131 yakla\u015f\u0131k 52.000$ civar\u0131ndayd\u0131. Ayr\u0131ca gece saatlerinde trafik d\u00fc\u015fmesine ra\u011fmen sunucular kapat\u0131lam\u0131yor, at\u0131l kapasite maliyeti katlan\u0131yordu.<\/p>\n<p><strong>Uygulanan M\u00fchendislik Ad\u0131mlar\u0131:<\/strong><\/p>\n<ul>\n<li><strong>Model De\u011fi\u015fimi ve Kuantizasyon:<\/strong> Llama-2-70B modeli, daha yeni ve verimli olan Llama-3-8B ve Llama-3-70B karmas\u0131 bir mimariye ge\u00e7irildi. Basit sorular 8B modeline, karma\u015f\u0131k sorular ise 70B AWQ (4-bit kuantize) modeline y\u00f6nlendirildi.<\/li>\n<li><strong>\u00c7\u0131kar\u0131m Motoru G\u00fcncellemesi:<\/strong> Standart kod yap\u0131s\u0131 b\u0131rak\u0131larak vLLM altyap\u0131s\u0131na ge\u00e7ildi. PagedAttention sayesinde VRAM darbo\u011faz\u0131 \u00e7\u00f6z\u00fcld\u00fc ve e\u015fzamanl\u0131 istek kapasitesi 5 kat\u0131na \u00e7\u0131kar\u0131ld\u0131.<\/li>\n<li><strong>Spot Instance ve Otomatik \u00d6l\u00e7eklendirme:<\/strong> Kubernetes tabanl\u0131 KEDA (Kubernetes Event-driven Autoscaling) entegre edildi. Gece saatlerinde sunucu say\u0131s\u0131 otomatik olarak azalt\u0131ld\u0131. Ayr\u0131ca yedekli mimari kurularak maliyeti %70 daha ucuz olan Bulut Spot GPU&#8217;lar kullan\u0131lmaya ba\u015fland\u0131.<\/li>\n<\/ul>\n<p><strong>Sonu\u00e7:<\/strong> \u015eirket, yan\u0131t s\u00fcrelerini (latency) ortalama 1.2 saniyeden 450 milisaniyeye d\u00fc\u015f\u00fcr\u00fcrken, ayl\u0131k GPU altyap\u0131 faturas\u0131n\u0131 52.000$&#8217;dan 8.400$&#8217;a indirmeyi ba\u015fard\u0131. Bu durum, yapay zeka uygulamalar\u0131nda do\u011fru m\u00fchendislik tercihlerinin do\u011frudan \u015firket karl\u0131l\u0131\u011f\u0131n\u0131 nas\u0131l etkiledi\u011finin net bir kan\u0131t\u0131d\u0131r.<\/p>\n<h2>Geli\u015fmi\u015f Sanalla\u015ft\u0131rma ve Otomatik \u00d6l\u00e7eklendirme Stratejileri Nas\u0131l Uygulan\u0131r?<\/h2>\n<p>Maliyeti minimum seviyede tutman\u0131n bir di\u011fer yolu, donan\u0131m kaynaklar\u0131n\u0131 mikro d\u00fczeyde payla\u015fmakt\u0131r. Bir\u00e7ok yapay zeka uygulamas\u0131nda, model her an t\u00fcm GPU g\u00fcc\u00fcn\u00fc kullanmaz. \u00d6zelikle k\u00fc\u00e7\u00fck boyutlu modeller \u00e7al\u0131\u015ft\u0131r\u0131yorsan\u0131z veya trafik dalgal\u0131ysa, NVIDIA MIG (Multi-Instance GPU) teknolojisini kullanabilirsiniz. MIG, tek bir fiziksel GPU&#8217;yu (\u00f6rne\u011fin bir A100) donan\u0131msal olarak izole edilmi\u015f 7 farkl\u0131 k\u00fc\u00e7\u00fck GPU par\u00e7as\u0131na b\u00f6lmenize olanak tan\u0131r. B\u00f6ylece tek bir GPU \u00fczerinde 7 farkl\u0131 mikro servisi g\u00fcvenle \u00e7al\u0131\u015ft\u0131rabilirsiniz.<\/p>\n<p>Bununla birlikte, kuyruk tabanl\u0131 otomatik \u00f6l\u00e7eklendirme (Queue-based Autoscaling) stratejileri uygulanmal\u0131d\u0131r. Geleneksel CPU kullan\u0131m\u0131na g\u00f6re \u00f6l\u00e7eklendirme yapay zeka y\u00fcklerinde \u00e7al\u0131\u015fmaz. CPU %100 olsa bile GPU bo\u015fta olabilir veya tam tersi durumlar ya\u015fanabilir. Bunun yerine, \u00e7\u0131kar\u0131m sunucunuzun \u00f6n\u00fcndeki istek kuyru\u011funun (queue depth) uzunlu\u011funa ve TTFT metriklerine bakarak \u00f6l\u00e7eklendirme yapmal\u0131s\u0131n\u0131z. Kuyrukta bekleyen istek say\u0131s\u0131 artt\u0131\u011f\u0131nda yeni GPU podlar\u0131 ba\u015flat\u0131lmal\u0131, kuyruk bo\u015fald\u0131\u011f\u0131nda ise sistem kademeli olarak s\u0131f\u0131ra yak\u0131n sunucuya (scale-to-zero) \u00e7ekilmelidir.<\/p>\n<p>Son olarak, Spek\u00fclatif \u00c7\u0131kar\u0131m (Speculative Decoding) gibi ileri d\u00fczey teknikler de\u011ferlendirilmelidir. Bu teknikte, k\u00fc\u00e7\u00fck ve ucuz bir &#8220;taslak model&#8221; (draft model) h\u0131zl\u0131ca birka\u00e7 token \u00fcretir, ard\u0131ndan b\u00fcy\u00fck ve pahal\u0131 ana model bu tokenlar\u0131 tek bir ge\u00e7i\u015fte do\u011frular. Bu sayede b\u00fcy\u00fck modelin \u00e7al\u0131\u015ft\u0131r\u0131lma say\u0131s\u0131 azal\u0131r ve \u00e7\u0131kar\u0131m h\u0131z\u0131 2 ila 3 kat artarken maliyetler d\u00fc\u015fer.<\/p>\n<h2>Sonu\u00e7 ve Yapay Zeka Maliyet Y\u00f6netimi Hakk\u0131nda S\u0131k\u00e7a Sorulan Sorular<\/h2>\n<p>\u00d6zetle, \u00fcretim ortam\u0131nda yapay zeka uygulamas\u0131 \u00e7al\u0131\u015ft\u0131rmak ciddi bir finansal disiplin gerektirir. Model se\u00e7imi, kuantizasyon, do\u011fru \u00e7\u0131kar\u0131m motoru tercihi ve ak\u0131ll\u0131 \u00f6l\u00e7eklendirme stratejileri bir araya geldi\u011finde maliyetlerinizi %80&#8217;e varan oranlarda azaltabilirsiniz. Yapay zeka projelerinde ba\u015far\u0131, yaln\u0131zca y\u00fcksek do\u011fruluk oranlar\u0131na ula\u015fmak de\u011fil, ayn\u0131 zamanda s\u00fcrd\u00fcr\u00fclebilir bir birim maliyet yap\u0131s\u0131 kurabilmektir.<\/p>\n<h3>Yapay zeka \u00e7\u0131kar\u0131m (inference) maliyetini d\u00fc\u015f\u00fcrmeye nereden ba\u015flamal\u0131y\u0131m?<\/h3>\n<p>\u0130lk ad\u0131m olarak modelinizi kuantize etmeyi (\u00f6rne\u011fin FP16&#8217;dan INT8 veya AWQ 4-bit seviyesine) ve \u00e7\u0131kar\u0131m motorunuzu vLLM veya TGI gibi PagedAttention destekleyen modern bir altyap\u0131ya ta\u015f\u0131may\u0131 deneyin. Bu de\u011fi\u015fiklikler genellikle kod mimarinizi bozmadan %50&#8217;den fazla tasarruf sa\u011flar.<\/p>\n<h3>Kuantizasyon i\u015flemi modelin yan\u0131t kalitesini bozar m\u0131?<\/h3>\n<p>Modern kuantizasyon teknikleri (AWQ, GPTQ, GGUF), \u00f6zellikle 8-bit ve 4-bit seviyelerinde insan g\u00f6z\u00fcyle fark edilemeyecek kadar k\u00fc\u00e7\u00fck kalite kay\u0131plar\u0131na yol a\u00e7ar. Bir\u00e7ok kullan\u0131m senaryosunda (\u00f6rne\u011fin metin \u00f6zetleme, sohbet botlar\u0131) do\u011fruluk kayb\u0131 %1&#8217;in alt\u0131ndad\u0131r ancak sa\u011flad\u0131\u011f\u0131 h\u0131z ve maliyet avantaj\u0131 muazzamd\u0131r.<\/p>\n<h3>API kullanmak m\u0131 yoksa kendi GPU sunucumu kiralamak m\u0131 daha mant\u0131kl\u0131?<\/h3>\n<p>Ayl\u0131k token t\u00fcketiminiz d\u00fc\u015f\u00fck ve trafi\u011finiz tahmin edilemez ise OpenAI, Anthropic gibi API sa\u011flay\u0131c\u0131lar\u0131n\u0131 kullanmak daha ekonomiktir. Ancak sabit, y\u00fcksek hacimli bir trafi\u011fe ula\u015ft\u0131\u011f\u0131n\u0131zda kendi GPU altyap\u0131n\u0131z\u0131 kurmak birim maliyetleri ciddi oranda d\u00fc\u015f\u00fcr\u00fcr.<\/p>\n<h3>GPU sunucular\u0131nda Spot Instance kullanmak g\u00fcvenli midir?<\/h3>\n<p>Do\u011fru yedekli altyap\u0131 mimarisi (fallback mechanisms) kuruldu\u011fu s\u00fcrece g\u00fcvenlidir. Spot sunucu kapand\u0131\u011f\u0131nda istekleri an\u0131nda iste\u011fe ba\u011fl\u0131 (On-Demand) sunuculara veya bir yedek API&#8217;ye y\u00f6nlendiren bir y\u00fck dengeleyici (load balancer) mant\u0131\u011f\u0131 kurgulayarak maliyetlerinizi %70 oran\u0131nda azaltabilirsiniz.<\/p>\n<div class=\"github-example-link\"><strong>\u00d6rnek kod:<\/strong> <a href=\"https:\/\/github.com\/fatihsoysalcom\/simulate-ai-inference-cost-optimization\" target=\"_blank\" rel=\"noopener noreferrer\">github.com\/fatihsoysalcom\/simulate-ai-inference-cost-optimization<\/a><\/div>\n","protected":false},"excerpt":{"rendered":"Production AI Uygulamalar\u0131n\u0131n Ger\u00e7ek Inference Maliyeti Yapay zeka modellerini canl\u0131ya alman\u0131n gizli maliyetlerini, GPU optimizasyonlar\u0131n\u0131 ve inference b\u00fct\u00e7enizi&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":[1342],"tags":[],"class_list":{"0":"post-43914","1":"post","2":"type-post","3":"status-publish","4":"format-standard","6":"category-ai","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>Production AI Uygulamalar\u0131n\u0131n Ger\u00e7ek Inference Maliyeti<\/title>\n<meta name=\"description\" content=\"Yapay zeka modellerini canl\u0131ya alman\u0131n gizli maliyetlerini, GPU optimizasyonlar\u0131n\u0131 ve inference b\u00fct\u00e7enizi d\u00fc\u015f\u00fcrme stratejilerini ke\u015ffedin.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/fatihsoysal.com\/blog\/production-ai-uygulamalarinin-gercek-inference-maliyeti\/\" \/>\n<meta property=\"og:locale\" content=\"tr_TR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Production AI Uygulamalar\u0131n\u0131n Ger\u00e7ek Inference Maliyeti\" \/>\n<meta property=\"og:description\" content=\"Yapay zeka modellerini canl\u0131ya alman\u0131n gizli maliyetlerini, GPU optimizasyonlar\u0131n\u0131 ve inference b\u00fct\u00e7enizi d\u00fc\u015f\u00fcrme stratejilerini ke\u015ffedin.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/fatihsoysal.com\/blog\/production-ai-uygulamalarinin-gercek-inference-maliyeti\/\" \/>\n<meta property=\"og:site_name\" content=\"Kodlar\u0131n Gizemli D\u00fcnyas\u0131\" \/>\n<meta property=\"article:published_time\" content=\"2026-08-07T11:00:51+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2026-08-07T11:01:30+00:00\" \/>\n<meta name=\"author\" content=\"Fatih Soysal\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Yazan:\" \/>\n\t<meta name=\"twitter:data1\" content=\"Fatih Soysal\" \/>\n\t<meta name=\"twitter:label2\" content=\"Tahmini okuma s\u00fcresi\" \/>\n\t<meta name=\"twitter:data2\" content=\"13 dakika\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\/\/fatihsoysal.com\/blog\/production-ai-uygulamalarinin-gercek-inference-maliyeti\/#article\",\"isPartOf\":{\"@id\":\"https:\/\/fatihsoysal.com\/blog\/production-ai-uygulamalarinin-gercek-inference-maliyeti\/\"},\"author\":{\"name\":\"Fatih Soysal\",\"@id\":\"https:\/\/fatihsoysal.com\/blog\/#\/schema\/person\/002a254750921dcfd568a99e48240dd1\"},\"headline\":\"Production AI Uygulamalar\u0131n\u0131n Ger\u00e7ek Inference Maliyeti\",\"datePublished\":\"2026-08-07T11:00:51+00:00\",\"dateModified\":\"2026-08-07T11:01:30+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\/\/fatihsoysal.com\/blog\/production-ai-uygulamalarinin-gercek-inference-maliyeti\/\"},\"wordCount\":2483,\"commentCount\":0,\"publisher\":{\"@id\":\"https:\/\/fatihsoysal.com\/blog\/#\/schema\/person\/002a254750921dcfd568a99e48240dd1\"},\"articleSection\":[\"AI\"],\"inLanguage\":\"tr\",\"potentialAction\":[{\"@type\":\"CommentAction\",\"name\":\"Comment\",\"target\":[\"https:\/\/fatihsoysal.com\/blog\/production-ai-uygulamalarinin-gercek-inference-maliyeti\/#respond\"]}],\"copyrightYear\":\"2026\",\"copyrightHolder\":{\"@id\":\"https:\/\/fatihsoysal.com\/blog\/#organization\"}},{\"@type\":\"WebPage\",\"@id\":\"https:\/\/fatihsoysal.com\/blog\/production-ai-uygulamalarinin-gercek-inference-maliyeti\/\",\"url\":\"https:\/\/fatihsoysal.com\/blog\/production-ai-uygulamalarinin-gercek-inference-maliyeti\/\",\"name\":\"Production AI Uygulamalar\u0131n\u0131n Ger\u00e7ek Inference Maliyeti\",\"isPartOf\":{\"@id\":\"https:\/\/fatihsoysal.com\/blog\/#website\"},\"datePublished\":\"2026-08-07T11:00:51+00:00\",\"dateModified\":\"2026-08-07T11:01:30+00:00\",\"description\":\"Yapay zeka modellerini canl\u0131ya alman\u0131n gizli maliyetlerini, GPU optimizasyonlar\u0131n\u0131 ve inference b\u00fct\u00e7enizi d\u00fc\u015f\u00fcrme stratejilerini ke\u015ffedin.\",\"breadcrumb\":{\"@id\":\"https:\/\/fatihsoysal.com\/blog\/production-ai-uygulamalarinin-gercek-inference-maliyeti\/#breadcrumb\"},\"inLanguage\":\"tr\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/fatihsoysal.com\/blog\/production-ai-uygulamalarinin-gercek-inference-maliyeti\/\"]}]},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\/\/fatihsoysal.com\/blog\/production-ai-uygulamalarinin-gercek-inference-maliyeti\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Anasayfa\",\"item\":\"https:\/\/fatihsoysal.com\/blog\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Production AI Uygulamalar\u0131n\u0131n Ger\u00e7ek Inference Maliyeti\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\/\/fatihsoysal.com\/blog\/#website\",\"url\":\"https:\/\/fatihsoysal.com\/blog\/\",\"name\":\"Fatihsoysal.com\",\"description\":\"Blog - Yaz\u0131l\u0131m D\u00fcnyas\u0131 Tecr\u00fcbelerim\",\"publisher\":{\"@id\":\"https:\/\/fatihsoysal.com\/blog\/#\/schema\/person\/002a254750921dcfd568a99e48240dd1\"},\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\/\/fatihsoysal.com\/blog\/?s={search_term_string}\"},\"query-input\":{\"@type\":\"PropertyValueSpecification\",\"valueRequired\":true,\"valueName\":\"search_term_string\"}}],\"inLanguage\":\"tr\"},{\"@type\":[\"Person\",\"Organization\"],\"@id\":\"https:\/\/fatihsoysal.com\/blog\/#\/schema\/person\/002a254750921dcfd568a99e48240dd1\",\"name\":\"Fatih Soysal\",\"image\":{\"@type\":\"ImageObject\",\"inLanguage\":\"tr\",\"@id\":\"https:\/\/fatihsoysal.com\/blog\/#\/schema\/person\/image\/\",\"url\":\"https:\/\/fatihsoysal.com\/blog\/wp-content\/uploads\/2024\/04\/cropped-replicate-prediction-3kgg1hgjn5rgp0cf0p5tr0jw7w-1.png\",\"contentUrl\":\"https:\/\/fatihsoysal.com\/blog\/wp-content\/uploads\/2024\/04\/cropped-replicate-prediction-3kgg1hgjn5rgp0cf0p5tr0jw7w-1.png\",\"width\":512,\"height\":512,\"caption\":\"Fatih Soysal\"},\"logo\":{\"@id\":\"https:\/\/fatihsoysal.com\/blog\/#\/schema\/person\/image\/\"},\"description\":\"Kullan\u0131m ve kodlama m\u00fckemmeliyetini odak alan uygulamalar olu\u015fturma deneyimine sahip, profesyonel olarak 15+ y\u0131l \u00fczeri deneyime sahip bir yaz\u0131l\u0131m m\u00fchendisi.\",\"url\":\"https:\/\/fatihsoysal.com\/blog\/author\/fatihsoysal\/\"}]}<\/script>\n<!-- \/ Yoast SEO Premium plugin. -->","yoast_head_json":{"title":"Production AI Uygulamalar\u0131n\u0131n Ger\u00e7ek Inference Maliyeti","description":"Yapay zeka modellerini canl\u0131ya alman\u0131n gizli maliyetlerini, GPU optimizasyonlar\u0131n\u0131 ve inference b\u00fct\u00e7enizi d\u00fc\u015f\u00fcrme stratejilerini ke\u015ffedin.","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/fatihsoysal.com\/blog\/production-ai-uygulamalarinin-gercek-inference-maliyeti\/","og_locale":"tr_TR","og_type":"article","og_title":"Production AI Uygulamalar\u0131n\u0131n Ger\u00e7ek Inference Maliyeti","og_description":"Yapay zeka modellerini canl\u0131ya alman\u0131n gizli maliyetlerini, GPU optimizasyonlar\u0131n\u0131 ve inference b\u00fct\u00e7enizi d\u00fc\u015f\u00fcrme stratejilerini ke\u015ffedin.","og_url":"https:\/\/fatihsoysal.com\/blog\/production-ai-uygulamalarinin-gercek-inference-maliyeti\/","og_site_name":"Kodlar\u0131n Gizemli D\u00fcnyas\u0131","article_published_time":"2026-08-07T11:00:51+00:00","article_modified_time":"2026-08-07T11:01:30+00:00","author":"Fatih Soysal","twitter_card":"summary_large_image","twitter_misc":{"Yazan:":"Fatih Soysal","Tahmini okuma s\u00fcresi":"13 dakika"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"Article","@id":"https:\/\/fatihsoysal.com\/blog\/production-ai-uygulamalarinin-gercek-inference-maliyeti\/#article","isPartOf":{"@id":"https:\/\/fatihsoysal.com\/blog\/production-ai-uygulamalarinin-gercek-inference-maliyeti\/"},"author":{"name":"Fatih Soysal","@id":"https:\/\/fatihsoysal.com\/blog\/#\/schema\/person\/002a254750921dcfd568a99e48240dd1"},"headline":"Production AI Uygulamalar\u0131n\u0131n Ger\u00e7ek Inference Maliyeti","datePublished":"2026-08-07T11:00:51+00:00","dateModified":"2026-08-07T11:01:30+00:00","mainEntityOfPage":{"@id":"https:\/\/fatihsoysal.com\/blog\/production-ai-uygulamalarinin-gercek-inference-maliyeti\/"},"wordCount":2483,"commentCount":0,"publisher":{"@id":"https:\/\/fatihsoysal.com\/blog\/#\/schema\/person\/002a254750921dcfd568a99e48240dd1"},"articleSection":["AI"],"inLanguage":"tr","potentialAction":[{"@type":"CommentAction","name":"Comment","target":["https:\/\/fatihsoysal.com\/blog\/production-ai-uygulamalarinin-gercek-inference-maliyeti\/#respond"]}],"copyrightYear":"2026","copyrightHolder":{"@id":"https:\/\/fatihsoysal.com\/blog\/#organization"}},{"@type":"WebPage","@id":"https:\/\/fatihsoysal.com\/blog\/production-ai-uygulamalarinin-gercek-inference-maliyeti\/","url":"https:\/\/fatihsoysal.com\/blog\/production-ai-uygulamalarinin-gercek-inference-maliyeti\/","name":"Production AI Uygulamalar\u0131n\u0131n Ger\u00e7ek Inference Maliyeti","isPartOf":{"@id":"https:\/\/fatihsoysal.com\/blog\/#website"},"datePublished":"2026-08-07T11:00:51+00:00","dateModified":"2026-08-07T11:01:30+00:00","description":"Yapay zeka modellerini canl\u0131ya alman\u0131n gizli maliyetlerini, GPU optimizasyonlar\u0131n\u0131 ve inference b\u00fct\u00e7enizi d\u00fc\u015f\u00fcrme stratejilerini ke\u015ffedin.","breadcrumb":{"@id":"https:\/\/fatihsoysal.com\/blog\/production-ai-uygulamalarinin-gercek-inference-maliyeti\/#breadcrumb"},"inLanguage":"tr","potentialAction":[{"@type":"ReadAction","target":["https:\/\/fatihsoysal.com\/blog\/production-ai-uygulamalarinin-gercek-inference-maliyeti\/"]}]},{"@type":"BreadcrumbList","@id":"https:\/\/fatihsoysal.com\/blog\/production-ai-uygulamalarinin-gercek-inference-maliyeti\/#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"Anasayfa","item":"https:\/\/fatihsoysal.com\/blog\/"},{"@type":"ListItem","position":2,"name":"Production AI Uygulamalar\u0131n\u0131n Ger\u00e7ek Inference Maliyeti"}]},{"@type":"WebSite","@id":"https:\/\/fatihsoysal.com\/blog\/#website","url":"https:\/\/fatihsoysal.com\/blog\/","name":"Fatihsoysal.com","description":"Blog - Yaz\u0131l\u0131m D\u00fcnyas\u0131 Tecr\u00fcbelerim","publisher":{"@id":"https:\/\/fatihsoysal.com\/blog\/#\/schema\/person\/002a254750921dcfd568a99e48240dd1"},"potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/fatihsoysal.com\/blog\/?s={search_term_string}"},"query-input":{"@type":"PropertyValueSpecification","valueRequired":true,"valueName":"search_term_string"}}],"inLanguage":"tr"},{"@type":["Person","Organization"],"@id":"https:\/\/fatihsoysal.com\/blog\/#\/schema\/person\/002a254750921dcfd568a99e48240dd1","name":"Fatih Soysal","image":{"@type":"ImageObject","inLanguage":"tr","@id":"https:\/\/fatihsoysal.com\/blog\/#\/schema\/person\/image\/","url":"https:\/\/fatihsoysal.com\/blog\/wp-content\/uploads\/2024\/04\/cropped-replicate-prediction-3kgg1hgjn5rgp0cf0p5tr0jw7w-1.png","contentUrl":"https:\/\/fatihsoysal.com\/blog\/wp-content\/uploads\/2024\/04\/cropped-replicate-prediction-3kgg1hgjn5rgp0cf0p5tr0jw7w-1.png","width":512,"height":512,"caption":"Fatih Soysal"},"logo":{"@id":"https:\/\/fatihsoysal.com\/blog\/#\/schema\/person\/image\/"},"description":"Kullan\u0131m ve kodlama m\u00fckemmeliyetini odak alan uygulamalar olu\u015fturma deneyimine sahip, profesyonel olarak 15+ y\u0131l \u00fczeri deneyime sahip bir yaz\u0131l\u0131m m\u00fchendisi.","url":"https:\/\/fatihsoysal.com\/blog\/author\/fatihsoysal\/"}]}},"yoast_meta":{"yoast_wpseo_title":"","yoast_wpseo_metadesc":"","yoast_wpseo_canonical":""},"amp_enabled":true,"_links":{"self":[{"href":"https:\/\/fatihsoysal.com\/blog\/wp-json\/wp\/v2\/posts\/43914","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/fatihsoysal.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/fatihsoysal.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/fatihsoysal.com\/blog\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/fatihsoysal.com\/blog\/wp-json\/wp\/v2\/comments?post=43914"}],"version-history":[{"count":1,"href":"https:\/\/fatihsoysal.com\/blog\/wp-json\/wp\/v2\/posts\/43914\/revisions"}],"predecessor-version":[{"id":43915,"href":"https:\/\/fatihsoysal.com\/blog\/wp-json\/wp\/v2\/posts\/43914\/revisions\/43915"}],"wp:attachment":[{"href":"https:\/\/fatihsoysal.com\/blog\/wp-json\/wp\/v2\/media?parent=43914"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/fatihsoysal.com\/blog\/wp-json\/wp\/v2\/categories?post=43914"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/fatihsoysal.com\/blog\/wp-json\/wp\/v2\/tags?post=43914"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}