{"id":41513,"date":"2026-04-30T14:01:44","date_gmt":"2026-04-30T11:01:44","guid":{"rendered":"https:\/\/fatihsoysal.com\/blog\/yapay-zeka-cikariminda-inference-temel-kavramlar-nelerdir\/"},"modified":"2026-04-30T14:01:44","modified_gmt":"2026-04-30T11:01:44","slug":"yapay-zeka-cikariminda-inference-temel-kavramlar-nelerdir","status":"publish","type":"post","link":"https:\/\/fatihsoysal.com\/blog\/yapay-zeka-cikariminda-inference-temel-kavramlar-nelerdir\/","title":{"rendered":"Yapay Zeka \u00c7\u0131kar\u0131m\u0131nda (Inference) Temel Kavramlar Nelerdir?"},"content":{"rendered":"Dedicated vs Serverless Inference: \u00d6l\u00e7eklendirme Rehberi<\/title><\/p>\n<p>Yapay zeka modellerini \u00fcretim ortam\u0131na ta\u015f\u0131rken kar\u015f\u0131la\u015f\u0131lan en b\u00fcy\u00fck ikilem, altyap\u0131 se\u00e7imidir. Dedicated ve Serverless Inference aras\u0131ndaki farklar\u0131 anlamak, hem maliyetlerinizi optimize etmenizi sa\u011flar hem de kullan\u0131c\u0131 deneyimini do\u011frudan etkileyen gecikme s\u00fcrelerini minimize eder. Bu rehberde, projenizin \u00f6l\u00e7e\u011fine g\u00f6re hangi y\u00f6ntemi se\u00e7meniz gerekti\u011fini derinlemesine inceleyece\u011fiz.<\/p>\n<h2>Yapay Zeka \u00c7\u0131kar\u0131m\u0131nda (Inference) Temel Kavramlar Nelerdir?<\/h2>\n<p>Yapay zeka d\u00fcnyas\u0131na yeni ad\u0131m atanlar veya altyap\u0131 taraf\u0131nda stratejik kararlar almak zorunda olan y\u00f6neticiler i\u00e7in “inference” yani \u00e7\u0131kar\u0131m s\u00fcreci, e\u011fitilmi\u015f bir modelin canl\u0131 verilerle tahmin \u00fcretmesi anlam\u0131na gelir. Bir model e\u011fitildikten sonra, onu kullan\u0131c\u0131lara sunmak i\u00e7in bir sunucuya ihtiya\u00e7 duyars\u0131n\u0131z. \u0130\u015fte bu noktada kar\u015f\u0131m\u0131za iki ana yol ayr\u0131m\u0131 \u00e7\u0131kar: Dedicated (Tahsis Edilmi\u015f) ve Serverless (Sunucusuz) mimariler.<\/p>\n<p>Dedicated mimaride, sizin i\u00e7in 7\/24 \u00e7al\u0131\u015fan, GPU (Grafik \u0130\u015flem Birimi) veya CPU kaynaklar\u0131 ayr\u0131lm\u0131\u015f \u00f6zel bir sunucu bulunur. Bu sunucu, trafik gelse de gelmese de sizin i\u00e7in haz\u0131r bekler. \u00d6te yandan, Serverless mimaride fiziksel sunucu y\u00f6netimiyle u\u011fra\u015fmazs\u0131n\u0131z. Sadece bir istek (request) geldi\u011finde kaynaklar tetiklenir, i\u015flem yap\u0131l\u0131r ve i\u015f bitti\u011finde kaynaklar serbest b\u0131rak\u0131l\u0131r. Bu temel ayr\u0131m, \u00f6zellikle \u00f6l\u00e7eklendirme a\u015famas\u0131nda maliyet ve performans dengesini belirleyen en kritik fakt\u00f6rd\u00fcr.<\/p>\n<p>Bunun yan\u0131 s\u0131ra, \u00e7\u0131kar\u0131m s\u00fcreci sadece donan\u0131mla ilgili de\u011fildir. Modelin bellekte kaplad\u0131\u011f\u0131 alan, giri\u015f (input) ve \u00e7\u0131k\u0131\u015f (output) verilerinin boyutu ve e\u015fzamanl\u0131 kullan\u0131c\u0131 say\u0131s\u0131 gibi de\u011fi\u015fkenler, hangi mimarinin daha verimli oldu\u011funu belirler. \u00d6rne\u011fin, devasa bir Large Language Model (LLM) kullan\u0131yorsan\u0131z, Serverless sistemlerdeki “cold start” (so\u011fuk ba\u015flatma) problemleri can\u0131n\u0131z\u0131 s\u0131kabilir. Ancak, seyrek kullan\u0131lan bir g\u00f6r\u00fcnt\u00fc i\u015fleme modeliniz varsa, Dedicated bir sunucuya her ay binlerce dolar \u00f6demek mant\u0131ks\u0131z olacakt\u0131r.<\/p>\n<h2>Dedicated Inference Nedir ve Hangi Avantajlar\u0131 Sunar?<\/h2>\n<p>Dedicated Inference, genellikle “Provisioned Throughput” olarak da adland\u0131r\u0131l\u0131r. Bu modelde, belirli bir i\u015flem g\u00fcc\u00fcn\u00fc (\u00f6rne\u011fin iki adet NVIDIA A100 GPU) belirli bir s\u00fcre i\u00e7in kiralars\u0131n\u0131z. Bu altyap\u0131 tamamen size aittir. Peki, neden bu kadar y\u00fcksek maliyetli bir yolu tercih etmelisiniz? Bunun temel sebebi \u00f6ng\u00f6r\u00fclebilirlik ve performanst\u0131r.<\/p>\n<p>\u00d6zellikle y\u00fcksek trafikli uygulamalarda, Dedicated sunucular birim i\u015flem ba\u015f\u0131na en d\u00fc\u015f\u00fck maliyeti sunar. E\u011fer saniyede y\u00fczlerce istek al\u0131yorsan\u0131z, sunucunuz zaten tam kapasite \u00e7al\u0131\u015f\u0131yor demektir. Bu durumda, her istek i\u00e7in ayr\u0131 bir fatura \u00f6demek yerine, sabit bir ayl\u0131k \u00fccret \u00f6demek \u00e7ok daha ekonomiktir. Ayr\u0131ca, gecikme (latency) s\u00fcreleri olduk\u00e7a stabildir. Sunucunuz her zaman “s\u0131cak” oldu\u011fu i\u00e7in, gelen istekler milisaniyeler i\u00e7inde i\u015flenmeye ba\u015flar.<\/p>\n<p>Buna ek olarak, Dedicated altyap\u0131lar \u00fczerinde tam kontrol sahibisinizdir. Modelinizi optimize etmek i\u00e7in CUDA s\u00fcr\u00fcc\u00fclerini g\u00fcncelleyebilir, \u00f6zel k\u00fct\u00fcphaneler y\u00fckleyebilir veya bellek y\u00f6netimini en ince ayr\u0131nt\u0131s\u0131na kadar yap\u0131land\u0131rabilirsiniz. G\u00fcvenlik a\u00e7\u0131s\u0131ndan da, verilerinizin izole bir ortamda i\u015flenmesi, reg\u00fclasyonlara tabi olan finans veya sa\u011fl\u0131k gibi sekt\u00f6rler i\u00e7in b\u00fcy\u00fck bir avantajd\u0131r. Ancak, bu \u00f6zg\u00fcrl\u00fc\u011f\u00fcn bedeli, trafik d\u00fc\u015ft\u00fc\u011f\u00fcnde bile \u00f6demeye devam etti\u011finiz faturalard\u0131r.<\/p>\n<pre><code>\n\/\/ \u00d6rnek bir Dedicated Sunucu Yap\u0131land\u0131rma Mant\u0131\u011f\u0131 (Pseudo Code)\nconst cluster = new DedicatedCluster({\n gpuType: \"NVIDIA-H100\",\n instanceCount: 3,\n autoScaling: false, \/\/ Kaynaklar her zaman aktif\n region: \"us-east-1\"\n});\n\nconsole.log(\"Sunucu haz\u0131r ve istekleri bekliyor...\");\n<\/code><\/pre>\n<h2>Serverless Inference Nas\u0131l \u00c7al\u0131\u015f\u0131r ve Neden Pop\u00fclerdir?<\/h2>\n<p>Serverless Inference, bulut bili\u015fimin “kulland\u0131\u011f\u0131n kadar \u00f6de” mant\u0131\u011f\u0131n\u0131 yapay zeka modellerine ta\u015f\u0131r. AWS Lambda, Google Cloud Functions veya daha spesifik olarak Modal, Replicate ve Hugging Face Inference Endpoints gibi platformlar bu hizmeti sunar. Burada odak noktas\u0131, altyap\u0131 y\u00f6netimi de\u011fil, do\u011frudan modelin kendisidir. Geli\u015ftirici sadece modeli y\u00fckler ve bir API u\u00e7 noktas\u0131 al\u0131r.<\/p>\n<p>Bu mimarinin en b\u00fcy\u00fck \u00e7ekicili\u011fi, “s\u0131f\u0131ra \u00f6l\u00e7eklenme” (scale to zero) yetene\u011fidir. E\u011fer uygulaman\u0131z gece saatlerinde hi\u00e7 trafik alm\u0131yorsa, hi\u00e7bir \u00fccret \u00f6demezsiniz. Trafik aniden y\u00fckseldi\u011finde ise bulut sa\u011flay\u0131c\u0131s\u0131 sizin yerinize yeni instance’lar aya\u011fa kald\u0131r\u0131r. Bu durum, \u00f6zellikle yeni kurulan giri\u015fimler (startuplar) ve prototip a\u015famas\u0131ndaki projeler i\u00e7in can kurtar\u0131c\u0131d\u0131r. Altyap\u0131 m\u00fchendisli\u011fi i\u00e7in harcayaca\u011f\u0131n\u0131z zaman\u0131, modelinizi iyile\u015ftirmeye harcayabilirsiniz.<\/p>\n<p>Ancak, Serverless d\u00fcnyas\u0131n\u0131n en b\u00fcy\u00fck d\u00fc\u015fman\u0131 “Cold Start” problemidir. Bir model uzun s\u00fcre istek almad\u0131\u011f\u0131nda, bulut sa\u011flay\u0131c\u0131s\u0131 kaynaklar\u0131 geri \u00e7eker. Yeni bir istek geldi\u011finde, modelin diskten belle\u011fe y\u00fcklenmesi, k\u00fct\u00fcphanelerin ba\u015flat\u0131lmas\u0131 ve GPU’nun haz\u0131rlanmas\u0131 saniyeler s\u00fcrebilir. Bu durum, ger\u00e7ek zamanl\u0131 yan\u0131t bekleyen kullan\u0131c\u0131lar i\u00e7in k\u00f6t\u00fc bir deneyim yaratabilir. Bu nedenle, Serverless se\u00e7imi yaparken model boyutunu ve ba\u015flatma s\u00fcrelerini mutlaka g\u00f6z \u00f6n\u00fcnde bulundurmal\u0131s\u0131n\u0131z.<\/p>\n<h2>\u00d6l\u00e7eklendirme S\u0131ras\u0131nda Kar\u015f\u0131la\u015f\u0131lan Temel Zorluklar Nelerdir?<\/h2>\n<p>Uygulaman\u0131z b\u00fcy\u00fcd\u00fck\u00e7e, ba\u015flang\u0131\u00e7ta verdi\u011finiz kararlar\u0131n sonu\u00e7lar\u0131yla y\u00fczle\u015fmeye ba\u015flars\u0131n\u0131z. \u00d6l\u00e7eklendirme sadece daha fazla sunucu eklemek de\u011fildir; bu s\u00fcre\u00e7te maliyet, karma\u015f\u0131kl\u0131k ve performans aras\u0131nda hassas bir denge kurman\u0131z gerekir. Bir\u00e7ok ekip, trafik artt\u0131\u011f\u0131nda Serverless faturalar\u0131n\u0131n kontrols\u00fczce y\u00fckseldi\u011fini veya Dedicated sunucular\u0131n ani trafik y\u00fcklerini kald\u0131ramay\u0131p \u00e7\u00f6kt\u00fc\u011f\u00fcn\u00fc g\u00f6r\u00fcr.<\/p>\n<p>Bunun yan\u0131 s\u0131ra, GPU stok sorunlar\u0131 da b\u00fcy\u00fck bir engeldir. D\u00fcnya genelindeki GPU k\u0131tl\u0131\u011f\u0131 nedeniyle, ihtiyac\u0131n\u0131z oldu\u011fu anda Dedicated bir sunucu bulamayabilirsiniz. Serverless sa\u011flay\u0131c\u0131lar\u0131 bu riski sizin yerinize y\u00f6netir ancak onlar da yo\u011fun saatlerde “rate limit” (istek s\u0131n\u0131rlamas\u0131) uygulayabilirler. Bu durum, uygulaman\u0131z\u0131n kesintiye u\u011framas\u0131na neden olabilir.<\/p>\n<p>Di\u011fer bir zorluk ise veri transferi ve bant geni\u015fli\u011fi maliyetleridir. Modeliniz \u00e7ok b\u00fcy\u00fckse (\u00f6rne\u011fin 70B parametreli bir model), bu modelin farkl\u0131 b\u00f6lgeler aras\u0131nda ta\u015f\u0131nmas\u0131 veya her istekte b\u00fcy\u00fck miktarda verinin i\u015flenmesi ciddi maliyetler do\u011furur. \u00d6l\u00e7eklendirme stratejinizi belirlerken, sadece i\u015flem g\u00fcc\u00fcn\u00fc de\u011fil, verinin yolculu\u011funu da planlamal\u0131s\u0131n\u0131z. \u00d6zellikle \u00e7ok b\u00f6lgeli (multi-region) bir yap\u0131 kuruyorsan\u0131z, veri tutarl\u0131l\u0131\u011f\u0131 ve gecikme s\u00fcreleri daha da karma\u015f\u0131k hale gelir.<\/p>\n<table>\n<thead>\n<tr>\n<th>\u00d6zellik<\/th>\n<th>Dedicated Inference<\/th>\n<th>Serverless Inference<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Maliyet Yap\u0131s\u0131<\/td>\n<td>Sabit Ayl\u0131k \u00dccret<\/td>\n<td>\u0130stek Ba\u015f\u0131na \u00d6deme<\/td>\n<\/tr>\n<tr>\n<td>Ba\u015flatma S\u00fcresi<\/td>\n<td>An\u0131nda (Her zaman s\u0131cak)<\/td>\n<td>Cold Start riski var<\/td>\n<\/tr>\n<tr>\n<td>Y\u00f6netim Y\u00fck\u00fc<\/td>\n<td>Y\u00fcksek (DevOps gerektirir)<\/td>\n<td>D\u00fc\u015f\u00fck (Managed servis)<\/td>\n<\/tr>\n<tr>\n<td>\u00d6l\u00e7eklenebilirlik<\/td>\n<td>Manuel veya Yava\u015f Auto-scale<\/td>\n<td>Otomatik ve H\u0131zl\u0131<\/td>\n<\/tr>\n<tr>\n<td>Donan\u0131m Kontrol\u00fc<\/td>\n<td>Tam Kontrol<\/td>\n<td>K\u0131s\u0131tl\u0131 \/ Sa\u011flay\u0131c\u0131ya Ba\u011fl\u0131<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2>Vaka Analizi: Bir Startup’\u0131n Serverless’tan Dedicated’a Ge\u00e7i\u015f Hikayesi<\/h2>\n<p>Ger\u00e7ek bir senaryoyu ele alal\u0131m. “AI-PhotoGen” ad\u0131nda, kullan\u0131c\u0131lar\u0131n metinlerden g\u00f6rsel olu\u015fturmas\u0131n\u0131 sa\u011flayan bir giri\u015fim d\u00fc\u015f\u00fcnelim. \u0130lk a\u015famada, kullan\u0131c\u0131 say\u0131lar\u0131 belirsiz oldu\u011fu i\u00e7in Serverless bir yap\u0131 kulland\u0131lar. Bu sayede, ilk 3 ay boyunca sadece 150 dolar gibi c\u00fczi bir altyap\u0131 \u00fccreti \u00f6dediler. \u00c7\u00fcnk\u00fc trafik d\u00fczensizdi ve reklam kampanyalar\u0131 d\u0131\u015f\u0131nda sistem \u00e7o\u011fu zaman bo\u015fta kal\u0131yordu.<\/p>\n<p>Bununla birlikte, uygulama sosyal medyada viral hale gelince i\u015fler de\u011fi\u015fti. G\u00fcnl\u00fck aktif kullan\u0131c\u0131 say\u0131s\u0131 50.000’e f\u0131rlad\u0131. Serverless faturas\u0131 bir ayda 12.000 dolara ula\u015ft\u0131. Ekip, yapt\u0131klar\u0131 analizde, e\u011fer ayn\u0131 trafi\u011fi Dedicated GPU sunucular\u0131 ile kar\u015f\u0131lasalard\u0131 maliyetin 4.500 dolar civar\u0131nda kalaca\u011f\u0131n\u0131 fark etti. Ayr\u0131ca, Serverless sistemdeki cold start nedeniyle kullan\u0131c\u0131lar bazen g\u00f6rselin olu\u015fmas\u0131 i\u00e7in 15 saniye beklemek zorunda kal\u0131yordu.<\/p>\n<p>Sonu\u00e7 olarak, ekip hibrit bir modele ge\u00e7i\u015f yapt\u0131. Temel trafik y\u00fck\u00fcn\u00fc (base load) kar\u015f\u0131lamak i\u00e7in 3 adet Dedicated GPU sunucusu kiralad\u0131lar. Beklenmedik trafik patlamalar\u0131n\u0131 (burst traffic) y\u00f6netmek i\u00e7in ise Serverless yap\u0131s\u0131n\u0131 yedek olarak tutmaya devam ettiler. Bu strateji sayesinde hem maliyetleri %50 oran\u0131nda d\u00fc\u015f\u00fcrd\u00fcler hem de ortalama yan\u0131t s\u00fcresini 8 saniyeden 3 saniyeye indirdiler. Bu vaka, \u00f6l\u00e7ek b\u00fcy\u00fcd\u00fck\u00e7e esnekli\u011fin yerini verimlili\u011fe b\u0131rakmas\u0131 gerekti\u011fini kan\u0131tl\u0131yor.<\/p>\n<h2>\u0130leri D\u00fczey Kullan\u0131c\u0131lar \u0130\u00e7in Optimizasyon \u0130pu\u00e7lar\u0131<\/h2>\n<p>E\u011fer altyap\u0131n\u0131z\u0131 bir \u00fcst seviyeye ta\u015f\u0131mak istiyorsan\u0131z, sadece mimari se\u00e7imiyle yetinmemelisiniz. Model optimizasyon teknikleri, hangi altyap\u0131y\u0131 kullan\u0131rsan\u0131z kullan\u0131n verimlili\u011fi art\u0131racakt\u0131r. \u0130lk olarak, “Quantization” (Niceleme) tekniklerini mutlaka inceleyin. 16-bit veya 32-bit a\u011f\u0131rl\u0131klarla \u00e7al\u0131\u015fan bir modeli 8-bit hatta 4-bit seviyesine indirmek, bellek kullan\u0131m\u0131n\u0131 yar\u0131 yar\u0131ya azalt\u0131rken \u00e7\u0131kar\u0131m h\u0131z\u0131n\u0131 iki kat\u0131na \u00e7\u0131karabilir.<\/p>\n<p>\u00d6zellikle dikkat etmeniz gereken bir di\u011fer konu ise “Dynamic Batching” y\u00f6ntemidir. Dedicated bir sunucunuz varsa, gelen istekleri tek tek i\u015flemek yerine k\u00fc\u00e7\u00fck gruplar (batch) halinde i\u015flemek GPU verimlili\u011fini maksimize eder. \u00d6rne\u011fin, birer milisaniye arayla gelen 10 iste\u011fi ayr\u0131 ayr\u0131 i\u015flemek yerine, 10 milisaniye bekleyip onunu birden tek seferde GPU’ya g\u00f6ndermek, toplam i\u015flem s\u00fcresini k\u0131salt\u0131r. Bir\u00e7ok modern \u00e7\u0131kar\u0131m sunucusu (NVIDIA Triton veya TGI gibi) bu \u00f6zelli\u011fi otomatik olarak sunar.<\/p>\n<p>Ayr\u0131ca, “Caching” (\u00d6nbelle\u011fe Alma) stratejilerini hafife almay\u0131n. E\u011fer kullan\u0131c\u0131lar\u0131n\u0131z s\u0131k s\u0131k benzer sorular soruyorsa veya benzer g\u00f6rseller talep ediyorsa, bu sonu\u00e7lar\u0131 bir Redis veritaban\u0131nda saklayarak GPU’ya hi\u00e7 y\u00fck bindirmeden yan\u0131t verebilirsiniz. Bu, hem Serverless maliyetlerini d\u00fc\u015f\u00fcr\u00fcr hem de Dedicated sunucular \u00fczerindeki y\u00fck\u00fc hafifletir. Unutmay\u0131n, en ucuz ve en h\u0131zl\u0131 \u00e7\u0131kar\u0131m, hi\u00e7 yap\u0131lmayan \u00e7\u0131kar\u0131md\u0131r.<\/p>\n<pre><code>\n\/\/ Basit bir \u00d6nbellek ve \u00c7\u0131kar\u0131m Ak\u0131\u015f\u0131\nasync function getInference(prompt) {\n const cachedResult = await redis.get(prompt);\n if (cachedResult) return JSON.parse(cachedResult);\n\n const result = await model.predict(prompt);\n await redis.set(prompt, JSON.stringify(result), 'EX', 3600);\n return result;\n}\n<\/code><\/pre>\n<h2>Hangi Durumda Hangisini Se\u00e7melisiniz? Karar Matrisi<\/h2>\n<p>Karar verme a\u015famas\u0131nda kendinize \u015fu sorular\u0131 sormal\u0131s\u0131n\u0131z: Trafi\u011fim ne kadar tahmin edilebilir? Yan\u0131t s\u00fcresi kritik mi? DevOps ekibim var m\u0131? E\u011fer trafi\u011finiz \u00e7ok de\u011fi\u015fkense ve bir g\u00fcn bin, ertesi g\u00fcn s\u0131f\u0131r istek al\u0131yorsan\u0131z, Serverless sizin i\u00e7in en g\u00fcvenli limand\u0131r. Bu sayede finansal risk almadan i\u015finizi b\u00fcy\u00fctebilirsiniz.<\/p>\n<p>\u00d6te yandan, e\u011fer uygulaman\u0131z\u0131n belirli bir kemik kitlesi varsa ve g\u00fcn\u00fcn her saati istikrarl\u0131 bir trafik al\u0131yorsan\u0131z, Dedicated sunuculara ge\u00e7me vaktiniz gelmi\u015f demektir. \u00d6zellikle b\u00fcy\u00fck modellerle \u00e7al\u0131\u015f\u0131yorsan\u0131z, Dedicated sistemlerin sundu\u011fu y\u00fcksek bellek bant geni\u015fli\u011fi ve d\u00fc\u015f\u00fck gecikme s\u00fcresi, kullan\u0131c\u0131 ba\u011fl\u0131l\u0131\u011f\u0131n\u0131 art\u0131racakt\u0131r. Bazen maliyetten ziyade, kullan\u0131c\u0131 deneyimi en b\u00fcy\u00fck \u00f6ncelik haline gelir.<\/p>\n<p>Bunun yan\u0131 s\u0131ra, kurumsal gereksinimleri de unutmamak gerekir. Baz\u0131 \u015firketler, verilerin \u00fc\u00e7\u00fcnc\u00fc taraf bir Serverless sa\u011flay\u0131c\u0131s\u0131n\u0131n payla\u015f\u0131ml\u0131 donan\u0131mlar\u0131nda i\u015flenmesine izin vermez. Bu gibi durumlarda, maliyetine bak\u0131lmaks\u0131z\u0131n Dedicated veya “On-premise” (yerle\u015fik) \u00e7\u00f6z\u00fcmler zorunluluk haline gelir. K\u0131sacas\u0131; h\u0131z ve esneklik i\u00e7in Serverless, istikrar ve d\u00fc\u015f\u00fck birim maliyet i\u00e7in Dedicated tercih edilmelidir.<\/p>\n<h2>Sonu\u00e7 ve Gelecek Projeksiyonu<\/h2>\n<p>Dedicated ve Serverless Inference aras\u0131ndaki sava\u015f, asl\u0131nda bir tercih meselesinden ziyade bir evrim s\u00fcrecidir. \u00c7o\u011fu ba\u015far\u0131l\u0131 proje Serverless ile ba\u015flar, b\u00fcy\u00fcd\u00fck\u00e7e Dedicated sistemlere g\u00f6\u00e7 eder ve nihayetinde her iki d\u00fcnyan\u0131n avantajlar\u0131n\u0131 birle\u015ftiren hibrit yap\u0131lara evrilir. Gelecekte, bulut sa\u011flay\u0131c\u0131lar\u0131n\u0131n “Cold Start” s\u00fcrelerini milisaniyelere indirmesiyle birlikte Serverless mimarilerin \u00e7ok daha bask\u0131n hale gelece\u011fini \u00f6ng\u00f6rebiliriz.<\/p>\n<p>Bununla birlikte, model boyutlar\u0131n\u0131n her ge\u00e7en g\u00fcn artmas\u0131, donan\u0131m taraf\u0131ndaki \u00f6zelle\u015fmi\u015f \u00e7iplerin (TPU, LPU gibi) \u00f6nemini art\u0131r\u0131yor. Bu da Dedicated altyap\u0131lar\u0131n, en az\u0131ndan en karma\u015f\u0131k modeller i\u00e7in, her zaman bir ihtiya\u00e7 olarak kalaca\u011f\u0131n\u0131 g\u00f6steriyor. Kendi projeniz i\u00e7in en do\u011fru karar\u0131 verirken, sadece bug\u00fcnk\u00fc trafi\u011finizi de\u011fil, alt\u0131 ay sonraki hedeflerinizi de g\u00f6z \u00f6n\u00fcnde bulundurarak bir mimari kurgulaman\u0131z ba\u015far\u0131n\u0131z\u0131n anahtar\u0131 olacakt\u0131r.<\/p>\n<h3>S\u0131k\u00e7a Sorulan Sorular<\/h3>\n<ul>\n<li><strong>Serverless Inference ger\u00e7ekten her zaman daha m\u0131 pahal\u0131d\u0131r?<\/strong> Hay\u0131r, e\u011fer trafi\u011finiz d\u00fc\u015f\u00fck veya d\u00fczensizse Serverless \u00e7ok daha ekonomiktir. Sadece y\u00fcksek ve s\u00fcrekli trafikte Dedicated daha ucuz hale gelir.<\/li>\n<li><strong>Cold Start problemini nas\u0131l \u00e7\u00f6zebilirim?<\/strong> Baz\u0131 sa\u011flay\u0131c\u0131lar “provisioned concurrency” \u00f6zelli\u011fi ile belirli say\u0131da instance’\u0131 s\u0131cak tutman\u0131za izin verir. Ayr\u0131ca modelinizi daha k\u00fc\u00e7\u00fck formatlara (ONNX, TensorRT) d\u00f6n\u00fc\u015ft\u00fcrmek y\u00fckleme s\u00fcresini k\u0131salt\u0131r.<\/li>\n<li><strong>Hibrit model kurmak zor mu?<\/strong> Evet, biraz daha karma\u015f\u0131k bir trafik y\u00f6netimi (Load Balancing) gerektirir. Ancak maliyet ve performans dengesi i\u00e7in en ideal y\u00f6ntem budur.<\/li>\n<li><strong>Hangi GPU tipi \u00e7\u0131kar\u0131m i\u00e7in daha iyidir?<\/strong> Genellikle NVIDIA T4 veya L4 gibi kartlar maliyet\/performans odakl\u0131 \u00e7\u0131kar\u0131m i\u00e7in tercih edilirken, A100 veya H100 gibi kartlar devasa modeller i\u00e7in gereklidir.<\/li>\n<li><strong>G\u00fcvenlik a\u00e7\u0131s\u0131ndan hangisi daha iyi?<\/strong> Dedicated sunucular, kaynak izolasyonu sa\u011flad\u0131\u011f\u0131 i\u00e7in genellikle daha g\u00fcvenli kabul edilir. Ancak b\u00fcy\u00fck Serverless sa\u011flay\u0131c\u0131lar\u0131 da \u00e7ok s\u0131k\u0131 g\u00fcvenlik protokolleri uygular.<\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"Dedicated vs Serverless Inference: \u00d6l\u00e7eklendirme Rehberi Yapay zeka modellerini \u00fcretim ortam\u0131na ta\u015f\u0131rken kar\u015f\u0131la\u015f\u0131lan en b\u00fcy\u00fck ikilem, altyap\u0131 se\u00e7imidir.…","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-41513","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>Yapay Zeka \u00c7\u0131kar\u0131m\u0131nda (Inference) Temel Kavramlar Nelerdir?<\/title>\n<meta name=\"description\" content=\"Yapay zeka modellerini \u00fcretim ortam\u0131na ta\u015f\u0131rken kar\u015f\u0131la\u015f\u0131lan en b\u00fcy\u00fck ikilem, altyap\u0131 se\u00e7imidir.\" \/>\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\/yapay-zeka-cikariminda-inference-temel-kavramlar-nelerdir\/\" \/>\n<meta property=\"og:locale\" content=\"tr_TR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Yapay Zeka \u00c7\u0131kar\u0131m\u0131nda (Inference) Temel Kavramlar Nelerdir?\" \/>\n<meta property=\"og:description\" content=\"Yapay zeka modellerini \u00fcretim ortam\u0131na ta\u015f\u0131rken kar\u015f\u0131la\u015f\u0131lan en b\u00fcy\u00fck ikilem, altyap\u0131 se\u00e7imidir.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/fatihsoysal.com\/blog\/yapay-zeka-cikariminda-inference-temel-kavramlar-nelerdir\/\" \/>\n<meta property=\"og:site_name\" content=\"Kodlar\u0131n Gizemli D\u00fcnyas\u0131\" \/>\n<meta property=\"article:published_time\" content=\"2026-04-30T11:01:44+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=\"11 dakika\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\/\/fatihsoysal.com\/blog\/yapay-zeka-cikariminda-inference-temel-kavramlar-nelerdir\/#article\",\"isPartOf\":{\"@id\":\"https:\/\/fatihsoysal.com\/blog\/yapay-zeka-cikariminda-inference-temel-kavramlar-nelerdir\/\"},\"author\":{\"name\":\"Fatih Soysal\",\"@id\":\"https:\/\/fatihsoysal.com\/blog\/#\/schema\/person\/002a254750921dcfd568a99e48240dd1\"},\"headline\":\"Yapay Zeka \u00c7\u0131kar\u0131m\u0131nda (Inference) Temel Kavramlar Nelerdir?\",\"datePublished\":\"2026-04-30T11:01:44+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\/\/fatihsoysal.com\/blog\/yapay-zeka-cikariminda-inference-temel-kavramlar-nelerdir\/\"},\"wordCount\":2229,\"commentCount\":0,\"publisher\":{\"@id\":\"https:\/\/fatihsoysal.com\/blog\/#\/schema\/person\/002a254750921dcfd568a99e48240dd1\"},\"inLanguage\":\"tr\",\"potentialAction\":[{\"@type\":\"CommentAction\",\"name\":\"Comment\",\"target\":[\"https:\/\/fatihsoysal.com\/blog\/yapay-zeka-cikariminda-inference-temel-kavramlar-nelerdir\/#respond\"]}],\"copyrightYear\":\"2026\",\"copyrightHolder\":{\"@id\":\"https:\/\/fatihsoysal.com\/blog\/#organization\"}},{\"@type\":\"WebPage\",\"@id\":\"https:\/\/fatihsoysal.com\/blog\/yapay-zeka-cikariminda-inference-temel-kavramlar-nelerdir\/\",\"url\":\"https:\/\/fatihsoysal.com\/blog\/yapay-zeka-cikariminda-inference-temel-kavramlar-nelerdir\/\",\"name\":\"Yapay Zeka \u00c7\u0131kar\u0131m\u0131nda (Inference) Temel Kavramlar Nelerdir?\",\"isPartOf\":{\"@id\":\"https:\/\/fatihsoysal.com\/blog\/#website\"},\"datePublished\":\"2026-04-30T11:01:44+00:00\",\"description\":\"Yapay zeka modellerini \u00fcretim ortam\u0131na ta\u015f\u0131rken kar\u015f\u0131la\u015f\u0131lan en b\u00fcy\u00fck ikilem, altyap\u0131 se\u00e7imidir.\",\"breadcrumb\":{\"@id\":\"https:\/\/fatihsoysal.com\/blog\/yapay-zeka-cikariminda-inference-temel-kavramlar-nelerdir\/#breadcrumb\"},\"inLanguage\":\"tr\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/fatihsoysal.com\/blog\/yapay-zeka-cikariminda-inference-temel-kavramlar-nelerdir\/\"]}]},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\/\/fatihsoysal.com\/blog\/yapay-zeka-cikariminda-inference-temel-kavramlar-nelerdir\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Anasayfa\",\"item\":\"https:\/\/fatihsoysal.com\/blog\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Yapay Zeka \u00c7\u0131kar\u0131m\u0131nda (Inference) Temel Kavramlar Nelerdir?\"}]},{\"@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":"Yapay Zeka \u00c7\u0131kar\u0131m\u0131nda (Inference) Temel Kavramlar Nelerdir?","description":"Yapay zeka modellerini \u00fcretim ortam\u0131na ta\u015f\u0131rken kar\u015f\u0131la\u015f\u0131lan en b\u00fcy\u00fck ikilem, altyap\u0131 se\u00e7imidir.","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\/yapay-zeka-cikariminda-inference-temel-kavramlar-nelerdir\/","og_locale":"tr_TR","og_type":"article","og_title":"Yapay Zeka \u00c7\u0131kar\u0131m\u0131nda (Inference) Temel Kavramlar Nelerdir?","og_description":"Yapay zeka modellerini \u00fcretim ortam\u0131na ta\u015f\u0131rken kar\u015f\u0131la\u015f\u0131lan en b\u00fcy\u00fck ikilem, altyap\u0131 se\u00e7imidir.","og_url":"https:\/\/fatihsoysal.com\/blog\/yapay-zeka-cikariminda-inference-temel-kavramlar-nelerdir\/","og_site_name":"Kodlar\u0131n Gizemli D\u00fcnyas\u0131","article_published_time":"2026-04-30T11:01:44+00:00","author":"Fatih Soysal","twitter_card":"summary_large_image","twitter_misc":{"Yazan:":"Fatih Soysal","Tahmini okuma s\u00fcresi":"11 dakika"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"Article","@id":"https:\/\/fatihsoysal.com\/blog\/yapay-zeka-cikariminda-inference-temel-kavramlar-nelerdir\/#article","isPartOf":{"@id":"https:\/\/fatihsoysal.com\/blog\/yapay-zeka-cikariminda-inference-temel-kavramlar-nelerdir\/"},"author":{"name":"Fatih Soysal","@id":"https:\/\/fatihsoysal.com\/blog\/#\/schema\/person\/002a254750921dcfd568a99e48240dd1"},"headline":"Yapay Zeka \u00c7\u0131kar\u0131m\u0131nda (Inference) Temel Kavramlar Nelerdir?","datePublished":"2026-04-30T11:01:44+00:00","mainEntityOfPage":{"@id":"https:\/\/fatihsoysal.com\/blog\/yapay-zeka-cikariminda-inference-temel-kavramlar-nelerdir\/"},"wordCount":2229,"commentCount":0,"publisher":{"@id":"https:\/\/fatihsoysal.com\/blog\/#\/schema\/person\/002a254750921dcfd568a99e48240dd1"},"inLanguage":"tr","potentialAction":[{"@type":"CommentAction","name":"Comment","target":["https:\/\/fatihsoysal.com\/blog\/yapay-zeka-cikariminda-inference-temel-kavramlar-nelerdir\/#respond"]}],"copyrightYear":"2026","copyrightHolder":{"@id":"https:\/\/fatihsoysal.com\/blog\/#organization"}},{"@type":"WebPage","@id":"https:\/\/fatihsoysal.com\/blog\/yapay-zeka-cikariminda-inference-temel-kavramlar-nelerdir\/","url":"https:\/\/fatihsoysal.com\/blog\/yapay-zeka-cikariminda-inference-temel-kavramlar-nelerdir\/","name":"Yapay Zeka \u00c7\u0131kar\u0131m\u0131nda (Inference) Temel Kavramlar Nelerdir?","isPartOf":{"@id":"https:\/\/fatihsoysal.com\/blog\/#website"},"datePublished":"2026-04-30T11:01:44+00:00","description":"Yapay zeka modellerini \u00fcretim ortam\u0131na ta\u015f\u0131rken kar\u015f\u0131la\u015f\u0131lan en b\u00fcy\u00fck ikilem, altyap\u0131 se\u00e7imidir.","breadcrumb":{"@id":"https:\/\/fatihsoysal.com\/blog\/yapay-zeka-cikariminda-inference-temel-kavramlar-nelerdir\/#breadcrumb"},"inLanguage":"tr","potentialAction":[{"@type":"ReadAction","target":["https:\/\/fatihsoysal.com\/blog\/yapay-zeka-cikariminda-inference-temel-kavramlar-nelerdir\/"]}]},{"@type":"BreadcrumbList","@id":"https:\/\/fatihsoysal.com\/blog\/yapay-zeka-cikariminda-inference-temel-kavramlar-nelerdir\/#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"Anasayfa","item":"https:\/\/fatihsoysal.com\/blog\/"},{"@type":"ListItem","position":2,"name":"Yapay Zeka \u00c7\u0131kar\u0131m\u0131nda (Inference) Temel Kavramlar Nelerdir?"}]},{"@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\/41513","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=41513"}],"version-history":[{"count":0,"href":"https:\/\/fatihsoysal.com\/blog\/wp-json\/wp\/v2\/posts\/41513\/revisions"}],"wp:attachment":[{"href":"https:\/\/fatihsoysal.com\/blog\/wp-json\/wp\/v2\/media?parent=41513"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/fatihsoysal.com\/blog\/wp-json\/wp\/v2\/categories?post=41513"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/fatihsoysal.com\/blog\/wp-json\/wp\/v2\/tags?post=41513"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}