{"id":2907,"date":"2026-05-29T13:43:47","date_gmt":"2026-05-29T10:43:47","guid":{"rendered":"https:\/\/shareai.now\/?p=2907"},"modified":"2026-05-29T13:43:54","modified_gmt":"2026-05-29T10:43:54","slug":"lilac-ai-cikarimi-sicak-sunucusuz-modeller-yonlendirme","status":"publish","type":"post","link":"https:\/\/shareai.now\/tr\/blog\/gelistiriciler\/lilac-ai-cikarimi-sicak-sunucusuz-modeller-yonlendirme\/","title":{"rendered":"Lilac AI \u00c7\u0131kar\u0131m\u0131: S\u0131cak Sunucusuz Modeller ve Y\u00f6nlendirme Uzla\u015fmalar\u0131"},"content":{"rendered":"<p><strong>Lilac AI \u00e7\u0131kar\u0131m\u0131<\/strong> model altyap\u0131 pazar\u0131n\u0131n nas\u0131l de\u011fi\u015fti\u011fini izleyen geli\u015ftiriciler i\u00e7in faydal\u0131 bir sinyaldir: daha a\u00e7\u0131k a\u011f\u0131rl\u0131kl\u0131 modeller, daha fazla OpenAI uyumlu u\u00e7 nokta, daha fazla token tabanl\u0131 fiyatland\u0131rma ve yaln\u0131zca markaya dayal\u0131 de\u011fil, maliyet, gecikme ve kullan\u0131labilirli\u011fe dayal\u0131 istekleri y\u00f6nlendirme konusunda daha fazla bask\u0131.<\/p>\n\n\n\n<p>Lilac, API'sini <a href=\"https:\/\/getlilac.com\/serverless-inference-api?utm_source=shareai.now&amp;utm_medium=content&amp;utm_campaign=lilac-ai-inference-warm-serverless-models-routing\">s\u0131cak sunucusuz u\u00e7 noktalar<\/a> bo\u015fta duran kurumsal GPU'lar taraf\u0131ndan desteklenmektedir. Sunum basittir: geli\u015ftirici deneyimini OpenAI SDK's\u0131na yak\u0131n tutun, ayr\u0131lm\u0131\u015f GPU taahh\u00fctlerinden ka\u00e7\u0131n\u0131n ve ekiplerin bir rotan\u0131n mant\u0131kl\u0131 olup olmad\u0131\u011f\u0131na karar verebilmesi i\u00e7in model fiyatland\u0131rmas\u0131n\u0131 yeterince a\u00e7\u0131k bir \u015fekilde ortaya koyun.<\/p>\n\n\n\n<p>ShareAI kullanan ekipler i\u00e7in \u00e7\u0131kar\u0131m, her yeni u\u00e7 noktay\u0131 manuel olarak takip etmek de\u011fildir. Bunun yerine, modellerin, sa\u011flay\u0131c\u0131lar\u0131n ve y\u00f6nlendirme se\u00e7eneklerinin her yeni se\u00e7enek ortaya \u00e7\u0131kt\u0131\u011f\u0131nda \u00fcr\u00fcn kodunu yeniden yazmadan de\u011ferlendirilebilece\u011fi bir AI pazar\u0131 ve API katman\u0131 etraf\u0131nda in\u015fa etmektir.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Lilac AI \u00e7\u0131kar\u0131m\u0131n\u0131 izlemeye de\u011fer k\u0131lan \u015fey<\/h2>\n\n\n\n<p>Lilac, sunucusuz \u00e7\u0131kar\u0131m API'sini OpenAI uyumlu, token fiyatl\u0131 ve payla\u015f\u0131lan s\u0131cak u\u00e7 noktalarla desteklenmi\u015f olarak tan\u0131ml\u0131yor. Halihaz\u0131rdaki genel model tablosunda MiniMax M2.7, Kimi K2.6, GLM 5.1 ve Gemma 4 (31B) listeleniyor ve ba\u011flam pencereleri yakla\u015f\u0131k 200K ile 262K token aras\u0131nda de\u011fi\u015fiyor.<\/p>\n\n\n\n<p>Bu kombinasyon \u00f6nemlidir \u00e7\u00fcnk\u00fc bir\u00e7ok \u00fcretim ekibi zaten uygulama mant\u0131\u011f\u0131n\u0131 model se\u00e7iminden ay\u0131rmaktad\u0131r. Bir destek botu, kodlama asistan\u0131, belge i\u015f ak\u0131\u015f\u0131 veya dahili analiz arac\u0131, h\u0131zl\u0131 k\u0131sa yan\u0131tlar i\u00e7in bir modele, uzun ba\u011flaml\u0131 ak\u0131l y\u00fcr\u00fctme i\u00e7in ba\u015fka bir modele ve kullan\u0131labilirlik de\u011fi\u015fti\u011finde yedek olarak ba\u015fka bir modele ihtiya\u00e7 duyabilir.<\/p>\n\n\n\n<p>Bir sa\u011flay\u0131c\u0131 OpenAI uyumlu bir API sundu\u011funda, SDK katman\u0131nda ge\u00e7i\u015f yapmak daha kolay olabilir. Ancak yaln\u0131zca uyumluluk, daha zor i\u015fletim sorular\u0131n\u0131 \u00e7\u00f6zmez: bu istek i\u00e7in en ucuz rota hangisi, hangi rota yeterince h\u0131zl\u0131, hangi model ba\u011flam uzunlu\u011funu i\u015fler ve u\u00e7 nokta bozulursa ne olur?<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Mevcut Lilac model setinin \u00f6nerdi\u011fi \u015fey<\/h2>\n\n\n\n<figure class=\"wp-block-table\"><table><thead><tr><th>Model<\/th><th>Yay\u0131nlanan ba\u011flam<\/th><th>Yay\u0131nlanan fiyatland\u0131rma sinyali<\/th><th>Pratik uyum<\/th><\/tr><\/thead><tbody><tr><td>MiniMax M2.7<\/td><td>200K<\/td><td>$0.30\/M giri\u015f, $1.20\/M \u00e7\u0131k\u0131\u015f<\/td><td>Maliyet duyarl\u0131 metin i\u015f y\u00fckleri ve y\u00fcksek hacimli deneyler<\/td><\/tr><tr><td>Kimi K2.6<\/td><td>262K<\/td><td>$0.70\/M giri\u015f, $3.50\/M \u00e7\u0131k\u0131\u015f<\/td><td>Uzun ba\u011flaml\u0131 ajan ve kodlama tarz\u0131 i\u015f ak\u0131\u015flar\u0131<\/td><\/tr><tr><td>GLM 5.1<\/td><td>203K<\/td><td>$0.90\/M giri\u015f, $3.00\/M \u00e7\u0131k\u0131\u015f<\/td><td>Ak\u0131l y\u00fcr\u00fctme, ara\u00e7 kullan\u0131m\u0131 ve yap\u0131land\u0131r\u0131lm\u0131\u015f \u00e7\u0131kt\u0131 testleri<\/td><\/tr><tr><td>Gemma 4 (31B)<\/td><td>262K<\/td><td>$0.11\/M giri\u015f, $0.35\/M \u00e7\u0131k\u0131\u015f<\/td><td>Modelin g\u00f6reve uygun oldu\u011fu d\u00fc\u015f\u00fck maliyetli a\u00e7\u0131k a\u011f\u0131rl\u0131k i\u015f y\u00fckleri<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p>Bu say\u0131lar testin yerine ge\u00e7mez. Bunlar bir ba\u015flang\u0131\u00e7 noktas\u0131d\u0131r. Tak\u0131mlar hala kendi trafiklerinde istem \u015fekli, \u00e7\u0131kt\u0131 uzunlu\u011fu, ilk token gecikmesi, verimlilik, g\u00fcvenilirlik ve cevap kalitesini kar\u015f\u0131la\u015ft\u0131rmal\u0131d\u0131r.<\/p>\n\n\n\n<p>Daha b\u00fcy\u00fck desen, herhangi bir tek sa\u011flay\u0131c\u0131 sayfas\u0131ndan daha \u00f6nemlidir. Model eri\u015fimi daha ak\u0131\u015fkan hale geliyor. En \u00e7ok fayda sa\u011flayan tak\u0131mlar, \u00e7\u0131kar\u0131m\u0131 y\u00f6nlendirilmi\u015f bir operasyonel katman olarak ele alanlar, kal\u0131c\u0131 bir tek model karar\u0131 olarak de\u011fil.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Yeni bir \u00e7\u0131kar\u0131m sa\u011flay\u0131c\u0131s\u0131n\u0131 nas\u0131l de\u011ferlendirece\u011finiz<\/h2>\n\n\n\n<p>Ger\u00e7ek \u00fcretim trafi\u011fini yeni bir model u\u00e7 noktas\u0131na ta\u015f\u0131madan \u00f6nce, geli\u015ftiriciler be\u015f \u015feyi test etmelidir.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Uyumluluk:<\/strong> U\u00e7 nokta mevcut SDK'n\u0131z, istek format\u0131n\u0131z, ak\u0131\u015f davran\u0131\u015f\u0131n\u0131z ve ara\u00e7 \u00e7a\u011f\u0131rma beklentilerinizle \u00e7al\u0131\u015fabilir mi?<\/li>\n\n\n\n<li><strong>Gecikme:<\/strong> \u0130lk token s\u00fcresi ve toplam tamamlama s\u00fcresi ihtiya\u00e7 duydu\u011funuz kullan\u0131c\u0131 deneyimine uyuyor mu?<\/li>\n\n\n\n<li><strong>Ba\u011flam davran\u0131\u015f\u0131:<\/strong> Model, sadece ilan edilen ba\u011flam penceresi de\u011fil, ger\u00e7ek uzun istemlerinizde g\u00fcvenilir kal\u0131yor mu?<\/li>\n\n\n\n<li><strong>Maliyet \u015fekli:<\/strong> Kullan\u0131c\u0131lar uzun yan\u0131tlar olu\u015fturdu\u011funda giri\u015f, \u00f6nbelle\u011fe al\u0131nm\u0131\u015f giri\u015f ve \u00e7\u0131kt\u0131 fiyatland\u0131rmas\u0131 hala i\u015fe yar\u0131yor mu?<\/li>\n\n\n\n<li><strong>Geri d\u00f6n\u00fc\u015f yolu:<\/strong> Se\u00e7ilen u\u00e7 nokta yava\u015flarsa veya kullan\u0131lamaz hale gelirse hangi yol trafi\u011fi almal\u0131d\u0131r?<\/li>\n<\/ul>\n\n\n\n<p>\u0130\u015fte burada bir pazar katman\u0131 yard\u0131mc\u0131 olur. ShareAI'de geli\u015ftiriciler <a href=\"https:\/\/shareai.now\/models\/?utm_source=blog&amp;utm_medium=content&amp;utm_campaign=lilac-ai-inference-warm-serverless-models-routing\">AI modellerini g\u00f6zden ge\u00e7irebilir<\/a>, mevcut se\u00e7enekleri kar\u015f\u0131la\u015ft\u0131r\u0131n ve her sa\u011flay\u0131c\u0131 de\u011fi\u015fikli\u011fini uygulamaya sabitlemek yerine y\u00f6nlendirme kararlar\u0131na g\u00f6re tasarlay\u0131n.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Y\u00f6nlendirme, tek seferlik sa\u011flay\u0131c\u0131 de\u011fi\u015fiminden daha iyidir.<\/h2>\n\n\n\n<p>Sa\u011flay\u0131c\u0131 esnekli\u011finin en basit versiyonu bir temel URL'yi de\u011fi\u015ftirmektir. Bu faydal\u0131d\u0131r, ancak sadece birinci ad\u0131md\u0131r. Ger\u00e7ek \u00fcretim sistemleri genellikle politika gerektirir: bu m\u00fc\u015fteri katman\u0131n\u0131 bir modele y\u00f6nlendirin, uzun ba\u011flaml\u0131 i\u015fleri ba\u015fka bir modele g\u00f6nderin, bir rota sa\u011fl\u0131ks\u0131z oldu\u011funda yedekleme yap\u0131n ve kullan\u0131m artt\u0131k\u00e7a maliyetleri g\u00f6r\u00fcn\u00fcr tutun.<\/p>\n\n\n\n<p>Y\u00f6nlendirilmi\u015f bir yap\u0131land\u0131rma, ekiplerin uygulamay\u0131 k\u0131r\u0131lgan hale getirmeden yeni sa\u011flay\u0131c\u0131lar\u0131 benimsemelerine olanak tan\u0131r. Ayr\u0131ca \u00fcr\u00fcn ve finans ekiplerine AI maliyetlerini tart\u0131\u015fmak i\u00e7in daha net bir yol sunar. Bir modelin kal\u0131c\u0131 kazanan olup olmad\u0131\u011f\u0131n\u0131 sormak yerine, hangi rotan\u0131n g\u00f6reve, fiyat noktas\u0131na ve g\u00fcvenilirlik gereksinimine uygun oldu\u011funu sorabilirler.<\/p>\n\n\n\n<p>Yap\u0131c\u0131lar i\u00e7in bu daha da \u00f6nemlidir. Mevcut bir uygulama AI \u00e7\u0131kar\u0131m\u0131n\u0131 ShareAI \u00fczerinden g\u00f6nderiyorsa, kullan\u0131m \u00f6l\u00e7\u00fclebilir ve gelir elde edilebilir, Yap\u0131c\u0131dan s\u0131f\u0131rdan bir faturalama sistemi olu\u015fturmas\u0131n\u0131 istemeden. Uygulama hala ShareAI d\u0131\u015f\u0131nda ya\u015far; ShareAI y\u00f6nlendirme, kullan\u0131m, faturalama, ek \u00fccret veya marj mant\u0131\u011f\u0131 ve uygun y\u00f6nlendirilmi\u015f trafik i\u00e7in ayl\u0131k Yap\u0131c\u0131 \u00f6demelerini y\u00f6netir.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Geli\u015ftiricilerin bir sonraki ad\u0131mda yapmas\u0131 gerekenler<\/h2>\n\n\n\n<p>Lilac AI \u00e7\u0131kar\u0131m\u0131, daha fazla sa\u011flay\u0131c\u0131 se\u00e7imi ve daha \u00f6zel model rotalar\u0131na y\u00f6nelik daha geni\u015f bir de\u011fi\u015fimin par\u00e7as\u0131d\u0131r. Pratik ad\u0131m, herhangi bir \u00fcretim ba\u011f\u0131ml\u0131l\u0131\u011f\u0131na uygulayaca\u011f\u0131n\u0131z ayn\u0131 disiplinle yeni u\u00e7 noktalar\u0131 test etmektir: onlar\u0131 k\u0131yaslay\u0131n, kar\u015f\u0131la\u015ft\u0131r\u0131n, yedek davran\u0131\u015f belirleyin ve y\u00f6nlendirmeyi yap\u0131land\u0131r\u0131labilir tutun.<\/p>\n\n\n\n<p>Bir model y\u00f6nlendirme stratejisi planl\u0131yorsan\u0131z, i\u015f y\u00fcklerinizi haritalayarak ba\u015flay\u0131n. K\u0131sa sohbeti, uzun ba\u011flam analizini, kod \u00fcretimini, belge i\u015flemesini ve m\u00fc\u015fteri odakl\u0131 premium \u00f6zellikleri ay\u0131r\u0131n. Ard\u0131ndan <a href=\"https:\/\/console.shareai.now\/chat\/?utm_source=shareai.now&amp;utm_medium=content&amp;utm_campaign=lilac-ai-inference-warm-serverless-models-routing\">ShareAI Playground'u kullan\u0131n<\/a> ve <a href=\"https:\/\/shareai.now\/documentation\/?utm_source=blog&amp;utm_medium=content&amp;utm_campaign=lilac-ai-inference-warm-serverless-models-routing\">ShareAI belgeleri<\/a> her bir rotan\u0131n \u00f6l\u00e7eklendirmeden \u00f6nce ne yapmas\u0131 gerekti\u011fini kar\u015f\u0131la\u015ft\u0131rmak i\u00e7in.<\/p>","protected":false},"excerpt":{"rendered":"<p>Lilac AI \u00e7\u0131kar\u0131m\u0131, ekipler model trafi\u011fini y\u00f6nlendirdi\u011finde neden s\u0131cak sunucusuz u\u00e7 noktalar\u0131n, token fiyatland\u0131rmas\u0131n\u0131n ve OpenAI uyumlu API'lerin \u00f6nemli oldu\u011funu g\u00f6steriyor.<\/p>","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"cta-title":"Explore AI Models","cta-description":"Compare price, latency, and availability across providers.","cta-button-text":"","cta-button-link":"","rank_math_title":"Lilac AI Inference: Warm Serverless Models","rank_math_description":"Lilac AI inference shows how warm serverless endpoints, model pricing, and routing trade-offs affect production AI apps.","rank_math_focus_keyword":"Lilac AI inference","footnotes":""},"categories":[4,7],"tags":[94,93,51,96,95],"class_list":["post-2907","post","type-post","status-publish","format-standard","hentry","category-developers","category-news","tag-ai-inference","tag-lilac","tag-model-routing","tag-open-weight-models","tag-serverless-inference"],"_links":{"self":[{"href":"https:\/\/shareai.now\/tr\/api\/wp\/v2\/posts\/2907","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/shareai.now\/tr\/api\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/shareai.now\/tr\/api\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/shareai.now\/tr\/api\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/shareai.now\/tr\/api\/wp\/v2\/comments?post=2907"}],"version-history":[{"count":2,"href":"https:\/\/shareai.now\/tr\/api\/wp\/v2\/posts\/2907\/revisions"}],"predecessor-version":[{"id":2909,"href":"https:\/\/shareai.now\/tr\/api\/wp\/v2\/posts\/2907\/revisions\/2909"}],"wp:attachment":[{"href":"https:\/\/shareai.now\/tr\/api\/wp\/v2\/media?parent=2907"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/shareai.now\/tr\/api\/wp\/v2\/categories?post=2907"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/shareai.now\/tr\/api\/wp\/v2\/tags?post=2907"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}