{"id":3147,"date":"2026-08-13T12:53:15","date_gmt":"2026-08-13T09:53:15","guid":{"rendered":"https:\/\/shareai.now\/?p=3147"},"modified":"2026-08-13T12:53:15","modified_gmt":"2026-08-13T09:53:15","slug":"slm-vs-llm-uretim-yonlendirme","status":"publish","type":"post","link":"https:\/\/shareai.now\/tr\/blog\/gelistiriciler\/slm-vs-llm-uretim-yonlendirme\/","title":{"rendered":"SLM ve LLM: \u00dcretim G\u00f6revlerini Do\u011fru Modele Y\u00f6nlendirme"},"content":{"rendered":"<p class=\"wp-block-paragraph\">SLM ve LLM kararlar\u0131, mimari beyaz tahtada bir kez al\u0131narak sonsuza kadar her iste\u011fe uygulanmamal\u0131d\u0131r. \u00dcretimde, model boyutu bir y\u00f6nlendirme karar\u0131d\u0131r. Baz\u0131 g\u00f6revler, geni\u015flik, ak\u0131l y\u00fcr\u00fctme aral\u0131\u011f\u0131 ve b\u00fcy\u00fck bir dil modelinin esnekli\u011fini gerektirir. Di\u011fer g\u00f6revler ise daha k\u00fc\u00e7\u00fck bir dil modelinin do\u011fru cevab\u0131 daha h\u0131zl\u0131 ve daha d\u00fc\u015f\u00fck maliyetle verebilece\u011fi kadar stabildir.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Pratik soru, hangi model t\u00fcr\u00fcn\u00fcn kazand\u0131\u011f\u0131 de\u011fil. Pratik soru, her g\u00f6revi hangi modelin, hangi k\u0131s\u0131tlar alt\u0131nda ve kalite, gecikme, maliyet veya eri\u015filebilirlik de\u011fi\u015fti\u011finde hangi yedekleme ile ele almas\u0131 gerekti\u011fidir.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">SLM ve LLM bir y\u00f6nlendirme karar\u0131d\u0131r.<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">B\u00fcy\u00fck bir dil modeli genellikle a\u00e7\u0131k u\u00e7lu i\u015fler i\u00e7in daha iyidir: karma\u015f\u0131k ak\u0131l y\u00fcr\u00fctme, kodlama yard\u0131m\u0131, geni\u015f bilgi eri\u015fimi, \u00e7ok ad\u0131ml\u0131 planlama ve kullan\u0131c\u0131n\u0131n neredeyse her \u015feyi sorabilece\u011fi durumlar. K\u00fc\u00e7\u00fck bir dil modeli genellikle tekrarlanabilir, dar, y\u00fcksek hacimli ve giri\u015f deseninin tahmin edilebilir oldu\u011fu, \u00e7\u0131kt\u0131 \u015feklinin iyi anla\u015f\u0131ld\u0131\u011f\u0131 g\u00f6revler i\u00e7in daha iyidir.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Bu ayr\u0131m, \u00fcretim yapay zekas\u0131 i\u00e7in \u00f6nemlidir \u00e7\u00fcnk\u00fc bir \u00fcr\u00fcn genellikle bir\u00e7ok g\u00f6rev t\u00fcr\u00fc i\u00e7erir. Bir m\u00fc\u015fteri destek asistan\u0131, belirsiz konu\u015fmalar i\u00e7in bir LLM, niyet s\u0131n\u0131fland\u0131rmas\u0131 i\u00e7in bir SLM, \u00e7\u0131kar\u0131m i\u00e7in \u00f6zel bir model ve g\u00fcvenilirlik i\u00e7in bir yedek model gerektirebilir. T\u00fcm bunlar\u0131 tek bir model se\u00e7imi olarak ele almak genellikle ya kaliteyi ya da b\u00fct\u00e7eyi bo\u015fa harcar.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">H\u0131zl\u0131 kar\u015f\u0131la\u015ft\u0131rma<\/h2>\n\n\n\n<figure class=\"wp-block-table\"><table><thead><tr><th>Karar fakt\u00f6r\u00fc<\/th><th>LLM uyumu<\/th><th>SLM uyumu<\/th><\/tr><\/thead><tbody><tr><td>G\u00f6rev \u015fekli<\/td><td>A\u00e7\u0131k u\u00e7lu, \u00e7ok ad\u0131ml\u0131, tahmin edilemez<\/td><td>Dar, stabil, tekrarlanabilir<\/td><\/tr><tr><td>Kalite ihtiyac\u0131<\/td><td>Y\u00fcksek ak\u0131l y\u00fcr\u00fctme aral\u0131\u011f\u0131 ve esneklik<\/td><td>Bilinen bir i\u015f i\u00e7in tutarl\u0131 \u00e7\u0131kt\u0131<\/td><\/tr><tr><td>Gecikme<\/td><td>Genellikle daha yava\u015f, modele ve sa\u011flay\u0131c\u0131ya ba\u011fl\u0131 olarak<\/td><td>S\u0131n\u0131rl\u0131 g\u00f6revler i\u00e7in genellikle daha h\u0131zl\u0131<\/td><\/tr><tr><td>Maliyet<\/td><td>Geni\u015f, b\u00fcy\u00fck ba\u011flaml\u0131 kullan\u0131m i\u00e7in daha y\u00fcksek<\/td><td>Basit g\u00f6revler i\u00e7in \u00f6l\u00e7ekli kullan\u0131ld\u0131\u011f\u0131nda daha d\u00fc\u015f\u00fck<\/td><\/tr><tr><td>En iyi kullan\u0131m<\/td><td>Ara\u015ft\u0131rma, kodlama, ajanlar, sentez, karma\u015f\u0131k sohbet<\/td><td>S\u0131n\u0131fland\u0131rma, \u00e7\u0131kar\u0131m, y\u00f6nlendirme, k\u0131sa \u00f6zetler, do\u011frulama<\/td><\/tr><tr><td>Risk<\/td><td>Basit g\u00f6revlerde a\u015f\u0131r\u0131 harcama<\/td><td>Karma\u015f\u0131k veya belirsiz g\u00f6revlerde d\u00fc\u015f\u00fck performans<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\">Esneklik \u00f6nemli oldu\u011funda bir LLM kullan\u0131n<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">G\u00f6rev esnek ak\u0131l y\u00fcr\u00fctme, geni\u015f ba\u011flam veya yarat\u0131c\u0131 sentez gerektiriyorsa bir LLM kullan\u0131n. Bunlar, istemin geni\u015f \u00f6l\u00e7\u00fcde de\u011fi\u015febilece\u011fi ve modelin yeni durumlar\u0131 kat\u0131 bir oyun kitab\u0131 olmadan yorumlayacak kadar yetenekli olmas\u0131 gereken i\u015f ak\u0131\u015flar\u0131d\u0131r.<\/p>\n\n\n\n<ul class=\"wp-block-list\"><li>Bir sonraki kullan\u0131c\u0131 sorusunun tahmin edilmesinin zor oldu\u011fu m\u00fc\u015fteri konu\u015fmalar\u0131.<\/li><li>Planlama, ara\u00e7 kullan\u0131m\u0131 ve k\u0131smi hatalardan kurtarma gerektiren ajan i\u015f ak\u0131\u015flar\u0131.<\/li><li>Kod olu\u015fturma, hata ay\u0131klama ve mimari ak\u0131l y\u00fcr\u00fctme.<\/li><li>Bir\u00e7ok belge veya talimat aras\u0131nda uzun bi\u00e7imli sentez.<\/li><li>Ekibin i\u015f ak\u0131\u015f\u0131n\u0131n ne olmas\u0131 gerekti\u011fini \u00f6\u011frenmeye devam etti\u011fi erken \u00fcr\u00fcn ke\u015ffi.<\/li><\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">LLM'ler, bir yapay zeka \u00f6zelli\u011fi ya\u015fam d\u00f6ng\u00fcs\u00fcn\u00fcn ba\u015flang\u0131c\u0131nda \u00f6zellikle faydal\u0131d\u0131r. G\u00f6rev hen\u00fcz tam olarak tan\u0131mlanmam\u0131\u015fken, daha b\u00fcy\u00fck bir model ekibe \u00f6\u011frenme alan\u0131 sa\u011flar. \u0130\u015f ak\u0131\u015f\u0131 tekrarlanabilir hale geldi\u011finde, baz\u0131 ad\u0131mlar daha k\u00fc\u00e7\u00fck bir model i\u00e7in aday olabilir.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">\u0130\u015f ak\u0131\u015f\u0131 sabit oldu\u011funda bir SLM kullan\u0131n.<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">\u0130\u015f ak\u0131\u015f\u0131n\u0131n net bir s\u0131n\u0131r\u0131, \u00f6ng\u00f6r\u00fclebilir bir girdisi ve \u00f6l\u00e7\u00fclebilir bir \u00e7\u0131kt\u0131s\u0131 oldu\u011funda bir SLM kullan\u0131n. Bu g\u00f6revler genellikle geni\u015f bir ak\u0131l y\u00fcr\u00fctme aral\u0131\u011f\u0131ndan ziyade verim, gecikme ve birim ekonomisine daha fazla \u00f6nem verir.<\/p>\n\n\n\n<ul class=\"wp-block-list\"><li>Destek talepleri veya sohbet y\u00f6nlendirme i\u00e7in niyet s\u0131n\u0131fland\u0131rmas\u0131.<\/li><li>Bilinen belge t\u00fcrlerinden yap\u0131land\u0131r\u0131lm\u0131\u015f \u00e7\u0131kar\u0131m.<\/li><li>Sabit bir formatta k\u0131sa \u00f6zetler.<\/li><li>Politika kontrolleri, g\u00fcvenlik filtreleri veya do\u011frulama ad\u0131mlar\u0131.<\/li><li>Hacmin y\u00fcksek ve g\u00f6revin dar oldu\u011fu tekrarlayan arka plan g\u00f6revleri.<\/li><\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">Bir SLM, daha k\u00fc\u00e7\u00fck oldu\u011fu i\u00e7in otomatik olarak daha iyi de\u011fildir. G\u00f6rev, daha k\u00fc\u00e7\u00fck modelin kalite standard\u0131n\u0131 kar\u015f\u0131layabilece\u011fi kadar s\u0131n\u0131rl\u0131 oldu\u011funda daha iyidir. Bunu bilmenin tek g\u00fcvenilir yolu, ger\u00e7ek \u00fcretim \u00f6rneklerine kar\u015f\u0131 test etmektir.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Hibrit bir y\u00f6nlendirme yolu olu\u015fturun.<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">En g\u00fc\u00e7l\u00fc \u00fcretim modeli genellikle hibrittir. \u00d6zellik yeni oldu\u011funda en yetenekli rotayla ba\u015flay\u0131n, ger\u00e7ek \u00f6rnekler toplay\u0131n, tekrarlanabilir alt g\u00f6revleri belirleyin ve yaln\u0131zca de\u011fi\u015fikli\u011fi destekleyen kan\u0131tlar oldu\u011funda bu alt g\u00f6revleri daha k\u00fc\u00e7\u00fck veya daha \u00f6zel rotalara ta\u015f\u0131y\u0131n.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Basit bir y\u00f6nlendirme plan\u0131 \u015fu \u015fekilde g\u00f6r\u00fcnebilir:<\/p>\n\n\n\n<ol class=\"wp-block-list\"><li>Erken ke\u015fif ve karma\u015f\u0131k geri d\u00f6n\u00fc\u015fler i\u00e7in bir LLM kullan\u0131n.<\/li><li>G\u00f6rev t\u00fcr\u00fcn\u00fc, gecikmeyi, kalite sinyallerini ve tamamlanan i\u015f ak\u0131\u015f\u0131 ba\u015f\u0131na maliyeti kaydedin.<\/li><li>Sabit bir giri\u015f ve \u00e7\u0131k\u0131\u015f \u015fekline sahip tekrarlanan ad\u0131mlar\u0131 bulun.<\/li><li>Bu ad\u0131mlarda ger\u00e7ek \u00f6rneklerle bir SLM test edin.<\/li><li>Yaln\u0131zca kan\u0131tlanm\u0131\u015f g\u00f6rev dilimini SLM'ye y\u00f6nlendirin.<\/li><li>D\u00fc\u015f\u00fck g\u00fcven, belirsiz veya ba\u015far\u0131s\u0131z istekler i\u00e7in bir LLM yedekleme tutun.<\/li><\/ol>\n\n\n\n<p class=\"wp-block-paragraph\">Bu, ekiplerin her iste\u011fin basit oldu\u011funu iddia etmeden maliyeti ve gecikmeyi azaltmas\u0131na olanak tan\u0131r. Ayr\u0131ca, yeni sa\u011flay\u0131c\u0131lar, model boyutlar\u0131 ve a\u00e7\u0131k a\u011f\u0131rl\u0131k se\u00e7enekleri kullan\u0131labilir hale geldik\u00e7e model y\u0131\u011f\u0131n\u0131n\u0131 geli\u015ftirmeyi kolayla\u015ft\u0131r\u0131r.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">ShareAI'nin uyumu<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">ShareAI, Geli\u015ftiricilerin geni\u015f bir AI modeli ve sa\u011flay\u0131c\u0131 a\u011f\u0131 aras\u0131nda tek bir API arac\u0131l\u0131\u011f\u0131yla y\u00f6nlendirme yapmas\u0131na yard\u0131mc\u0131 olur. SLM ve LLM'yi kal\u0131c\u0131 bir sat\u0131c\u0131 karar\u0131 olarak ele almak yerine, Geli\u015ftiriciler se\u00e7enekleri kar\u015f\u0131la\u015ft\u0131rabilir, rotalar\u0131 test edebilir ve \u00fcr\u00fcn mant\u0131\u011f\u0131n\u0131 model katman\u0131ndan ayr\u0131 tutabilir.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Bu, SaaS \u00fcr\u00fcnleri, ajanslar, a\u00e7\u0131k kaynak ara\u00e7lar\u0131, gizlilik odakl\u0131 uygulamalar ve her model de\u011fi\u015fikli\u011finin bir s\u00fcr\u00fcm d\u00f6ng\u00fcs\u00fc haline gelmesini istemeyen AI \u00f6zelliklerine ihtiya\u00e7 duyan dahili yaz\u0131l\u0131m ekipleri i\u00e7in kullan\u0131\u015fl\u0131d\u0131r. Geli\u015ftiriciler, <a href=\"https:\/\/shareai.now\/documentation\/?utm_source=blog&amp;utm_medium=content&amp;utm_campaign=slm-vs-llm-production-routing\">ShareAI belgeleri<\/a>, ile ba\u015flayabilir, mevcut <a href=\"https:\/\/shareai.now\/models\/?utm_source=blog&amp;utm_medium=content&amp;utm_campaign=slm-vs-llm-production-routing\">AI modellerini<\/a>, kar\u015f\u0131la\u015ft\u0131rabilir ve \u00e7\u0131kt\u0131lar\u0131 <a href=\"https:\/\/console.shareai.now\/chat\/?utm_source=shareai.now&amp;utm_medium=content&amp;utm_campaign=slm-vs-llm-production-routing\">ShareAI Oyun Alan\u0131<\/a>.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">test edebilir. Ayn\u0131 model y\u00f6nlendirme mant\u0131\u011f\u0131, Sa\u011flay\u0131c\u0131lar\u0131 da destekler. Bir sa\u011flay\u0131c\u0131, bir i\u015f y\u00fck\u00fc s\u0131n\u0131f\u0131 i\u00e7in g\u00fc\u00e7l\u00fc gecikme, kullan\u0131labilirlik veya fiyatland\u0131rma sunuyorsa, y\u00f6nlendirme bu kapasiteye talep i\u00e7in bir yol sa\u011flar. Yarat\u0131c\u0131lar ve model sahipleri i\u00e7in y\u00f6nlendirme, bir modelin ger\u00e7ek bir \u00fcretim g\u00f6revine uygun oldu\u011funda Geli\u015ftiriciler taraf\u0131ndan denenmesini, benimsenmesini ve gelir elde edilmesini kolayla\u015ft\u0131rabilir.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">G\u00f6revleri de\u011fi\u015ftirmeden \u00f6nce pratik bir test<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Bir i\u015f y\u00fck\u00fcn\u00fc bir LLM'den bir SLM'ye ta\u015f\u0131madan \u00f6nce kalite standard\u0131n\u0131 tan\u0131mlay\u0131n. \u00d6rne\u011fin, bir \u00e7\u0131kar\u0131m ad\u0131m\u0131 ge\u00e7erli JSON, do\u011fru alanlar ve hayali de\u011ferler i\u00e7ermemelidir. Bir s\u0131n\u0131fland\u0131rma ad\u0131m\u0131, hedef e\u015fi\u011fin \u00fczerindeki insan etiketleriyle uyum gerektirebilir. Bir y\u00f6nlendirme ad\u0131m\u0131 hem do\u011fruluk hem de h\u0131zl\u0131 yan\u0131t s\u00fcresi gerektirebilir.<\/p>\n\n\n\n<ul class=\"wp-block-list\"><li>A\u00e7\u0131k ba\u015far\u0131 kriterlerine sahip dar bir g\u00f6rev se\u00e7in.<\/li><li>Ger\u00e7ek m\u00fc\u015fteri veya \u00fcretim \u00f6rneklerinden bir test seti olu\u015fturun.<\/li><li>LLM ve SLM \u00e7\u0131kt\u0131lar\u0131 yan yana kar\u015f\u0131la\u015ft\u0131r\u0131n.<\/li><li>Yaln\u0131zca token fiyat\u0131n\u0131 de\u011fil, tam g\u00f6rev maliyetini \u00f6l\u00e7\u00fcn.<\/li><li>D\u00fc\u015f\u00fck g\u00fcven veya bozuk \u00e7\u0131kt\u0131lar i\u00e7in yedek kurallar belirleyin.<\/li><li>Model ve sa\u011flay\u0131c\u0131lar de\u011fi\u015fti\u011fi i\u00e7in da\u011f\u0131t\u0131mdan sonra rota performans\u0131n\u0131 g\u00f6zden ge\u00e7irin.<\/li><\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">Do\u011fru cevap, her LLM \u00e7a\u011fr\u0131s\u0131n\u0131 bir SLM ile de\u011fi\u015ftirmek de\u011fildir. Daha iyi cevap, sabit i\u015fleri daha k\u00fc\u00e7\u00fck modellere y\u00f6nlendirmek ve ger\u00e7ekten ihtiya\u00e7 duyulan i\u015fler i\u00e7in daha b\u00fcy\u00fck modelleri korumakt\u0131r.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">K\u00fc\u00e7\u00fck dil modellerinin daha geni\u015f bir tan\u0131m\u0131 i\u00e7in Microsoft Azure\u2019un rehberine bak\u0131n. <a href=\"https:\/\/azure.microsoft.com\/en-us\/resources\/cloud-computing-dictionary\/what-are-small-language-models?utm_source=shareai.now&amp;utm_medium=content&amp;utm_campaign=slm-vs-llm-production-routing\">k\u00fc\u00e7\u00fck dil modelleri<\/a>.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">SSS<\/h2>\n\n\n\n<h3 class=\"wp-block-heading\">Bir SLM ile bir LLM aras\u0131ndaki temel fark nedir?<\/h3>\n\n\n<p class=\"wp-block-paragraph\">Bir SLM daha k\u00fc\u00e7\u00fckt\u00fcr ve genellikle dar, tekrarlanabilir g\u00f6revler i\u00e7in daha uygundur. Bir LLM daha b\u00fcy\u00fckt\u00fcr ve genellikle geni\u015f ak\u0131l y\u00fcr\u00fctme, karma\u015f\u0131k konu\u015fma, kodlama ve \u00f6ng\u00f6r\u00fclemeyen g\u00f6revler i\u00e7in daha iyidir.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Bir SLM her zaman bir LLM\u2019den daha m\u0131 ucuzdur?<\/h3>\n\n\n<p class=\"wp-block-paragraph\">Bir SLM genellikle y\u00fcksek hacimli, dar g\u00f6revler i\u00e7in daha ucuzdur, ancak ger\u00e7ek kar\u015f\u0131la\u015ft\u0131rma ba\u015far\u0131l\u0131 g\u00f6rev ba\u015f\u0131na maliyettir. S\u0131k s\u0131k ba\u015far\u0131s\u0131z olan ucuz bir model, yeniden denemeler, yedek \u00e7a\u011fr\u0131lar ve insan incelemesi nedeniyle daha pahal\u0131ya mal olabilir.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Bir SLM her zaman bir LLM\u2019den daha m\u0131 h\u0131zl\u0131d\u0131r?<\/h3>\n\n\n<p class=\"wp-block-paragraph\">Daha k\u00fc\u00e7\u00fck modeller genellikle daha h\u0131zl\u0131d\u0131r, ancak gecikme sa\u011flay\u0131c\u0131ya, donan\u0131ma, b\u00f6lgeye, kuyruklama, ba\u011flam uzunlu\u011funa ve ak\u0131\u015f davran\u0131\u015f\u0131na ba\u011fl\u0131d\u0131r. Sadece model boyutunu de\u011fil, t\u00fcm i\u015f ak\u0131\u015f\u0131n\u0131 \u00f6l\u00e7\u00fcn.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Bir \u00fcr\u00fcn hem SLM\u2019leri hem de LLM\u2019leri kullanabilir mi?<\/h3>\n\n\n<p class=\"wp-block-paragraph\">Evet. Bir\u00e7ok \u00fcretim sistemi her ikisini de kullanmal\u0131d\u0131r. Basit, sabit g\u00f6revleri SLM\u2019lere y\u00f6nlendirin ve karma\u015f\u0131k, belirsiz veya y\u00fcksek de\u011ferli talepler i\u00e7in LLM\u2019leri saklay\u0131n.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Bir ekip ne zaman bir SLM kullanmaktan ka\u00e7\u0131nmal\u0131d\u0131r?<\/h3>\n\n\n<p class=\"wp-block-paragraph\">G\u00f6rev a\u00e7\u0131k u\u00e7lu, k\u00f6t\u00fc tan\u0131mlanm\u0131\u015f, g\u00fc\u00e7l\u00fc do\u011frulama olmadan g\u00fcvenlik a\u00e7\u0131s\u0131ndan kritik veya daha k\u00fc\u00e7\u00fck modelin g\u00fcvenilir bir \u015fekilde ele alamayaca\u011f\u0131 geni\u015f ak\u0131l y\u00fcr\u00fctmeye ba\u011fl\u0131 oldu\u011funda bir SLM\u2019den ka\u00e7\u0131n\u0131n.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Model y\u00f6nlendirme, SLM ve LLM kararlar\u0131nda nas\u0131l yard\u0131mc\u0131 olur?<\/h3>\n\n\n<p class=\"wp-block-paragraph\">Model y\u00f6nlendirme, uygulaman\u0131n g\u00f6rev, m\u00fc\u015fteri, maliyet s\u0131n\u0131r\u0131, gecikme hedefi veya yedekleme ko\u015fuluna g\u00f6re bir model se\u00e7mesine olanak tan\u0131r. Bu, her istek i\u00e7in tek bir model boyutu se\u00e7mekten daha esnektir.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Geli\u015ftiriciler bir LLM ile mi yoksa bir SLM ile mi ba\u015flamal\u0131?<\/h3>\n\n\n<p class=\"wp-block-paragraph\">En h\u0131zl\u0131 \u00f6\u011frenmenize yard\u0131mc\u0131 olan rotayla ba\u015flay\u0131n. Bir\u00e7ok ekip, i\u015f ak\u0131\u015f\u0131 de\u011fi\u015firken bir LLM ile ba\u015flar, ard\u0131ndan ger\u00e7ek \u00f6rnekler ve net ba\u015far\u0131 \u00f6l\u00e7\u00fctleri elde ettikten sonra sabit alt g\u00f6revleri SLM'lere ta\u015f\u0131r.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">ShareAI uygulamam\u0131 m\u0131 olu\u015fturur veya bar\u0131nd\u0131r\u0131r m\u0131?<\/h3>\n\n\n<p class=\"wp-block-paragraph\">Hay\u0131r. ShareAI bir uygulama \u00e7er\u00e7evesi, CMS, bar\u0131nd\u0131rma platformu veya kodsuz olu\u015fturucu de\u011fildir. Geli\u015ftiriciler, ShareAI'yi bir API arac\u0131l\u0131\u011f\u0131yla AI modellerine eri\u015fmek, kar\u015f\u0131la\u015ft\u0131rmak ve y\u00f6nlendirmek i\u00e7in kullan\u0131r.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Ajanslar SLM ve LLM y\u00f6nlendirmesini nas\u0131l kullanmal\u0131?<\/h3>\n\n\n<p class=\"wp-block-paragraph\">Ajanslar, m\u00fc\u015fteri i\u015f y\u00fcklerini maliyet, kalite, gizlilik ihtiya\u00e7lar\u0131 ve yan\u0131t s\u00fcresi gereksinimlerine g\u00f6re y\u00f6nlendirebilir. Bu, her m\u00fc\u015fteri i\u00e7in s\u0131f\u0131rdan \u00f6zel bir model entegrasyon plan\u0131 olu\u015fturma ihtiyac\u0131n\u0131 \u00f6nler.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Sa\u011flay\u0131c\u0131lar SLM ve LLM y\u00f6nlendirmesinden nas\u0131l faydalan\u0131r?<\/h3>\n\n\n<p class=\"wp-block-paragraph\">Sa\u011flay\u0131c\u0131lar, belirli i\u015f y\u00fck\u00fc t\u00fcrleri i\u00e7in hesaplama veya \u00e7\u0131kar\u0131m kapasiteleri iyi performans g\u00f6sterdi\u011finde talep kazanabilir. Y\u00f6nlendirme, iyi sa\u011flay\u0131c\u0131 kapasitesinin Geli\u015ftiriciler taraf\u0131ndan ke\u015ffedilebilir olmas\u0131na yard\u0131mc\u0131 olur.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">En g\u00fcvenli ilk \u00fcretim testi nedir?<\/h3>\n\n\n<p class=\"wp-block-paragraph\">Dar bir g\u00f6rev se\u00e7in, ba\u015far\u0131 kriterlerini tan\u0131mlay\u0131n, ger\u00e7ek \u00f6rneklerde SLM ve LLM \u00e7\u0131kt\u0131s\u0131n\u0131 kar\u015f\u0131la\u015ft\u0131r\u0131n, yedekleme kurallar\u0131 belirleyin ve ancak o zaman trafi\u011fin k\u00fc\u00e7\u00fck bir k\u0131sm\u0131n\u0131 yeni yola y\u00f6nlendirin.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/shareai.now\/documentation\/?utm_source=blog&amp;utm_medium=content&amp;utm_campaign=slm-vs-llm-production-routing\">Bir API entegre edin<\/a> \u00fcr\u00fcn mant\u0131\u011f\u0131n\u0131 tek bir model boyutuna ba\u011flamadan model yollar\u0131n\u0131 test etmek i\u00e7in.<\/p>","protected":false},"excerpt":{"rendered":"<p>G\u00f6rev karma\u015f\u0131kl\u0131\u011f\u0131, gecikme, maliyet ve kaliteye g\u00f6re \u00fcretim AI y\u00f6nlendirmesi i\u00e7in pratik bir SLM vs LLM rehberi, her istek i\u00e7in tek bir model boyutu se\u00e7mek yerine.<\/p>","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"cta-title":"Integrate one API","cta-description":"Access 150+ models with smart routing and failover.","cta-button-text":"View Docs","cta-button-link":"https:\/\/shareai.now\/documentation\/?utm_source=blog&utm_medium=content&utm_campaign=slm-vs-llm-production-routing","rank_math_title":"SLM vs LLM: Route Production Tasks to the Right Model","rank_math_description":"SLM vs LLM decisions should be made per task, using routing to balance quality, latency, cost, and reliability in production.","rank_math_focus_keyword":"SLM vs LLM, small language models, large language models, AI model routing, AI API routing, model routing","footnotes":""},"categories":[4,6],"tags":[42,92,236,234,235],"class_list":["post-3147","post","type-post","status-publish","format-standard","hentry","category-developers","category-insights","tag-ai-api-routing","tag-ai-model-routing","tag-large-language-models","tag-slm-vs-llm","tag-small-language-models"],"_links":{"self":[{"href":"https:\/\/shareai.now\/tr\/api\/wp\/v2\/posts\/3147","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=3147"}],"version-history":[{"count":1,"href":"https:\/\/shareai.now\/tr\/api\/wp\/v2\/posts\/3147\/revisions"}],"predecessor-version":[{"id":3195,"href":"https:\/\/shareai.now\/tr\/api\/wp\/v2\/posts\/3147\/revisions\/3195"}],"wp:attachment":[{"href":"https:\/\/shareai.now\/tr\/api\/wp\/v2\/media?parent=3147"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/shareai.now\/tr\/api\/wp\/v2\/categories?post=3147"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/shareai.now\/tr\/api\/wp\/v2\/tags?post=3147"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}