{"id":1739,"date":"2026-04-09T12:24:16","date_gmt":"2026-04-09T09:24:16","guid":{"rendered":"https:\/\/shareai.now\/?p=1739"},"modified":"2026-04-14T03:20:24","modified_gmt":"2026-04-14T00:20:24","slug":"llm-api-saglayicilari","status":"publish","type":"post","link":"https:\/\/shareai.now\/tr\/blog\/icgoruler\/llm-api-saglayicilari\/","title":{"rendered":"2026\u2019da En \u0130yi 12 LLM API Sa\u011flay\u0131c\u0131s\u0131 (ShareAI Rehberi)"},"content":{"rendered":"

May\u0131s 2026'da g\u00fcncellendi \u00b7 ~12 dakikal\u0131k okuma<\/em><\/p>\n\n\n\n

LLM API sa\u011flay\u0131c\u0131lar\u0131 2026<\/strong> \u00fcretim uygulamalar\u0131 i\u00e7in her zamankinden daha \u00f6nemli. \u00d6l\u00e7eklenebilir, maliyet etkin \u00e7\u0131kar\u0131m, sizi d\u00fcr\u00fcst tutan g\u00f6zlemlenebilirlik ve her i\u015f i\u00e7in en iyi modele trafik y\u00f6nlendirme \u00f6zg\u00fcrl\u00fc\u011f\u00fcne ihtiyac\u0131n\u0131z var\u2014kilitlenme olmadan.<\/p>\n\n\n\n

Bu k\u0131lavuz en iyi 12 LLM API sa\u011flay\u0131c\u0131s\u0131n\u0131 2026<\/strong> kar\u015f\u0131la\u015ft\u0131r\u0131r ve ShareAI<\/strong> tek bir OpenAI uyumlu API, 150+ model aras\u0131nda insan g\u00fcc\u00fcyle y\u00f6nlendirme ve yerle\u015fik maliyet & gecikme g\u00f6r\u00fcn\u00fcrl\u00fc\u011f\u00fc isteyen ekipler i\u00e7in nerede uygun oldu\u011funu g\u00f6sterir\u2014b\u00f6ylece daha h\u0131zl\u0131 g\u00f6nderebilir ve daha ak\u0131ll\u0131ca harcayabilirsiniz. Model ke\u015ffi i\u00e7in, Model Pazar\u0131<\/a> ve ile in\u015fa etmeye ba\u015flay\u0131n API Referans\u0131<\/a>.<\/p>\n\n\n\n

Neden LLM API Sa\u011flay\u0131c\u0131lar\u0131 2026 \u00d6nemli<\/h2>\n\n\n\n

Prototipten \u00fcretime: g\u00fcvenilirlik, gecikme, maliyet, gizlilik<\/h3>\n\n\n\n

G\u00fcvenilirlik:<\/strong> \u00fcretim trafi\u011fi, patlamalar, yeniden denemeler, geri d\u00f6n\u00fc\u015fler ve SLA g\u00f6r\u00fc\u015fmeleri anlam\u0131na gelir\u2014sadece m\u00fckemmel bir demo yolu de\u011fil.<\/p>\n\n\n\n

Gecikme:<\/strong> ilk token s\u00fcresi (TTFT)<\/em> ve saniye ba\u015f\u0131na token UX (sohbet, ajanlar) ve altyap\u0131 maliyeti (kaydedilen hesaplama dakikalar\u0131) i\u00e7in \u00f6nemlidir.<\/p>\n\n\n\n

Maliyet:<\/strong> tokenlar birikir. G\u00f6rev ba\u015f\u0131na do\u011fru modele y\u00f6nlendirme, \u00f6l\u00e7eklendirme s\u0131ras\u0131nda harcamay\u0131 \u00e7ift haneli y\u00fczdelerle azaltabilir.<\/p>\n\n\n\n

Gizlilik ve uyumluluk:<\/strong> veri i\u015fleme, b\u00f6lge ikameti ve saklama politikalar\u0131 tedarik i\u00e7in temel gerekliliklerdir.<\/p>\n\n\n\n

Tedarikin \u00f6nemsedi\u011fi ile geli\u015ftiricilerin ihtiya\u00e7 duydu\u011fu \u015feyler<\/h3>\n\n\n\n

Tedarik:<\/strong> SLA'lar, denetim g\u00fcnl\u00fckleri, DPA'lar, SOC2\/HIPAA\/ISO beyanlar\u0131, b\u00f6lgesellik ve maliyet \u00f6ng\u00f6r\u00fclebilirli\u011fi.<\/p>\n\n\n\n

Geli\u015ftiriciler:<\/strong> model \u00e7e\u015fitlili\u011fi, TTFT\/saniye ba\u015f\u0131na token, ak\u0131\u015f kararl\u0131l\u0131\u011f\u0131, ba\u011flam pencereleri, g\u00f6mme kalitesi, ince ayar ve s\u00fcrt\u00fcnmesiz model ge\u00e7i\u015fi. Ke\u015ffedin Belgeler Ana Sayfas\u0131<\/a> ve Playground'da<\/a>.<\/p>\n\n\n\n

TL;DR konumland\u0131rma\u2014pazar yeri vs. tek sa\u011flay\u0131c\u0131 vs. ShareAI<\/h3>\n\n\n\n

Tek sa\u011flay\u0131c\u0131 API'leri:<\/strong> basitle\u015ftirilmi\u015f s\u00f6zle\u015fmeler; s\u0131n\u0131rl\u0131 model se\u00e7imi; potansiyel premium fiyatland\u0131rma.<\/p>\n\n\n\n

Pazar yerleri\/y\u00f6nlendiriciler:<\/strong> tek bir API \u00fczerinden bir\u00e7ok model; fiyat\/performans kar\u015f\u0131la\u015ft\u0131rmas\u0131; sa\u011flay\u0131c\u0131lar aras\u0131nda yedekleme.<\/p>\n\n\n\n

ShareAI:<\/strong> insan g\u00fcc\u00fcyle \u00e7al\u0131\u015fan pazar yeri + varsay\u0131lan olarak g\u00f6zlemlenebilirlik + OpenAI uyumlu + kilitlenme yok.<\/p>\n\n\n\n

LLM API Sa\u011flay\u0131c\u0131lar\u0131 2026: Genel Bak\u0131\u015f Kar\u015f\u0131la\u015ft\u0131rmas\u0131<\/h2>\n\n\n\n

Bunlar, se\u00e7enekleri k\u0131sa listeye almak i\u00e7in y\u00f6nlendirme anl\u0131k g\u00f6r\u00fcnt\u00fcleridir. Fiyatland\u0131rma ve model varyantlar\u0131 s\u0131k s\u0131k de\u011fi\u015fir; taahh\u00fctte bulunmadan \u00f6nce her sa\u011flay\u0131c\u0131yla do\u011frulay\u0131n.<\/em><\/p>\n\n\n\n

Sa\u011flay\u0131c\u0131<\/th>Tipik Fiyatland\u0131rma Modeli<\/th>Gecikme \u00d6zellikleri (TTFT \/ Verim)<\/th>Ba\u011flam Penceresi (tipik)<\/th>Kapsam \/ Notlar<\/th><\/tr><\/thead>
ShareAI (y\u00f6nlendirici)<\/strong><\/td>Y\u00f6nlendirilen sa\u011flay\u0131c\u0131ya g\u00f6re de\u011fi\u015fir; politika tabanl\u0131 (maliyet\/gecikme)<\/td>Se\u00e7ilen rotaya ba\u011fl\u0131d\u0131r; otomatik hata tolerans\u0131 ve b\u00f6lgesel se\u00e7imler<\/td>Sa\u011flay\u0131c\u0131ya ba\u011fl\u0131<\/td>150+ model; OpenAI-uyumlu; yerle\u015fik g\u00f6zlemlenebilirlik; politika y\u00f6nlendirme; hata tolerans\u0131; BYOI<\/strong> desteklenir<\/td><\/tr>
Birlikte AI<\/strong><\/td>Modele g\u00f6re token ba\u015f\u0131na<\/td>Optimize edilmi\u015f y\u0131\u011f\u0131nlarda 100ms alt\u0131 iddialar<\/td>128k+ kadar<\/td>200+ OSS modeli; ince ayar<\/td><\/tr>
Havai Fi\u015fekler AI<\/strong><\/td>Token ba\u015f\u0131na; sunucusuz ve iste\u011fe ba\u011fl\u0131<\/td>\u00c7ok d\u00fc\u015f\u00fck TTFT; g\u00fc\u00e7l\u00fc \u00e7ok modlu<\/td>128k\u2013164k<\/td>Metin+g\u00f6r\u00fcnt\u00fc+ses; FireAttention<\/td><\/tr>
OpenRouter (y\u00f6nlendirici)<\/strong><\/td>Modele \u00f6zg\u00fc (de\u011fi\u015fken)<\/td>Temel sa\u011flay\u0131c\u0131ya ba\u011fl\u0131<\/td>Sa\u011flay\u0131c\u0131ya \u00f6zg\u00fc<\/td>Tek bir API ile ~300+ model<\/td><\/tr>
Hiperbolik<\/strong><\/td>D\u00fc\u015f\u00fck token ba\u015f\u0131na; indirim odakl\u0131<\/td>H\u0131zl\u0131 model entegrasyonu<\/td>~131k<\/td>API + uygun fiyatl\u0131 GPU'lar<\/td><\/tr>
\u00c7o\u011falt<\/strong><\/td>Sorgu ba\u015f\u0131na kullan\u0131m<\/td>Topluluk modeline g\u00f6re de\u011fi\u015fir<\/td>Modele \u00f6zg\u00fc<\/td>Uzun kuyruk modelleri; h\u0131zl\u0131 prototipler<\/td><\/tr>
Hugging Face<\/strong><\/td>Bar\u0131nd\u0131r\u0131lan API'ler \/ kendi kendine bar\u0131nd\u0131rma<\/td>Donan\u0131m ba\u011f\u0131ml\u0131<\/td>128k+ kadar<\/td>OSS merkezi + kurumsal k\u00f6pr\u00fcler<\/td><\/tr>
Groq<\/strong><\/td>Token ba\u015f\u0131na<\/td>Ultra d\u00fc\u015f\u00fck TTFT<\/strong> (LPU)<\/td>~128k<\/td>Donan\u0131m h\u0131zland\u0131rmal\u0131 \u00e7\u0131kar\u0131m<\/td><\/tr>
DeepInfra<\/strong><\/td>Token ba\u015f\u0131na \/ \u00f6zel<\/td>\u00d6l\u00e7ekli kararl\u0131 \u00e7\u0131kar\u0131m<\/td>64k\u2013128k<\/td>\u00d6zel u\u00e7 noktalar mevcut<\/td><\/tr>
Karma\u015f\u0131kl\u0131k (pplx-api)<\/strong><\/td>Kullan\u0131m \/ abonelik<\/td>Arama\/QA i\u00e7in optimize edilmi\u015f<\/td>128k'ya kadar<\/td>Yeni OSS modellerine h\u0131zl\u0131 eri\u015fim<\/td><\/tr>
Anyscale<\/strong><\/td>Kullan\u0131m; kurumsal<\/td>Ray-native \u00f6l\u00e7ek<\/td>\u0130\u015f y\u00fck\u00fcne ba\u011fl\u0131<\/td>Ray \u00fczerinde u\u00e7tan uca platform<\/td><\/tr>
Novita AI<\/strong><\/td>Token ba\u015f\u0131na \/ saniye ba\u015f\u0131na<\/td>D\u00fc\u015f\u00fck maliyet + h\u0131zl\u0131 so\u011fuk ba\u015flang\u0131\u00e7lar<\/td>~64k<\/td>Sunucusuz + \u00f6zel GPU'lar<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

Metodoloji notu:<\/em> bildirilen TTFT\/token\/sn, istem uzunlu\u011fu, \u00f6nbellekleme, toplama ve sunucu yerel konumuna g\u00f6re de\u011fi\u015fir. Say\u0131lar\u0131 mutlak de\u011fil, g\u00f6receli g\u00f6stergeler olarak de\u011ferlendirin. H\u0131zl\u0131 bir anl\u0131k g\u00f6r\u00fcnt\u00fc i\u00e7in LLM API sa\u011flay\u0131c\u0131lar\u0131 2026<\/strong>, yukar\u0131daki fiyatland\u0131rmay\u0131, TTFT'yi, ba\u011flam pencerelerini ve model geni\u015fli\u011fini kar\u015f\u0131la\u015ft\u0131r\u0131n.<\/p>\n\n\n\n

ShareAI'nin 2026'da LLM API Sa\u011flay\u0131c\u0131lar\u0131 Aras\u0131ndaki Yeri<\/h2>\n\n\n\n

\u0130nsan g\u00fcc\u00fcyle \u00e7al\u0131\u015fan pazar yeri: 150+ model, esnek y\u00f6nlendirme, kilitlenme yok<\/h3>\n\n\n\n

ShareAI, en iyi modelleri (OSS ve tescilli) tek bir OpenAI uyumlu API'nin arkas\u0131nda toplar. Model ad\u0131na veya politikaya g\u00f6re (bir g\u00f6rev i\u00e7in en ucuz, en h\u0131zl\u0131, en do\u011fru) iste\u011fe ba\u011fl\u0131 y\u00f6nlendirme yap\u0131n, bir b\u00f6lge veya modelde aksama oldu\u011funda otomatik olarak devreye girin ve uygulaman\u0131z\u0131 yeniden yazmadan modelleri tek bir sat\u0131rla de\u011fi\u015ftirin. Konsol Genel Bak\u0131\u015f<\/a>.<\/p>\n\n\n\n

Varsay\u0131lan olarak maliyet kontrol\u00fc ve g\u00f6zlemlenebilirlik<\/h3>\n\n\n\n

Ger\u00e7ek zamanl\u0131 token, gecikme, hata ve maliyet takibini istek ve kullan\u0131c\u0131 d\u00fczeyinde al\u0131n. Sa\u011flay\u0131c\u0131\/model baz\u0131nda ayr\u0131nt\u0131lara inerek gerilemeleri yakalay\u0131n ve y\u00f6nlendirme politikalar\u0131n\u0131 optimize edin. Sat\u0131n alma dostu raporlama, kullan\u0131m trendlerini, birim ekonomisini ve denetim izlerini i\u00e7erir. Aras\u0131nda LLM API sa\u011flay\u0131c\u0131lar\u0131 2026<\/strong>, ShareAI y\u00f6nlendirme, yedekleme, g\u00f6zlemlenebilirlik ve BYOI ile kontrol d\u00fczlemi olarak hareket eder.<\/p>\n\n\n\n

Tek API, bir\u00e7ok sa\u011flay\u0131c\u0131: s\u0131f\u0131r ge\u00e7i\u015f s\u00fcrt\u00fcnmesi<\/h3>\n\n\n\n

ShareAI, SDK'lar\u0131n\u0131z\u0131 koruyabilmeniz i\u00e7in OpenAI uyumlu bir aray\u00fcz kullan\u0131r. Kimlik bilgileri kapsaml\u0131 kal\u0131r; gerekti\u011finde kendi anahtarlar\u0131n\u0131z\u0131 getirin. Kilitlenme yok:<\/strong> istemleriniz, g\u00fcnl\u00fckleriniz ve y\u00f6nlendirme politikalar\u0131n\u0131z ta\u015f\u0131nabilir. G\u00f6nderime haz\u0131r oldu\u011funuzda, en son S\u00fcr\u00fcm Notlar\u0131n\u0131 kontrol edin<\/a>.<\/p>\n\n\n\n

5 dakikada deneyin (yap\u0131c\u0131 odakl\u0131 kod)<\/h3>\n\n\n\n
curl -s https:\/\/api.shareai.now\/api\/v1\/chat\/completions \\\"<\/code><\/pre>\n\n\n\n
Deneme yapmak i\u00e7in LLM API sa\u011flay\u0131c\u0131lar\u0131 2026<\/strong> yeniden d\u00fczenlemeler olmadan, yukar\u0131daki ShareAI\u2019nin OpenAI uyumlu u\u00e7 noktas\u0131n\u0131 kullanarak y\u00f6nlendirin ve sonu\u00e7lar\u0131 ger\u00e7ek zamanl\u0131 olarak kar\u015f\u0131la\u015ft\u0131r\u0131n.<\/p>\n\n\n\n
Do\u011fru LLM API Sa\u011flay\u0131c\u0131s\u0131n\u0131 Nas\u0131l Se\u00e7ersiniz (2026)<\/h2>\n\n\n\n
Karar matrisi (gecikme, maliyet, gizlilik, \u00f6l\u00e7ek, model eri\u015fimi)<\/h3>\n\n\n\n
Gecikme kritik sohbet\/ajanlar:<\/strong> Groq, Fireworks, Together; veya b\u00f6lgeye g\u00f6re en h\u0131zl\u0131ya y\u00f6nlendirme i\u00e7in ShareAI.<\/p>\n\n\n\n
Maliyet duyarl\u0131 toplu i\u015flem:<\/strong> Hiperbolik, Novita, DeepInfra; veya ShareAI maliyet optimize edilmi\u015f politika.<\/p>\n\n\n\n
Model \u00e7e\u015fitlili\u011fi \/ h\u0131zl\u0131 ge\u00e7i\u015f:<\/strong> OpenRouter; veya ShareAI \u00e7ok sa\u011flay\u0131c\u0131l\u0131 ve yedeklemeli.<\/p>\n\n\n\n
Kurumsal y\u00f6netim:<\/strong> Anyscale (Ray), DeepInfra (\u00f6zel), art\u0131 ShareAI raporlar\u0131 ve denetlenebilirlik.<\/p>\n\n\n\n
\u00c7ok modlu (metin+g\u00f6r\u00fcnt\u00fc+ses):<\/strong> Fireworks, Together, Replicate; ShareAI bunlar aras\u0131nda y\u00f6nlendirme yapabilir. Daha derin bir kurulum i\u00e7in, \u015furadan ba\u015flay\u0131n Belgeler Ana Sayfas\u0131<\/a>.<\/p>\n\n\n\n
Tak\u0131mlar k\u0131sa listeleme LLM API sa\u011flay\u0131c\u0131lar\u0131 2026<\/strong> TTFT ve maliyeti do\u011frulamak i\u00e7in hizmet verdikleri b\u00f6lgede test yapmal\u0131d\u0131r.<\/p>\n\n\n\n
\u0130\u015f y\u00fckleri: sohbet uygulamalar\u0131, RAG, ajanlar, toplu i\u015flem, \u00e7ok modlu<\/h3>\n\n\n\n
Sohbet UX:<\/strong> TTFT ve saniye ba\u015f\u0131na tokenleri \u00f6nceliklendirin; ak\u0131\u015f kararl\u0131l\u0131\u011f\u0131 \u00f6nemlidir.<\/p>\n\n\n\n
RAG:<\/strong> g\u00f6mme kalitesi + pencere boyutu + maliyet.<\/p>\n\n\n\n
Ajanlar\/ara\u00e7lar:<\/strong> sa\u011flam i\u015flev \u00e7a\u011f\u0131rma; zaman a\u015f\u0131m\u0131 kontrolleri; yeniden denemeler.<\/p>\n\n\n\n
Toplu\/\u00e7evrimd\u0131\u015f\u0131:<\/strong> 1M token ba\u015f\u0131na verimlilik ve $ bask\u0131n gelir.<\/p>\n\n\n\n
\u00c7ok modlu:<\/strong> model kullan\u0131labilirli\u011fi ve metin d\u0131\u015f\u0131 tokenlar\u0131n maliyeti.<\/p>\n\n\n\n
Tedarik kontrol listesi (SLA, DPA, b\u00f6lge, veri saklama)<\/h3>\n\n\n\n
SLA hedeflerini ve kredilerini, DPA \u015fartlar\u0131n\u0131 (i\u015fleme, alt i\u015flemciler), b\u00f6lge se\u00e7imini ve istem\/\u00e7\u0131kt\u0131lar i\u00e7in saklama politikas\u0131n\u0131 onaylay\u0131n. G\u00f6zlemlenebilirlik kancalar\u0131 (ba\u015fl\u0131klar, web kancalar\u0131, d\u0131\u015fa aktarma), ince ayar veri kontrolleri ve gerekiyorsa BYOK\/BYOI se\u00e7eneklerini isteyin. \u015euna bak\u0131n Sa\u011flay\u0131c\u0131 K\u0131lavuzu<\/a> kapasite getirmeyi planl\u0131yorsan\u0131z.<\/p>\n\n\n\n
En \u0130yi 12 LLM API Sa\u011flay\u0131c\u0131s\u0131 2026<\/h2>\n\n\n\n
Her profil, \u201cen uygun\u201d bir \u00f6zet, neden geli\u015ftiricilerin bunu se\u00e7ti\u011fi, h\u0131zl\u0131 bir fiyatland\u0131rma \u00f6zeti ve ShareAI ile nas\u0131l uyum sa\u011flad\u0131\u011f\u0131na dair notlar i\u00e7erir. Bunlar LLM API sa\u011flay\u0131c\u0131lar\u0131 2026<\/strong> \u00fcretim i\u00e7in en s\u0131k de\u011ferlendirilenlerdir.<\/em><\/p>\n\n\n\n
1) ShareAI \u2014 \u00e7ok sa\u011flay\u0131c\u0131l\u0131 y\u00f6nlendirme, g\u00f6zlemlenebilirlik ve BYOI i\u00e7in en iyisi<\/h3>\n\n\n\n
\"\"<\/figure>\n\n\n\n
Neden geli\u015ftiriciler bunu se\u00e7iyor:<\/strong> 150'den fazla model i\u00e7in tek bir OpenAI uyumlu API, politika tabanl\u0131 y\u00f6nlendirme (maliyet\/gecikme\/do\u011fruluk), otomatik hata tolerans\u0131, ger\u00e7ek zamanl\u0131 maliyet ve gecikme analiti\u011fi ve \u00f6zel kapasite veya uyumluluk kontrol\u00fcne ihtiya\u00e7 duydu\u011funuzda BYOI.<\/p>\n\n\n\n
Fiyatland\u0131rma bir bak\u0131\u015fta:<\/strong> y\u00f6nlendirilmi\u015f sa\u011flay\u0131c\u0131n\u0131n fiyatland\u0131rmas\u0131n\u0131 takip eder; maliyet-optimize edilmi\u015f veya gecikme-optimize edilmi\u015f politikalar\u0131 (veya belirli bir sa\u011flay\u0131c\u0131\/model) se\u00e7ersiniz.<\/p>\n\n\n\n
Notlar:<\/strong> yeniden yap\u0131land\u0131rma olmadan sa\u011flay\u0131c\u0131lar\u0131 de\u011fi\u015ftirme \u00f6zg\u00fcrl\u00fc\u011f\u00fc isteyen ekipler i\u00e7in ideal \u201ckontrol d\u00fczlemi\u201d, kullan\u0131m\/maliyet raporlar\u0131yla sat\u0131n almay\u0131 mutlu tutar ve \u00fcretimde k\u0131yaslama yapar.<\/p>\n\n\n\n
2) Together AI \u2014 y\u00fcksek \u00f6l\u00e7ekli a\u00e7\u0131k kaynakl\u0131 LLM'ler i\u00e7in en iyisi<\/h3>\n\n\n\n
\"\"<\/figure>\n\n\n\n
Neden geli\u015ftiriciler bunu se\u00e7iyor:<\/strong> OSS'de m\u00fckemmel fiyat\/performans (\u00f6rne\u011fin, Llama-3 s\u0131n\u0131f\u0131), ince ayar deste\u011fi, 100ms alt\u0131 iddialar, geni\u015f katalog.<\/p>\n\n\n\n
Fiyatland\u0131rma bir bak\u0131\u015fta:<\/strong> modele g\u00f6re token ba\u015f\u0131na; denemeler i\u00e7in \u00fccretsiz krediler mevcut olabilir.<\/p>\n\n\n\n
ShareAI uyumu:<\/strong> y\u00f6nlendirme birlikte\/<model-id><\/code> veya bir ShareAI maliyet-optimize edilmi\u015f politika, b\u00f6lgenizde en ucuz oldu\u011funda Together'\u0131 se\u00e7sin.<\/p>\n\n\n\n
3) Fireworks AI \u2014 d\u00fc\u015f\u00fck gecikmeli \u00e7ok modlu i\u00e7in en iyisi<\/h3>\n\n\n\n
\"\"<\/figure>\n\n\n\n
Neden geli\u015ftiriciler bunu se\u00e7iyor:<\/strong> \u00e7ok h\u0131zl\u0131 TTFT, FireAttention motoru, metin+g\u00f6r\u00fcnt\u00fc+ses, SOC2\/HIPAA se\u00e7enekleri.<\/p>\n\n\n\n
Fiyatland\u0131rma bir bak\u0131\u015fta:<\/strong> kulland\u0131k\u00e7a \u00f6de (sunucusuz veya iste\u011fe ba\u011fl\u0131).<\/p>\n\n\n\n
ShareAI uyumu:<\/strong> \u00e7a\u011fr\u0131 havai fi\u015fekler\/<model-id><\/code> do\u011frudan veya politika y\u00f6nlendirmesinin \u00e7ok modlu istemler i\u00e7in Fireworks'u se\u00e7mesine izin verin.<\/p>\n\n\n\n
4) OpenRouter \u2014 bir\u00e7ok sa\u011flay\u0131c\u0131ya tek bir API eri\u015fimi i\u00e7in en iyisi<\/h3>\n\n\n\n
\"\"<\/figure>\n\n\n\n
Neden geli\u015ftiriciler bunu se\u00e7iyor:<\/strong> ~300+ modeli birle\u015fik bir API arkas\u0131nda; h\u0131zl\u0131 model ke\u015ffi i\u00e7in iyi.<\/p>\n\n\n\n
Fiyatland\u0131rma bir bak\u0131\u015fta:<\/strong> model ba\u015f\u0131na fiyatland\u0131rma; baz\u0131 \u00fccretsiz katmanlar.<\/p>\n\n\n\n
ShareAI uyumu:<\/strong> ShareAI ayn\u0131 \u00e7ok sa\u011flay\u0131c\u0131l\u0131 ihtiyac\u0131 kar\u015f\u0131lar ancak politika y\u00f6nlendirme + g\u00f6zlemlenebilirlik + sat\u0131n alma d\u00fczeyinde raporlar ekler.<\/p>\n\n\n\n
5) Hyperbolic \u2014 agresif maliyet tasarrufu ve h\u0131zl\u0131 model da\u011f\u0131t\u0131m\u0131 i\u00e7in en iyisi<\/h3>\n\n\n\n
\"\"<\/figure>\n\n\n\n
Neden geli\u015ftiriciler bunu se\u00e7iyor:<\/strong> tutarl\u0131 d\u00fc\u015f\u00fck token ba\u015f\u0131na fiyatlar, yeni a\u00e7\u0131k kaynak modeller i\u00e7in h\u0131zl\u0131 kurulum ve daha a\u011f\u0131r i\u015fler i\u00e7in uygun fiyatl\u0131 GPU'lara eri\u015fim.<\/p>\n\n\n\n
Fiyatland\u0131rma bir bak\u0131\u015fta:<\/strong> ba\u015flamak \u00fccretsiz; kulland\u0131k\u00e7a \u00f6de.<\/p>\n\n\n\n
ShareAI uyumu:<\/strong> trafi\u011fi y\u00f6nlendirin hiperbolik\/<\/code> en d\u00fc\u015f\u00fck maliyetli \u00e7al\u0131\u015ft\u0131rmalar i\u00e7in veya \u00f6zel bir politika belirleyin (\u00f6rne\u011fin, \u201cmaliyet-sonra-gecikme\u201d) b\u00f6ylece ShareAI Hyperbolic'i tercih eder ancak ani art\u0131\u015flar s\u0131ras\u0131nda bir sonraki en ucuz sa\u011fl\u0131kl\u0131 rotaya otomatik ge\u00e7i\u015f yapar.<\/p>\n\n\n\n
6) Replicate \u2014 prototipleme ve uzun kuyruk modelleri i\u00e7in en iyisi<\/h3>\n\n\n\n
\"\"<\/figure>\n\n\n\n
Neden geli\u015ftiriciler bunu se\u00e7iyor:<\/strong> b\u00fcy\u00fck topluluk katalo\u011fu (metin, g\u00f6r\u00fcnt\u00fc, ses, ni\u015f modeller), h\u0131zl\u0131 MVP'ler i\u00e7in tek sat\u0131rl\u0131k da\u011f\u0131t\u0131mlar.<\/p>\n\n\n\n
Fiyatland\u0131rma bir bak\u0131\u015fta:<\/strong> sorgu ba\u015f\u0131na; model konteynerine g\u00f6re de\u011fi\u015fir.<\/p>\n\n\n\n
ShareAI uyumu:<\/strong> ke\u015fif i\u00e7in harika; \u00f6l\u00e7eklendirme s\u0131ras\u0131nda, gecikme\/maliyet kar\u015f\u0131la\u015ft\u0131rmas\u0131 yapmak i\u00e7in ShareAI \u00fczerinden y\u00f6nlendirin, kod de\u011fi\u015fikli\u011fi olmadan.<\/p>\n\n\n\n
7) Hugging Face \u2014 OSS ekosistemi ve kurumsal k\u00f6pr\u00fcler i\u00e7in en iyisi<\/h3>\n\n\n\n
\"\"<\/figure>\n\n\n\n
Neden geli\u015ftiriciler bunu se\u00e7iyor:<\/strong> model merkezi + veri setleri; bar\u0131nd\u0131r\u0131lan \u00e7\u0131kar\u0131m veya kendi bulutunuzda bar\u0131nd\u0131rma; g\u00fc\u00e7l\u00fc kurumsal MLOps k\u00f6pr\u00fcleri.<\/p>\n\n\n\n
Fiyatland\u0131rma bir bak\u0131\u015fta:<\/strong> temel \u00f6zellikler i\u00e7in \u00fccretsiz; kurumsal planlar mevcut.<\/p>\n\n\n\n
ShareAI uyumu:<\/strong> OSS modellerinizi koruyun ve bir uygulamada HF u\u00e7 noktalar\u0131n\u0131 di\u011fer sa\u011flay\u0131c\u0131larla kar\u0131\u015ft\u0131rmak i\u00e7in ShareAI \u00fczerinden y\u00f6nlendirin.<\/p>\n\n\n\n
8) Groq \u2014 ultra d\u00fc\u015f\u00fck gecikme (LPU) i\u00e7in en iyisi<\/h3>\n\n\n\n
\"\"<\/figure>\n\n\n\n
Neden geli\u015ftiriciler bunu se\u00e7iyor:<\/strong> sohbet\/ajanlar i\u00e7in sekt\u00f6r lideri TTFT\/saniye ba\u015f\u0131na token ile donan\u0131m h\u0131zland\u0131rmal\u0131 \u00e7\u0131kar\u0131m.<\/p>\n\n\n\n
Fiyatland\u0131rma bir bak\u0131\u015fta:<\/strong> token ba\u015f\u0131na; kurumsal dostu.<\/p>\n\n\n\n
ShareAI uyumu:<\/strong> kullan groq\/<model-id><\/code> gecikme duyarl\u0131 yollar i\u00e7in; dayan\u0131kl\u0131l\u0131k i\u00e7in ShareAI yedekleme ayar\u0131n\u0131 GPU yollar\u0131na yap\u0131n.<\/p>\n\n\n\n
9) DeepInfra \u2014 \u00f6zel bar\u0131nd\u0131rma ve maliyet etkin \u00e7\u0131kar\u0131m i\u00e7in en iyisi<\/h3>\n\n\n\n
\"\"<\/figure>\n\n\n\n
Neden geli\u015ftiriciler bunu se\u00e7iyor:<\/strong> OpenAI tarz\u0131 desenlerle kararl\u0131 API; \u00f6zel\/genel LLM'ler i\u00e7in \u00f6zel u\u00e7 noktalar.<\/p>\n\n\n\n
Fiyatland\u0131rma bir bak\u0131\u015fta:<\/strong> token ba\u015f\u0131na veya y\u00fcr\u00fctme s\u00fcresi; \u00f6zel \u00f6rnek fiyatland\u0131rmas\u0131 mevcut.<\/p>\n\n\n\n
ShareAI uyumu:<\/strong> ShareAI \u00fczerinden sa\u011flay\u0131c\u0131lar aras\u0131 analizleri korurken \u00f6zel kapasiteye ihtiya\u00e7 duydu\u011funuzda faydal\u0131d\u0131r.<\/p>\n\n\n\n
10) Perplexity (pplx-api) \u2014 arama\/QA entegrasyonlar\u0131 i\u00e7in en iyisi<\/h3>\n\n\n\n
\"\"<\/figure>\n\n\n\n
Neden geli\u015ftiriciler bunu se\u00e7iyor:<\/strong> yeni OSS modellerine h\u0131zl\u0131 eri\u015fim, basit REST API, bilgi alma ve QA i\u00e7in g\u00fc\u00e7l\u00fc.<\/p>\n\n\n\n
Fiyatland\u0131rma bir bak\u0131\u015fta:<\/strong> kullan\u0131m tabanl\u0131; Pro genellikle ayl\u0131k API kredilerini i\u00e7erir.<\/p>\n\n\n\n
ShareAI uyumu:<\/strong> bir ShareAI projesi alt\u0131nda alma i\u00e7in pplx-api'yi ba\u015fka bir sa\u011flay\u0131c\u0131yla olu\u015fturma i\u00e7in kar\u0131\u015ft\u0131r\u0131n.<\/p>\n\n\n\n
11) Anyscale \u2014 Ray \u00fczerinde u\u00e7tan uca \u00f6l\u00e7ekleme i\u00e7in en iyisi<\/h3>\n\n\n\n
\"\"<\/figure>\n\n\n\n
Neden geli\u015ftiriciler bunu se\u00e7iyor:<\/strong> e\u011fitim \u2192 hizmet \u2192 Ray \u00fczerinde toplu i\u015flem; kurumsal platform ekipleri i\u00e7in y\u00f6netim\/administrasyon \u00f6zellikleri.<\/p>\n\n\n\n
Fiyatland\u0131rma bir bak\u0131\u015fta:<\/strong> kullan\u0131m tabanl\u0131; kurumsal se\u00e7enekler.<\/p>\n\n\n\n
ShareAI uyumu:<\/strong> altyap\u0131y\u0131 Ray \u00fczerinde standartla\u015ft\u0131r\u0131n, ard\u0131ndan ShareAI'yi uygulama kenar\u0131nda \u00e7apraz sa\u011flay\u0131c\u0131 y\u00f6nlendirme ve birle\u015fik analizler i\u00e7in kullan\u0131n.<\/p>\n\n\n\n
12) Novita AI \u2014 d\u00fc\u015f\u00fck maliyetle sunucusuz + \u00f6zel GPU i\u00e7in en iyisi<\/h3>\n\n\n\n
\"\"<\/figure>\n\n\n\n
Neden geli\u015ftiriciler bunu se\u00e7iyor:<\/strong> saniye ba\u015f\u0131na faturaland\u0131rma, h\u0131zl\u0131 so\u011fuk ba\u015flang\u0131\u00e7lar, k\u00fcresel GPU a\u011f\u0131; hem sunucusuz hem de \u00f6zel \u00f6rnekler.<\/p>\n\n\n\n
Fiyatland\u0131rma bir bak\u0131\u015fta:<\/strong> token ba\u015f\u0131na (LLM) veya saniye ba\u015f\u0131na (GPU); kurumsal i\u00e7in \u00f6zel u\u00e7 noktalar.<\/p>\n\n\n\n
ShareAI uyumu:<\/strong> toplu i\u015flem maliyet tasarruflar\u0131 i\u00e7in g\u00fc\u00e7l\u00fc; b\u00f6lge\/fiyat baz\u0131nda Novita ve e\u015fleri aras\u0131nda ge\u00e7i\u015f yapmak i\u00e7in ShareAI y\u00f6nlendirmesini koruyun.<\/p>\n\n\n\n
H\u0131zl\u0131 Ba\u015flang\u0131\u00e7: ShareAI \u00dczerinden Herhangi Bir Sa\u011flay\u0131c\u0131y\u0131 Y\u00f6nlendirin (G\u00f6zlemlenebilirlik Dahil)<\/h2>\n\n\n\n
OpenAI uyumlu \u00f6rnek (sohbet tamamlama)<\/h3>\n\n\n\n
curl -s https:\/\/api.shareai.now\/api\/v1\/chat\/completions \\\"<\/code><\/pre>\n\n\n\n
Sa\u011flay\u0131c\u0131lar\u0131 tek bir sat\u0131rla de\u011fi\u015ftirme<\/h3>\n\n\n\n
{\n  \"model\": \"growably\/deepseek-r1:70b\",\n  \"messages\": [\n    {\"role\": \"user\", \"content\": \"Latency matters for agents\u2014explain why.\"}\n  ]\n}<\/code><\/pre>\n\n\n\n
Deneme yapmak i\u00e7in LLM API sa\u011flay\u0131c\u0131lar\u0131 2026<\/strong> h\u0131zl\u0131ca, ayn\u0131 y\u00fck\u00fc koruyun ve sadece de\u011fi\u015ftirin model<\/code> veya bir y\u00f6nlendirici politikas\u0131 se\u00e7in.<\/p>\n\n\n\n
Kar\u015f\u0131la\u015ft\u0131rma Notlar\u0131 ve Uyar\u0131lar<\/h2>\n\n\n\n
Tokenizasyon farkl\u0131l\u0131klar\u0131<\/strong> sa\u011flay\u0131c\u0131lar aras\u0131nda toplam token say\u0131lar\u0131n\u0131 de\u011fi\u015ftirir.<\/p>\n\n\n\n
Gruplama ve \u00f6nbellekleme<\/strong> tekrarlanan istemlerde TTFT'nin ger\u00e7ek\u00e7i olmayan \u015fekilde d\u00fc\u015f\u00fck g\u00f6r\u00fcnmesine neden olabilir.<\/p>\n\n\n\n
Sunucu yerelli\u011fi<\/strong> \u00f6nemlidir: kullan\u0131c\u0131lar\u0131 hizmet verdi\u011finiz b\u00f6lgeden \u00f6l\u00e7\u00fcn.<\/p>\n\n\n\n
Ba\u011flam penceresi pazarlamas\u0131<\/strong> tam hikaye de\u011fildir\u2014kesme davran\u0131\u015f\u0131na ve s\u0131n\u0131rlar yak\u0131n\u0131ndaki etkili verimlili\u011fe bak\u0131n.<\/p>\n\n\n\n
Fiyatland\u0131rma anl\u0131k g\u00f6r\u00fcnt\u00fcleri:<\/strong> taahh\u00fctte bulunmadan \u00f6nce her zaman mevcut fiyatland\u0131rmay\u0131 do\u011frulay\u0131n. Haz\u0131r oldu\u011funuzda, dan\u0131\u015f\u0131n S\u00fcr\u00fcmler<\/a> ve Blog Ar\u015fivi<\/a> g\u00fcncellemeler i\u00e7in.<\/p>\n\n\n\n
SSS: LLM API Sa\u011flay\u0131c\u0131lar\u0131 2026<\/h2>\n\n\n\n
LLM API sa\u011flay\u0131c\u0131s\u0131 nedir?<\/h3>\n\n\n\n
Bir LLM API sa\u011flay\u0131c\u0131s\u0131<\/strong> HTTP API'leri veya SDK'lar arac\u0131l\u0131\u011f\u0131yla b\u00fcy\u00fck dil modellerine hizmet olarak \u00e7\u0131kar\u0131m eri\u015fimi sunar. Kendi GPU filonuzu y\u00f6netmeden \u00f6l\u00e7eklenebilirlik, izleme ve SLA'lar elde edersiniz.<\/p>\n\n\n\n
A\u00e7\u0131k kaynak vs tescilli: \u00fcretim i\u00e7in hangisi daha iyi?<\/h3>\n\n\n\n
A\u00e7\u0131k kaynak<\/strong> (\u00f6r. Llama-3 s\u0131n\u0131f\u0131) maliyet kontrol\u00fc, \u00f6zelle\u015ftirme ve ta\u015f\u0131nabilirlik sunar; tescilli<\/strong> modeller belirli \u00f6l\u00e7\u00fctlerde ve kolayl\u0131kta \u00f6ne \u00e7\u0131kabilir. Bir\u00e7ok ekip her ikisini harmanlar\u2014ShareAI<\/strong> bu kar\u0131\u015f\u0131k y\u00f6nlendirmeyi \u00f6nemsiz hale getirir.<\/p>\n\n\n\n
Together AI vs Fireworks \u2014 hangisi \u00e7ok modlu i\u00e7in daha h\u0131zl\u0131?<\/h3>\n\n\n\n
Havai fi\u015fekler<\/strong> d\u00fc\u015f\u00fck TTFT ve g\u00fc\u00e7l\u00fc bir \u00e7ok modlu y\u0131\u011f\u0131n ile bilinir; Birlikte<\/strong> geni\u015f bir OSS katalo\u011fu ve rekabet\u00e7i i\u015flem hacmi sunar. En iyi se\u00e7iminiz istem boyutuna, b\u00f6lgeye ve moduna ba\u011fl\u0131d\u0131r. ShareAI<\/strong>, ile, her ikisine y\u00f6nlendirebilir ve ger\u00e7ek sonu\u00e7lar\u0131 \u00f6l\u00e7ebilirsiniz.<\/p>\n\n\n\n
OpenRouter vs ShareAI \u2014 pazar yeri mi yoksa insan g\u00fcc\u00fcyle y\u00f6nlendirme mi?<\/h3>\n\n\n\n
OpenRouter<\/strong> bir\u00e7ok modeli tek bir API arac\u0131l\u0131\u011f\u0131yla birle\u015ftirir\u2014ke\u015fif i\u00e7in harika. ShareAI<\/strong> politika tabanl\u0131 y\u00f6nlendirme, sat\u0131n alma dostu g\u00f6zlemlenebilirlik ve insan g\u00fcc\u00fcyle k\u00fcrasyon ekler, b\u00f6ylece ekipler maliyet\/gecikmeyi optimize edebilir ve sa\u011flay\u0131c\u0131lar aras\u0131nda raporlamay\u0131 standartla\u015ft\u0131rabilir.<\/p>\n\n\n\n
Groq vs GPU Cloud \u2014 LPU ne zaman kazan\u0131r?<\/h3>\n\n\n\n
\u0130\u015f y\u00fck\u00fcn\u00fcz gecikme a\u00e7\u0131s\u0131ndan kritikse (ajanlar, etkile\u015fimli sohbet, ak\u0131\u015fl\u0131 UX), Groq LPU'lar<\/strong> sekt\u00f6r lideri TTFT\/saniye ba\u015f\u0131na token sunabilir. Hesaplama yo\u011fun toplu i\u015fler i\u00e7in, maliyet optimize edilmi\u015f GPU sa\u011flay\u0131c\u0131lar\u0131 daha ekonomik olabilir. ShareAI<\/strong> her ikisini kullanman\u0131za olanak tan\u0131r.<\/p>\n\n\n\n
DeepInfra vs Anyscale \u2014 \u00f6zel \u00e7\u0131kar\u0131m m\u0131 yoksa Ray platformu mu?<\/h3>\n\n\n\n
DeepInfra<\/strong> \u00f6zel \u00e7\u0131kar\u0131m u\u00e7 noktalar\u0131 i\u00e7in parl\u0131yor; Anyscale<\/strong> e\u011fitimden hizmete ve toplu i\u015flere kadar uzanan Ray-yerel bir platformdur. Ekipler genellikle platform orkestrasyonu i\u00e7in Anyscale'i ve ShareAI<\/strong> uygulama kenar\u0131nda \u00e7apraz sa\u011flay\u0131c\u0131 y\u00f6nlendirme ve analiz i\u00e7in kullan\u0131r.<\/p>\n\n\n\n
Novita vs Hyperbolic \u2014 \u00f6l\u00e7eklendirme s\u0131ras\u0131nda en d\u00fc\u015f\u00fck maliyet?<\/h3>\n\n\n\n
Her ikisi de agresif tasarruf vaat ediyor. Novita<\/strong> sunucusuz + \u00f6zel GPU'lar\u0131 vurgular ve saniye bazl\u0131 faturaland\u0131rma sunar; Hiperbolik<\/strong> indirimli GPU eri\u015fimi ve h\u0131zl\u0131 model y\u00fcklemeyi \u00f6ne \u00e7\u0131kar\u0131r. Her ikisini de istemlerinizle test edin; kullan\u0131n ShareAI\u2019nin<\/strong> y\u00f6nlendirici:maliyet_optimize edilmi\u015f<\/code> maliyetleri d\u00fcr\u00fcst tutmak i\u00e7in.<\/p>\n\n\n\n
Replicate vs Hugging Face \u2014 prototipleme vs ekosistem derinli\u011fi?<\/h3>\n\n\n\n
\u00c7o\u011falt<\/strong> h\u0131zl\u0131 prototipleme ve uzun kuyruklu topluluk modelleri i\u00e7in m\u00fckemmeldir; Hugging Face<\/strong> kurumsal k\u00f6pr\u00fcler ve kendi bar\u0131nd\u0131rma se\u00e7enekleriyle OSS ekosistemine liderlik eder. Her ikisini \u015fu \u015fekilde y\u00f6nlendirin ShareAI<\/strong> maliyet ve gecikme a\u00e7\u0131s\u0131ndan kar\u015f\u0131la\u015ft\u0131rmak i\u00e7in e\u015fit ko\u015fullarda.<\/p>\n\n\n\n
2026'da en maliyet etkin LLM API sa\u011flay\u0131c\u0131s\u0131 hangisi?<\/h3>\n\n\n\n
\u0130stem kar\u0131\u015f\u0131m\u0131na ve trafik \u015fekline ba\u011fl\u0131d\u0131r. Maliyet odakl\u0131 adaylar: Hiperbolik<\/strong>, Novita<\/strong>, DeepInfra<\/strong>. G\u00fcvenilir bir yan\u0131t yolu, \u00f6l\u00e7\u00fcm yapmakt\u0131r. ShareAI<\/strong> g\u00f6zlemlenebilirlik ve maliyet optimize edilmi\u015f bir y\u00f6nlendirme politikas\u0131.<\/p>\n\n\n\n
Hangi sa\u011flay\u0131c\u0131 en h\u0131zl\u0131 (TTFT)?<\/h3>\n\n\n\n
Groq<\/strong> genellikle TTFT\/saniye ba\u015f\u0131na jetonlarda \u00f6nde gelir, \u00f6zellikle sohbet UX i\u00e7in. Havai fi\u015fekler<\/strong> ve Birlikte<\/strong> ayr\u0131ca g\u00fc\u00e7l\u00fcd\u00fcr. Her zaman b\u00f6lgenizde k\u0131yaslama yap\u0131n\u2014ve ShareAI<\/strong> her istek i\u00e7in en h\u0131zl\u0131 u\u00e7 noktaya y\u00f6nlendirin.<\/p>\n\n\n\n
RAG\/ajanlar\/toplu i\u015flem i\u00e7in en iyi sa\u011flay\u0131c\u0131?<\/h3>\n\n\n\n
RAG:<\/strong> daha b\u00fcy\u00fck ba\u011flam + kaliteli g\u00f6mme; d\u00fc\u015f\u00fcn\u00fcn Together\/Fireworks<\/strong>; geri alma i\u00e7in pplx-api ile kar\u0131\u015ft\u0131r\u0131n. Ajanlar:<\/strong> d\u00fc\u015f\u00fck TTFT + g\u00fcvenilir i\u015flev \u00e7a\u011f\u0131rma; Groq\/Fireworks\/Together<\/strong>. Toplu \u0130\u015flem:<\/strong> maliyet avantaj\u0131; Novita\/Hyperbolic\/DeepInfra<\/strong>. \u0130le y\u00f6nlendirin ShareAI<\/strong> h\u0131z\u0131 ve harcamay\u0131 dengelemek i\u00e7in.<\/p>\n\n\n\n
Son D\u00fc\u015f\u00fcnceler<\/h2>\n\n\n\n
Aras\u0131nda se\u00e7im yap\u0131yorsan\u0131z LLM API sa\u011flay\u0131c\u0131lar\u0131 2026<\/strong>, yaln\u0131zca fiyat etiketlerine ve anekdotlara dayanarak se\u00e7im yapmay\u0131n. Ger\u00e7ek istemleriniz ve trafik profilinizle 1 haftal\u0131k bir deneme yap\u0131n. Kullanarak ShareAI<\/strong> sa\u011flay\u0131c\u0131lar aras\u0131nda TTFT, verim, hatalar ve istek ba\u015f\u0131na maliyeti \u00f6l\u00e7\u00fcn\u2014ard\u0131ndan hedeflerinize uygun bir y\u00f6nlendirme politikas\u0131 belirleyin (en d\u00fc\u015f\u00fck maliyet, en d\u00fc\u015f\u00fck gecikme veya ak\u0131ll\u0131 bir kar\u0131\u015f\u0131m). \u0130\u015fler de\u011fi\u015fti\u011finde (ve de\u011fi\u015fecek), yeniden yap\u0131land\u0131rma yapmadan ge\u00e7i\u015f yapmak i\u00e7in zaten g\u00f6zlemlenebilirlik ve esnekli\u011fe sahip olacaks\u0131n\u0131z.<\/p>","protected":false},"excerpt":{"rendered":"
G\u00fcncellendi \u00b7 ~12 dakikal\u0131k okuma LLM API sa\u011flay\u0131c\u0131lar\u0131, \u00fcretim uygulamalar\u0131 i\u00e7in her zamankinden daha \u00f6nemli. G\u00fcvenilir, maliyet etkin ve \u00f6l\u00e7eklenebilir \u00e7\u0131kar\u0131m, sizi d\u00fcr\u00fcst tutan g\u00f6zlemlenebilirlik ve her i\u015f i\u00e7in en iyi modele trafi\u011fi y\u00f6nlendirme \u00f6zg\u00fcrl\u00fc\u011f\u00fcne ihtiyac\u0131n\u0131z var\u2014kilitlenme olmadan. Bu k\u0131lavuz, en iyi 12 LLM API sa\u011flay\u0131c\u0131s\u0131n\u0131 kar\u015f\u0131la\u015ft\u0131r\u0131r ve ShareAI'nin nerede oldu\u011funu g\u00f6sterir [\u2026]<\/p>","protected":false},"author":1,"featured_media":1762,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"cta-title":"Start routing with ShareAI","cta-description":"One OpenAI-compatible API to 150+ models with policy routing, failover, and real-time cost\/latency analytics.","cta-button-text":"Try ShareAI","cta-button-link":"https:\/\/console.shareai.now\/?login=true&type=login&utm_source=shareai.now&utm_medium=content&utm_campaign=llm-api-providers","rank_math_title":"LLM API Providers [sai_current_year]: Top 12 (ShareAI Guide)","rank_math_description":"LLM API providers [sai_current_year] compared on cost, latency, and scale. ShareAI routes across 150+ models with policy routing, observability, and BYOI.","rank_math_focus_keyword":"LLM API providers,top LLM providers,AI inferencing platforms,LLM API comparison","footnotes":""},"categories":[6,38],"tags":[],"class_list":["post-1739","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-insights","category-alternatives"],"_links":{"self":[{"href":"https:\/\/shareai.now\/tr\/api\/wp\/v2\/posts\/1739","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=1739"}],"version-history":[{"count":14,"href":"https:\/\/shareai.now\/tr\/api\/wp\/v2\/posts\/1739\/revisions"}],"predecessor-version":[{"id":1775,"href":"https:\/\/shareai.now\/tr\/api\/wp\/v2\/posts\/1739\/revisions\/1775"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/shareai.now\/tr\/api\/wp\/v2\/media\/1762"}],"wp:attachment":[{"href":"https:\/\/shareai.now\/tr\/api\/wp\/v2\/media?parent=1739"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/shareai.now\/tr\/api\/wp\/v2\/categories?post=1739"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/shareai.now\/tr\/api\/wp\/v2\/tags?post=1739"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}