{"id":38234,"date":"2026-01-26T14:00:34","date_gmt":"2026-01-26T11:00:34","guid":{"rendered":"https:\/\/fatihsoysal.com\/blog\/prompt-rate-limitleri-ve-batch-isleme-llm-apinizin-asiri-yuklenmesini-nasil-engellersiniz\/"},"modified":"2026-01-26T14:00:34","modified_gmt":"2026-01-26T11:00:34","slug":"prompt-rate-limitleri-ve-batch-isleme-llm-apinizin-asiri-yuklenmesini-nasil-engellersiniz","status":"publish","type":"post","link":"https:\/\/fatihsoysal.com\/blog\/prompt-rate-limitleri-ve-batch-isleme-llm-apinizin-asiri-yuklenmesini-nasil-engellersiniz\/","title":{"rendered":"Prompt Rate Limitleri ve Batch \u0130\u015fleme: LLM API’nizin A\u015f\u0131r\u0131 Y\u00fcklenmesini Nas\u0131l Engellersiniz?"},"content":{"rendered":"

Prompt Rate Limitleri ve Batch \u0130\u015fleme: LLM API’nizin A\u015f\u0131r\u0131 Y\u00fcklenmesini Nas\u0131l Engellersiniz?<\/h2>\n

B\u00fcy\u00fck Dil Modelleri (LLM’ler) hayat\u0131m\u0131z\u0131n vazge\u00e7ilmez bir par\u00e7as\u0131 haline geldik\u00e7e, bu g\u00fc\u00e7l\u00fc yapay zeka ara\u00e7lar\u0131na eri\u015fim sa\u011flayan API’lerin verimli ve kesintisiz kullan\u0131m\u0131 kritik \u00f6nem ta\u015f\u0131maktad\u0131r. Ancak, y\u00fcksek talep ve altyap\u0131 s\u0131n\u0131rlamalar\u0131 nedeniyle LLM API’leri genellikle “oran limitleri” ile gelir. Bu limitleri a\u015fmak, uygulaman\u0131z\u0131n performans\u0131n\u0131 d\u00fc\u015f\u00fcrebilir, maliyetleri art\u0131rabilir ve hatta API eri\u015fiminizin ge\u00e7ici olarak ask\u0131ya al\u0131nmas\u0131na neden olabilir. Bu makalede, oran limitlerinin ne oldu\u011funu, neden var olduklar\u0131n\u0131 ve \u00f6zellikle “batch i\u015fleme” (batching) olmak \u00fczere, LLM API’nizin sorunsuz \u00e7al\u0131\u015fmas\u0131n\u0131 sa\u011flamak i\u00e7in kullanabilece\u011finiz stratejileri derinlemesine inceleyece\u011fiz.<\/p>\n

LLM API’lerinde Oran Limitleri Neden Var?<\/h3>\n

LLM API sa\u011flay\u0131c\u0131lar\u0131, hizmetlerinin istikrarl\u0131, g\u00fcvenli ve adil bir \u015fekilde sunulmas\u0131n\u0131 sa\u011flamak i\u00e7in \u00e7e\u015fitli oran limitleri uygular. Bu limitler, hem sa\u011flay\u0131c\u0131 hem de kullan\u0131c\u0131lar i\u00e7in bir\u00e7ok fayda sa\u011flar.<\/p>\n

Altyap\u0131 Koruma<\/h4>\n

API sa\u011flay\u0131c\u0131lar\u0131n\u0131n en temel amac\u0131, altyap\u0131lar\u0131n\u0131 a\u015f\u0131r\u0131 y\u00fcklenmeden korumakt\u0131r. S\u0131n\u0131rs\u0131z istek ak\u0131\u015f\u0131, sunucular\u0131n \u00e7\u00f6kmesine, yan\u0131t s\u00fcrelerinin uzamas\u0131na veya hizmetin tamamen kesintiye u\u011framas\u0131na neden olabilir. Oran limitleri, bu t\u00fcr senaryolar\u0131 \u00f6nleyerek hizmetin s\u00fcreklili\u011fini garanti alt\u0131na al\u0131r.<\/p>\n

Adil Kullan\u0131m<\/h4>\n

Bir\u00e7ok kullan\u0131c\u0131n\u0131n ayn\u0131 anda API’ye eri\u015fmeye \u00e7al\u0131\u015ft\u0131\u011f\u0131 bir ortamda, oran limitleri kaynaklar\u0131n adil da\u011f\u0131t\u0131m\u0131n\u0131 sa\u011flar. Bu sayede, tek bir kullan\u0131c\u0131n\u0131n veya uygulaman\u0131n t\u00fcm kaynaklar\u0131 t\u00fcketerek di\u011fer kullan\u0131c\u0131lar\u0131n deneyimini olumsuz etkilemesi engellenir.<\/p>\n

Maliyet Kontrol\u00fc<\/h4>\n

LLM’leri \u00e7al\u0131\u015ft\u0131rmak y\u00fcksek hesaplama maliyetleri gerektirir. Oran limitleri, sa\u011flay\u0131c\u0131lar\u0131n operasyonel maliyetlerini y\u00f6netmelerine yard\u0131mc\u0131 olurken, kullan\u0131c\u0131lar\u0131n da beklenmedik y\u00fcksek faturalarla kar\u015f\u0131la\u015fmas\u0131n\u0131 \u00f6nler. Genellikle, daha y\u00fcksek limitler daha y\u00fcksek maliyetlerle ili\u015fkilidir.<\/p>\n

Oran Limitleri Nas\u0131l \u00c7al\u0131\u015f\u0131r ve Belirlenir?<\/h3>\n

API sa\u011flay\u0131c\u0131lar\u0131, oran limitlerini farkl\u0131 metrikler \u00fczerinden belirleyebilir. Bu limitler genellikle API dok\u00fcmantasyonunda a\u00e7\u0131k\u00e7a belirtilir ve uygulaman\u0131z\u0131 tasarlarken dikkate alman\u0131z gereken en \u00f6nemli fakt\u00f6rlerden biridir.<\/p>\n

\u0130stek Ba\u015f\u0131na Limitler (RPM\/RPS)<\/h4>\n

En yayg\u0131n limit t\u00fcrlerinden biri, belirli bir zaman diliminde (genellikle dakika veya saniye) yap\u0131labilecek maksimum istek say\u0131s\u0131d\u0131r. \u00d6rne\u011fin, “dakikada 60 istek” (RPM) veya “saniyede 10 istek” (RPS) gibi limitler g\u00f6rebilirsiniz. Bu limitler, API’ye yap\u0131lan \u00e7a\u011fr\u0131lar\u0131n s\u0131kl\u0131\u011f\u0131n\u0131 kontrol eder.<\/p>\n

Token Ba\u015f\u0131na Limitler (TPM\/TPS)<\/h4>\n

LLM API’leri i\u00e7in \u00f6zellikle \u00f6nemli olan bir di\u011fer limit t\u00fcr\u00fc, belirli bir zaman diliminde i\u015flenebilecek maksimum token say\u0131s\u0131d\u0131r. \u00d6rne\u011fin, “dakikada 250.000 token” (TPM) limiti, uygulaman\u0131z\u0131n bir dakikada API’ye g\u00f6nderebilece\u011fi veya API’den alabilece\u011fi toplam token miktar\u0131n\u0131 s\u0131n\u0131rlar. Bu, \u00f6zellikle uzun prompt’lar veya yan\u0131tlar \u00fcreten uygulamalar i\u00e7in kritik bir fakt\u00f6rd\u00fcr.<\/p>\n

E\u015fzamanl\u0131 \u0130stek Limitleri<\/h4>\n

Baz\u0131 API’ler, ayn\u0131 anda aktif olabilecek maksimum istek say\u0131s\u0131n\u0131 da s\u0131n\u0131rlayabilir. Bu, belirli bir anda API’ye ka\u00e7 paralel \u00e7a\u011fr\u0131 yapabilece\u011finizi belirler ve sunucu \u00fczerindeki anl\u0131k y\u00fck\u00fc y\u00f6netmeye yard\u0131mc\u0131 olur.<\/p>\n

Hata Kodlar\u0131 ve Yeniden Denemeler (429 Too Many Requests)<\/h4>\n

Bir oran limitini a\u015ft\u0131\u011f\u0131n\u0131zda, API genellikle “429 Too Many Requests” HTTP durum kodu ile yan\u0131t verir. Bu yan\u0131t, uygulaman\u0131z\u0131n bir s\u00fcre bekleyip iste\u011fi yeniden denemesi gerekti\u011fini belirtir. Bu hata kodunu do\u011fru bir \u015fekilde y\u00f6netmek, uygulaman\u0131z\u0131n dayan\u0131kl\u0131l\u0131\u011f\u0131 i\u00e7in hayati \u00f6neme sahiptir.<\/p>\n

Batch \u0130\u015fleme (Batching) Nedir ve Neden \u00d6nemlidir?<\/h3>\n

Batch i\u015fleme, birden fazla ba\u011f\u0131ms\u0131z iste\u011fi tek bir API \u00e7a\u011fr\u0131s\u0131nda birle\u015ftirme tekni\u011fidir. Bu, \u00f6zellikle oran limitlerini a\u015fma riskini azaltmak ve API kullan\u0131m\u0131n\u0131 optimize etmek i\u00e7in g\u00fc\u00e7l\u00fc bir stratejidir.<\/p>\n

Verimlilik Art\u0131\u015f\u0131<\/h4>\n

Her API \u00e7a\u011fr\u0131s\u0131, a\u011f gecikmesi, ba\u011flant\u0131 kurma ve kimlik do\u011frulama gibi belirli bir ek y\u00fck ile gelir. Batch i\u015fleme, bu ek y\u00fck\u00fc birden fazla iste\u011fe yayarak genel verimlili\u011fi art\u0131r\u0131r ve API ile daha az ileti\u015fim kurman\u0131z\u0131 sa\u011flar.<\/p>\n

Oran Limitlerini A\u015fma<\/h4>\n

Batch i\u015fleme, \u00f6zellikle istek ba\u015f\u0131na limitleri a\u015fma riskini \u00f6nemli \u00f6l\u00e7\u00fcde azalt\u0131r. Tek tek binlerce istek yapmak yerine, bu istekleri daha b\u00fcy\u00fck, ancak daha az say\u0131da batch’lere ay\u0131rarak API’nin belirledi\u011fi RPM\/RPS limitleri dahilinde kalabilirsiniz.<\/p>\n

Maliyet Optimizasyonu<\/h4>\n

Baz\u0131 API’ler, istek ba\u015f\u0131na \u00fccretlendirme yaparken, baz\u0131lar\u0131 token ba\u015f\u0131na \u00fccretlendirme yapar. Batch i\u015fleme, istek say\u0131s\u0131n\u0131 azaltarak, istek ba\u015f\u0131na \u00fccretlendirilen senaryolarda maliyetleri d\u00fc\u015f\u00fcrebilir. Ayr\u0131ca, daha az API \u00e7a\u011fr\u0131s\u0131 yapmak, genel altyap\u0131 ve a\u011f maliyetlerinizi de d\u00fc\u015f\u00fcrebilir.<\/p>\n

Gecikme S\u00fcresini Azaltma<\/h4>\n

\u00c7ok say\u0131da k\u00fc\u00e7\u00fck iste\u011fi s\u0131rayla g\u00f6ndermek yerine, bunlar\u0131 bir batch halinde g\u00f6nderip tek bir yan\u0131t beklemek, toplam i\u015flem s\u00fcresini ve dolay\u0131s\u0131yla gecikme s\u00fcresini azaltabilir. Bu, \u00f6zellikle ger\u00e7ek zamanl\u0131 veya yak\u0131n ger\u00e7ek zamanl\u0131 uygulamalar i\u00e7in \u00f6nemlidir.<\/p>\n

Batch \u0130\u015fleme Stratejileri ve Uygulamalar\u0131<\/h3>\n

Batch i\u015flemeyi uygularken, uygulaman\u0131z\u0131n ihtiya\u00e7lar\u0131na ve API’nin \u00f6zelliklerine uygun bir strateji se\u00e7mek \u00f6nemlidir.<\/p>\n

Sabit Boyutlu Batchler<\/h4>\n

En basit batch i\u015fleme stratejisi, her batch’e sabit say\u0131da \u00f6\u011fe koymakt\u0131r. \u00d6rne\u011fin, her batch’te 10 prompt g\u00f6ndermek gibi. Bu y\u00f6ntem kolay uygulanabilir olsa da, prompt’lar\u0131n token boyutlar\u0131 farkl\u0131l\u0131k g\u00f6steriyorsa token limitlerini a\u015fma riski ta\u015f\u0131yabilir.<\/p>\n

Dinamik Boyutlu Batchler (Token Say\u0131s\u0131na G\u00f6re)<\/h4>\n

LLM API’leri i\u00e7in en etkili batch i\u015fleme stratejilerinden biri, batch boyutunu prompt’lar\u0131n toplam token say\u0131s\u0131na g\u00f6re dinamik olarak belirlemektir. \u00d6rne\u011fin, API’nin dakikada 250.000 token limiti varsa, bir batch’e eklenen prompt’lar\u0131n toplam token say\u0131s\u0131 belirli bir e\u015fi\u011fi (\u00f6rne\u011fin 20.000 token) ge\u00e7meyecek \u015fekilde ayarlanabilir. Bu, hem istek limitlerini hem de token limitlerini ayn\u0131 anda y\u00f6netmenizi sa\u011flar.<\/p>\n

Asenkron Batch \u0130\u015fleme<\/h4>\n

Birden fazla batch’i paralel olarak i\u015flemek, genel i\u015flem s\u00fcresini daha da h\u0131zland\u0131rabilir. Python’daki asyncio<\/code> veya ThreadPoolExecutor<\/code> gibi yap\u0131larla asenkron API \u00e7a\u011fr\u0131lar\u0131 yaparak ayn\u0131 anda birden fazla batch’i g\u00f6nderebilirsiniz. Ancak, bu durumda e\u015fzamanl\u0131 istek limitlerine dikkat etmek \u00f6nemlidir.<\/p>\n

\u00d6rnek Python Kodu (Basit Batching)<\/h4>\n

A\u015fa\u011f\u0131daki basit Python kodu, prompt’lar\u0131 nas\u0131l batch’leyebilece\u011finizi ve bir LLM API’sine nas\u0131l g\u00f6nderebilece\u011finizi g\u00f6sterir. Bu \u00f6rnek, OpenAI API’sinin completions<\/code> veya chat completions<\/code> endpoint’ine uygun bir yap\u0131y\u0131 temsil eder.<\/p>\n

\nimport openai\nimport time\n\n# API anahtar\u0131n\u0131z\u0131 buraya ekleyin\n# openai.api_key = \"YOUR_API_KEY\" \n\ndef send_batch_to_llm_api(prompts_batch):\n    \"\"\"\n    Bir prompt batch'ini LLM API'sine g\u00f6nderir ve yan\u0131tlar\u0131 d\u00f6nd\u00fcr\u00fcr.\n    Bu k\u0131s\u0131m, kulland\u0131\u011f\u0131n\u0131z API'ye g\u00f6re \u00f6zelle\u015ftirilmelidir.\n    \"\"\"\n    print(f\"G\u00f6nderilen batch boyutu: {len(prompts_batch)}\")\n    try:\n        # \u00d6rnek: OpenAI ChatCompletion API kullan\u0131m\u0131\n        messages_batch = [[{\"role\": \"user\", \"content\": prompt}] for prompt in prompts_batch]\n        \n        # Bu k\u0131s\u0131m, API'nin batch endpoint'i varsa daha verimli olabilir.\n        # Aksi takdirde, her prompt i\u00e7in ayr\u0131 bir \u00e7a\u011fr\u0131 yapman\u0131z gerekebilir,\n        # ancak bu durumda batching'in faydas\u0131 azal\u0131r.\n        # Ger\u00e7ek bir batch endpoint'i olmayan API'ler i\u00e7in, \n        # bu fonksiyon i\u00e7inde her prompt'u s\u0131rayla veya asenkron olarak \u00e7a\u011f\u0131r\u0131rs\u0131n\u0131z\n        # ve oran limitlerini y\u00f6netmek i\u00e7in gecikmeler eklersiniz.\n\n        # Basit bir sim\u00fclasyon:\n        responses = []\n        for msg in messages_batch:\n            # Ger\u00e7ek API \u00e7a\u011fr\u0131s\u0131 burada olurdu:\n            # response = openai.chat.completions.create(\n            #     model=\"gpt-3.5-turbo\",\n            #     messages=msg\n            # )\n            # responses.append(response.choices[0].message.content)\n            responses.append(f\"Sim\u00fcle edilmi\u015f yan\u0131t: {msg[0]['content'][:20]}...\")\n            time.sleep(0.1) # Oran limiti sim\u00fclasyonu\n        \n        return responses\n    except Exception as e:\n        print(f\"API hatas\u0131: {e}\")\n        return [None] * len(prompts_batch)\n\ndef process_prompts_with_batching(all_prompts, batch_size=10, delay_between_batches=1):\n    \"\"\"\n    Prompt listesini batch'ler halinde i\u015fler ve API'ye g\u00f6nderir.\n    \"\"\"\n    results = []\n    for i in range(0, len(all_prompts), batch_size):\n        batch = all_prompts[i:i + batch_size]\n        print(f\"Batch {int(i\/batch_size) + 1} i\u015fleniyor...\")\n        batch_results = send_batch_to_llm_api(batch)\n        results.extend(batch_results)\n        \n        if i + batch_size < len(all_prompts):\n            print(f\"Sonraki batch i\u00e7in {delay_between_batches} saniye bekleniyor...\")\n            time.sleep(delay_between_batches) # Oran limitlerini a\u015fmamak i\u00e7in bekleme\n    return results\n\n# \u00d6rnek kullan\u0131m\nif __name__ == \"__main__\":\n    sample_prompts = [f\"Bana {i}. konu hakk\u0131nda k\u0131sa bir hikaye anlat.\" for i in range(50)]\n    \n    print(\"Batch i\u015fleme ile prompt'lar i\u015fleniyor...\")\n    processed_responses = process_prompts_with_batching(sample_prompts, batch_size=5, delay_between_batches=2)\n    \n    print(\"\\n\u0130\u015flem tamamland\u0131. \u0130lk 5 yan\u0131t:\")\n    for j, res in enumerate(processed_responses[:5]):\n        print(f\"Prompt {j+1} yan\u0131t\u0131: {res}\")\n\n<\/pre>\n
<\/code><\/p>\n
Geli\u015fmi\u015f Oran Limiti Y\u00f6netimi Teknikleri<\/h3>\n
Batch i\u015fleme tek ba\u015f\u0131na yeterli olmayabilir. Oran limitlerini daha etkin y\u00f6netmek i\u00e7in ek teknikler kullanmak \u00f6nemlidir.<\/p>\n
\u00dcstel Geri \u00c7ekilme (Exponential Backoff)<\/h4>\n
API'den 429 hatas\u0131 ald\u0131\u011f\u0131n\u0131zda, iste\u011fi hemen yeniden denemek yerine, artan s\u00fcrelerle bekleyerek yeniden denemek en iyi uygulamad\u0131r. \u00dcstel geri \u00e7ekilme, her ba\u015far\u0131s\u0131z denemeden sonra bekleme s\u00fcresini katlayarak (\u00f6rne\u011fin, 1 saniye, sonra 2 saniye, sonra 4 saniye vb.) API sunucular\u0131na gereksiz y\u00fck bindirmeyi \u00f6nler ve ba\u015far\u0131l\u0131 bir yeniden deneme \u015fans\u0131n\u0131 art\u0131r\u0131r.<\/p>\n
Token Kovas\u0131 (Token Bucket) Algoritmas\u0131<\/h4>\n
Bu algoritma, oran limitlerini programatik olarak y\u00f6netmek i\u00e7in kullan\u0131lan pop\u00fcler bir y\u00f6ntemdir. Belirli bir oranda (\u00f6rne\u011fin, saniyede X token) dolan bir \"kovay\u0131\" sim\u00fcle eder. Her istek g\u00f6nderildi\u011finde kovadan token al\u0131n\u0131r. Kovada yeterli token yoksa, istek geciktirilir veya reddedilir. Bu, uygulaman\u0131z\u0131n API limitlerini a\u015fmamas\u0131n\u0131 sa\u011flamak i\u00e7in proaktif bir yakla\u015f\u0131md\u0131r.<\/p>\n
Kuyruk Sistemleri (Queueing Systems)<\/h4>\n
Y\u00fcksek hacimli veya kritik uygulamalar i\u00e7in, t\u00fcm API isteklerini bir kuyruk sistemine (\u00f6rne\u011fin, RabbitMQ, Apache Kafka, AWS SQS) yerle\u015ftirmek iyi bir \u00e7\u00f6z\u00fcmd\u00fcr. Ayr\u0131 bir i\u015fleyici (worker) servisi, bu kuyruktan istekleri al\u0131r, oran limitlerine uygun bir \u015fekilde batch'ler ve API'ye g\u00f6nderir. Bu, sistemin dayan\u0131kl\u0131l\u0131\u011f\u0131n\u0131 art\u0131r\u0131r ve y\u00fck dalgalanmalar\u0131n\u0131 daha iyi y\u00f6netir.<\/p>\n
Paralel \u0130\u015fleme ve \u0130\u015f Par\u00e7ac\u0131klar\u0131<\/h4>\n
Python'daki concurrent.futures<\/code> mod\u00fcl\u00fc gibi ara\u00e7larla, birden fazla i\u015f par\u00e7ac\u0131\u011f\u0131 (thread) veya i\u015flem (process) kullanarak ayn\u0131 anda birden fazla batch'i veya iste\u011fi i\u015fleyebilirsiniz. Ancak, bu y\u00f6ntemleri kullan\u0131rken API'nin e\u015fzamanl\u0131 istek limitlerini a\u015fmamaya \u00f6zen g\u00f6stermelisiniz.<\/p>\n
Pratik \u0130pu\u00e7lar\u0131 ve En \u0130yi Uygulamalar<\/h3>\n
LLM API'nizi verimli ve sorunsuz bir \u015fekilde kullanmak i\u00e7in baz\u0131 ek ipu\u00e7lar\u0131 ve en iyi uygulamalar.<\/p>\n
API Belgelerini \u0130nceleyin<\/h4>\n
Her API'nin oran limitleri, hata kodlar\u0131 ve \u00f6nerilen kullan\u0131m desenleri farkl\u0131d\u0131r. Her zaman kulland\u0131\u011f\u0131n\u0131z API'nin g\u00fcncel belgelerini dikkatlice okuyun ve bu bilgilere g\u00f6re uygulaman\u0131z\u0131 tasarlay\u0131n.<\/p>\n
\u0130zleme ve G\u00fcnl\u00fckleme<\/h4>\n
Uygulaman\u0131z\u0131n API kullan\u0131m\u0131n\u0131 (istek say\u0131s\u0131, token say\u0131s\u0131, hata oranlar\u0131) s\u00fcrekli olarak izleyin ve g\u00fcnl\u00fckleyin. Bu veriler, oran limitlerine ne kadar yakla\u015ft\u0131\u011f\u0131n\u0131z\u0131 anlaman\u0131za ve potansiyel sorunlar\u0131 \u00f6nceden tespit etmenize yard\u0131mc\u0131 olur.<\/p>\n
Hata Y\u00f6netimi<\/h4>\n
API'den gelen hata yan\u0131tlar\u0131n\u0131 (\u00f6zellikle 429) do\u011fru bir \u015fekilde ele al\u0131n. Uygulaman\u0131z\u0131n bu hatalar\u0131 yakalay\u0131p uygun geri \u00e7ekilme stratejileriyle yeniden denemeler yapmas\u0131n\u0131 sa\u011flay\u0131n. Kullan\u0131c\u0131ya net geri bildirimler sunmay\u0131 unutmay\u0131n.<\/p>\n
Maliyet ve Performans Dengesi<\/h4>\n
Batch i\u015fleme ve oran limiti y\u00f6netimi stratejileri, genellikle maliyet ve performans aras\u0131nda bir denge kurmay\u0131 gerektirir. \u00c7ok b\u00fcy\u00fck batch'ler gecikmeyi art\u0131rabilirken, \u00e7ok k\u00fc\u00e7\u00fck batch'ler oran limitlerini a\u015fman\u0131za neden olabilir. Uygulaman\u0131z\u0131n \u00f6zel ihtiya\u00e7lar\u0131na g\u00f6re bu dengeyi optimize edin.<\/p>\n
Sonu\u00e7<\/h3>\n
LLM API'lerinin sundu\u011fu inan\u0131lmaz potansiyelden tam olarak yararlanmak i\u00e7in oran limitlerini anlamak ve etkin bir \u015fekilde y\u00f6netmek hayati \u00f6neme sahiptir. Batch i\u015fleme, API kullan\u0131m\u0131n\u0131z\u0131 optimize etmenin, oran limitlerini a\u015fma riskini azaltman\u0131n ve uygulaman\u0131z\u0131n genel performans\u0131n\u0131 art\u0131rman\u0131n en g\u00fc\u00e7l\u00fc yollar\u0131ndan biridir. \u00dcstel geri \u00e7ekilme, kuyruk sistemleri ve dinamik batch boyutland\u0131rma gibi geli\u015fmi\u015f tekniklerle birle\u015ftirildi\u011finde, LLM API'nizin a\u015f\u0131r\u0131 y\u00fcklenmesini engelleyebilir ve uygulaman\u0131z\u0131n kesintisiz ve verimli bir \u015fekilde \u00e7al\u0131\u015fmas\u0131n\u0131 sa\u011flayabilirsiniz. Unutmay\u0131n, iyi bir API y\u00f6netimi, hem sizin hem de API sa\u011flay\u0131c\u0131s\u0131 i\u00e7in daha s\u00fcrd\u00fcr\u00fclebilir bir ekosistem yarat\u0131r.<\/p>\n
SSS (S\u0131k Sorulan Sorular)<\/h3>\n
Oran limiti nedir?<\/h4>\n
Oran limiti, bir API'ye belirli bir zaman diliminde (\u00f6rne\u011fin dakika veya saniye) yap\u0131labilecek istek veya i\u015flenebilecek veri (token) miktar\u0131n\u0131 s\u0131n\u0131rlayan bir kurald\u0131r. Amac\u0131, API altyap\u0131s\u0131n\u0131 korumak ve kaynaklar\u0131n adil kullan\u0131m\u0131n\u0131 sa\u011flamakt\u0131r.<\/p>\n
Batch i\u015fleme ne i\u015fe yarar?<\/h4>\n
Batch i\u015fleme (batching), birden fazla ba\u011f\u0131ms\u0131z iste\u011fi tek bir API \u00e7a\u011fr\u0131s\u0131nda birle\u015ftirme tekni\u011fidir. Oran limitlerini a\u015fma riskini azalt\u0131r, API kullan\u0131m verimlili\u011fini art\u0131r\u0131r, a\u011f gecikmesini d\u00fc\u015f\u00fcr\u00fcr ve baz\u0131 durumlarda maliyetleri optimize eder.<\/p>\n
Hangi durumlarda batch i\u015fleme kullanmal\u0131y\u0131m?<\/h4>\n
\u00c7ok say\u0131da k\u00fc\u00e7\u00fck ve ba\u011f\u0131ms\u0131z API iste\u011fi g\u00f6ndermeniz gerekti\u011finde (\u00f6rne\u011fin, bir liste metni \u00f6zetlemek veya birden fazla kullan\u0131c\u0131n\u0131n prompt'lar\u0131n\u0131 i\u015flemek gibi) batch i\u015fleme kullanmal\u0131s\u0131n\u0131z. \u00d6zellikle API'nin istek ba\u015f\u0131na limitleri d\u00fc\u015f\u00fckse veya y\u00fcksek token hacmiyle \u00e7al\u0131\u015f\u0131yorsan\u0131z \u00e7ok faydal\u0131d\u0131r.<\/p>\n
\u00dcstel geri \u00e7ekilme neden \u00f6nemlidir?<\/h4>\n
\u00dcstel geri \u00e7ekilme (exponential backoff), API'den bir hata (\u00f6rne\u011fin 429 Too Many Requests) al\u0131nd\u0131\u011f\u0131nda, iste\u011fi artan s\u00fcrelerle bekleyerek yeniden deneme stratejisidir. Bu, API sunucular\u0131na gereksiz y\u00fck bindirmeyi \u00f6nler ve uygulaman\u0131z\u0131n ba\u015far\u0131l\u0131 bir \u015fekilde yeniden ba\u011flanma \u015fans\u0131n\u0131 art\u0131r\u0131r.<\/p>\n
LLM API'm a\u015f\u0131r\u0131 y\u00fcklenirse ne yapmal\u0131y\u0131m?<\/h4>\n
\u00d6ncelikle API'den gelen hata kodlar\u0131n\u0131 (\u00f6zellikle 429) yakalay\u0131n. Ard\u0131ndan, uygulaman\u0131zda \u00fcstel geri \u00e7ekilme ile yeniden deneme mekanizmalar\u0131 uygulay\u0131n. Uzun vadede, batch i\u015fleme stratejilerini benimseyin, dinamik batch boyutland\u0131rma kullan\u0131n ve gerekirse bir kuyruk sistemi entegre ederek API kullan\u0131m\u0131n\u0131z\u0131 daha verimli hale getirin.<\/p>\n","protected":false},"excerpt":{"rendered":"Prompt Rate Limitleri ve Batch \u0130\u015fleme: LLM API’nizin A\u015f\u0131r\u0131 Y\u00fcklenmesini Nas\u0131l Engellersiniz?\nB\u00fcy\u00fck Dil Modelleri (LLM’ler) hayat\u0131m\u0131z\u0131n vazge\u00e7il…","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-38234","1":"post","2":"type-post","3":"status-publish","4":"format-standard","6":"category-genel","7":"cs-entry","8":"cs-video-wrap"},"yoast_head":"\nPrompt Rate Limitleri ve Batch \u0130\u015fleme: LLM API'nizin A\u015f\u0131r\u0131 Y\u00fcklenmesini Nas\u0131l Engellersiniz? - Kodlar\u0131n Gizemli D\u00fcnyas\u0131<\/title>\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\/prompt-rate-limitleri-ve-batch-isleme-llm-apinizin-asiri-yuklenmesini-nasil-engellersiniz\/\" \/>\n<meta property=\"og:locale\" content=\"tr_TR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Prompt Rate Limitleri ve Batch \u0130\u015fleme: LLM API'nizin A\u015f\u0131r\u0131 Y\u00fcklenmesini Nas\u0131l Engellersiniz?\" \/>\n<meta property=\"og:description\" content=\"Prompt Rate Limitleri ve Batch \u0130\u015fleme: LLM API'nizin A\u015f\u0131r\u0131 Y\u00fcklenmesini Nas\u0131l Engellersiniz? 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