Idan \u0199ungiyarku tana aika fasalullukan AI zuwa samarwa, kulle mai siyar da LLM yawanci yana bayyana kafin siyan kayan aiki ya lura da shi. Wannan jagorar tana nufin masu ha\u0253akawa da \u0199ungiyoyin samfur wa\u0257anda ke bu\u0199atar motsi, mafi kyawun za\u0253u\u0253\u0253ukan madadin, da \u0199arancin mamaki lokacin da samfurin ya canza a \u0199ar\u0199ashin aikace-aikacen kai tsaye.<\/p>\n\n\n\n
Hatsarin ba na ka'idar kawai ba ne yanzu. Binciken Masu Ha\u0253aka na Stack Overflow na 2025<\/a> ya ba da rahoton cewa 84% na masu amsa suna amfani ko suna shirin amfani da kayan aikin AI a cikin tsarin ha\u0253akarsu, yayin da masu ha\u0253aka da yawa suka fi rashin yarda da daidaiton fitarwar AI fiye da yarda da shi. A lokaci guda, duka Anthropic<\/a> kuma OpenAI<\/a> suna fitar da jadawalin dakatarwa don samfura da hanyoyin ha\u0257i. Wannan tunatarwa ce cewa samun damar samfurin yana da ala\u0199a da aiki, ba wani abu mai \u0257orewa ba.<\/p>\n\n\n\n Kulle ba ya farawa da kwangila sau da yawa. Yana farawa a cikin lambar. Wata \u0199ungiya tana hardcode-ing siffar amsa ta musamman ga mai siyarwa, tana daidaita tambayoyi a kusa da wasu halayen samfurin, ko tana \u0257auka cewa wani bayanin jinkiri zai kasance mai tsayayye. Sannan sigar samfurin ta canza, yawan aiki ya ragu, ko tsarin fitarwa ya canza kadan don karya nazarin bayanai na \u0199asa da duba inganci.<\/p>\n\n\n\n Da zarar hakan ya faru, \u0199aura ba ta zama shawarar hanya ba. Yana zama sake rubutawa. Farashin yana bayyana a matsayin gyaran gaggawa, gwaje-gwaje masu rauni, jinkirin sakin kayayyaki, da rage amincewa da kowane fasalin AI da aka gina a saman wannan ala\u0199ar.<\/p>\n\n\n\n Kada ku \u0257auki canje-canjen samfurin a matsayin abubuwan da ba a gani na kayan aiki. Ku \u0257auke su kamar sakin aikace-aikace. Kulle zuwa sigar samfurin da aka bayyana lokacin da mai siyarwa ya goyi bayan shi, ayyana mai kula da ha\u0253akawa, kuma ku yi amfani da jerin abubuwan duba gajere kafin zirga-zirga ta motsa zuwa sabon sigar.<\/p>\n\n\n\n Wannan jerin abubuwan duba ya kamata ya ha\u0257a da tsarin fitarwa, jinkiri, farashi, da ingancin aiki akan tambayoyin da suka fi muhimmanci ga samfurinku. Idan mai siyarwa ya sanar da dakatarwa, kuna son hanyar \u0199aura mai sarrafawa maimakon rikice-rikice na dole.<\/p>\n\n\n\n Idan aikace-aikacenku yana sarrafa amsoshin salon OpenAI ta wata hanya da kuma amsoshin salon Anthropic ta wata hanya, iyakar mai siyarwa tuni tana zubowa cikin sauran tsarin ku. Gina wani siririn layin daidaitawa wanda ke taswira amsoshin samfurin zuwa tsarin ciki guda \u0257aya don rubutu, kiran kayan aiki, ma'aunin amfani, da kurakurai.<\/p>\n\n\n\n Manufar tana da sau\u0199i: canza masu siyarwa bai kamata ya bu\u0199aci gyare-gyare masu yawa a cikin dabarun kasuwanci, nazari, da fassarar gaba ba. Ya kamata ya zama mafi yawa aikin hanya da dacewa.<\/p>\n\n\n\n Wani tsari mai sassauci yana tsara hanyoyi ta hanyar manufofi. Wannan yana nufin za\u0253ar samfur ko mai bayarwa bisa ga aikin da ke hannun, kamar juriya ga jinkiri, kasafin ku\u0257i, yanki, samuwa, ko dokokin madadin. Hardcoding \u0257aya mai bayarwa don kowane bu\u0199ata yana sa matsaloli da canje-canjen farashi su zama masu wahala fiye da yadda ya kamata su kasance.<\/p>\n\n\n\n Wannan shine inda kasuwar AI da API layer za su iya taimakawa. Tare da Samfura na ShareAI<\/a>, \u0199ungiyoyi za su iya kwatanta hanyoyi a tsakanin samfurori da yawa. Tare da Takardun ShareAI<\/a> kuma Bayanin API<\/a>, za ku iya kiyaye ha\u0257in guda \u0257aya yayin da kuke da damar canza dabarar samfurin a bayansa.<\/p>\n\n\n\n Yawancin \u0199ungiyoyi suna da gwaje-gwaje, amma suna gudana ne kawai a matakin gwaji ko akan saitin ma'auni mai iyaka. Wannan yana da amfani, amma ba cikakke ba. Hadarin kullewa yana bayyana lokacin da kuka gwada akan ainihin siffofin tambaya, girman kaya na gaske, da ainihin lokutan gazawa daga zirga-zirgar samarwa.<\/p>\n\n\n\n Yi amfani da ma'auni mai tsauri don mahimman hanyoyin aiki. Sake gudanar da wa\u0257annan binciken duk lokacin da kuka canza nau'ikan samfurori, manufofin tsara hanyoyi, ko samfuran tambaya. Idan ba za ku iya auna karkacewa ba, ba za ku iya sarrafa shi ba.<\/p>\n\n\n\n \u0198ungiyoyi suna shiga cikin matsala lokacin da suka mai da hankali kawai kan ingancin fitarwa kuma suka yi watsi da siginar aiki. Sau\u0199in \u0257aukar samfurin yana sau\u0199a\u0199a lokacin da za ku iya ganin musayar a fili: wa\u0257anne hanyoyi sun fi arha, wa\u0257anne sun fi jinkiri, wa\u0257anne sun fi yawan gazawa, da wa\u0257anne ya kamata a yi amfani da su kawai a matsayin madadin.<\/p>\n\n\n\n Wannan bayyanar tana taimaka muku yanke shawarar tsara hanyoyi tun da wuri maimakon lokacin da wani lamari ya faru. Hakanan yana ba \u0199ungiyoyin injiniya da samfuri wata hanya ta ha\u0257in gwiwa don tattauna lokacin da hanyar da ta fi tsada ta dace da lokacin da madadin mai rahusa ya isa.<\/p>\n\n\n\n ShareAI ya dace da \u0199ungiyoyin da suke son API guda \u0257aya don samfurori da yawa ba tare da ha\u0257a aikace-aikacen su da mai siyarwa guda \u0257aya ba. Kuna iya amfani da shi don kwatanta hanyoyi, kiyaye za\u0253in mai bayarwa mai sassauci, da gina madadin cikin tsarin gine-gine tun da wuri maimakon gyara shi bayan matsalar samarwa.<\/p>\n\n\n\n Idan tsarin ku na yanzu ya riga ya kasance mai matu\u0199ar ha\u0257uwa, burin ba shine sake rubutawa gaba \u0257aya ba. Fara da matsar da sabbin ayyuka a bayan tsari mai tsabta, tsakiya yanke shawarar tsara hanyoyi, kuma gwada hanya \u0257aya ta madadin daga farko zuwa \u0199arshe. Daga nan, kowace zato ta musamman ga mai bayarwa da kuka cire tana sau\u0199a\u0199a \u0199aura ta gaba.<\/p>\n\n\n\n Idan kuna son rage kullewar mai siyarwa na LLM ba tare da sake gina aikace-aikacen ku a kowane sakin samfur ba, fara da hanya \u0257aya mai \u0257aukar nauyi. Duba takardu<\/a>, kwatanta hanyoyi a cikin Filin wasa<\/a>, kuma za\u0253i dabarar samfurin da za ka iya canzawa daga baya.<\/p>","protected":false},"excerpt":{"rendered":" Kulle mai siyarwa na LLM yana bayyana a cikin karkacewa, katsewa, da ha\u0257in gwiwa masu rauni. Ga hanyoyi guda biyar masu amfani don kiyaye tsarin AI \u0257inku mai \u0257aukar nauyi da juriya.<\/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=llm-vendor-lock-in-flexible-ai-stack","rank_math_title":"LLM Vendor Lock-In: 5 Ways to Build a Flexible AI Stack","rank_math_description":"LLM vendor lock-in can raise migration risk and break workflows. Learn five practical ways to build a flexible AI stack with routing and failover.","rank_math_focus_keyword":"LLM vendor lock-in","footnotes":""},"categories":[6,4],"tags":[42,76,74,75],"class_list":["post-2890","post","type-post","status-publish","format-standard","hentry","category-insights","category-developers","tag-ai-api-routing","tag-ai-failover","tag-llm-vendor-lock-in","tag-model-agnostic-ai-architecture"],"_links":{"self":[{"href":"https:\/\/shareai.now\/ha\/api\/wp\/v2\/posts\/2890","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/shareai.now\/ha\/api\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/shareai.now\/ha\/api\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/shareai.now\/ha\/api\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/shareai.now\/ha\/api\/wp\/v2\/comments?post=2890"}],"version-history":[{"count":1,"href":"https:\/\/shareai.now\/ha\/api\/wp\/v2\/posts\/2890\/revisions"}],"predecessor-version":[{"id":2892,"href":"https:\/\/shareai.now\/ha\/api\/wp\/v2\/posts\/2890\/revisions\/2892"}],"wp:attachment":[{"href":"https:\/\/shareai.now\/ha\/api\/wp\/v2\/media?parent=2890"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/shareai.now\/ha\/api\/wp\/v2\/categories?post=2890"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/shareai.now\/ha\/api\/wp\/v2\/tags?post=2890"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}Me yasa kulle mai siyar da LLM ke zama mai tsada cikin sauri<\/h2>\n\n\n\n
1. Kulle sigar samfurin kuma ku \u0257auki ha\u0253akawa kamar sakin kayayyaki<\/h2>\n\n\n\n
2. Daidaita amsoshi a bayan tsarin ciki guda \u0257aya<\/h2>\n\n\n\n
3. Shirya zirga-zirga ta hanyar manufofi maimakon masu siyarwa da aka hardcode.<\/h2>\n\n\n\n
4. Gudanar da gwaje-gwaje akan tsarin samarwa na gaske<\/h2>\n\n\n\n
5. Kiyaye farashi, jinkiri, da samuwa a bayyane<\/h2>\n\n\n\n
Inda ShareAI ya dace.<\/h2>\n\n\n\n
Mataki na gaba<\/h2>\n\n\n\n