firesidetech/byok-pricing-guide.html at main · JasperNoBoxDev/firesidetech · GitHub

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items-center space-x-2.5"><div>Create Your First Chatbot</div></div></a></li><li id="/lead-collection" class="scroll-m-4 first:scroll-m-20"><a class="group mt-2 lg:mt-0 flex items-center pr-3 py-1.5 cursor-pointer focus:outline-primary dark:focus:outline-primary-light space-x-3 rounded-xl hover:bg-gray-600/5 dark:hover:bg-gray-200/5 text-gray-700 hover:text-gray-900 dark:text-gray-400 dark:hover:text-gray-300" style="padding-left:1rem" href="lead-collection.html"><div class="flex-1 flex items-center space-x-2.5"><div>Lead Collection</div></div></a></li><li id="/human-support-escalation" class="scroll-m-4 first:scroll-m-20"><a class="group mt-2 lg:mt-0 flex items-center pr-3 py-1.5 cursor-pointer focus:outline-primary dark:focus:outline-primary-light space-x-3 rounded-xl hover:bg-gray-600/5 dark:hover:bg-gray-200/5 text-gray-700 hover:text-gray-900 dark:text-gray-400 dark:hover:text-gray-300" style="padding-left:1rem" href="human-support-escalation.html"><div class="flex-1 flex items-center space-x-2.5"><div>Human Support Escalation</div></div></a></li><li id="/inbox-notifications" class="scroll-m-4 first:scroll-m-20"><a class="group mt-2 lg:mt-0 flex items-center pr-3 py-1.5 cursor-pointer focus:outline-primary dark:focus:outline-primary-light space-x-3 rounded-xl hover:bg-gray-600/5 dark:hover:bg-gray-200/5 text-gray-700 hover:text-gray-900 dark:text-gray-400 dark:hover:text-gray-300" style="padding-left:1rem" href="inbox-notifications.html"><div class="flex-1 flex items-center space-x-2.5"><div>Inbox Notifications</div></div></a></li><li id="/conversation-labeling" class="scroll-m-4 first:scroll-m-20"><a class="group mt-2 lg:mt-0 flex items-center pr-3 py-1.5 cursor-pointer focus:outline-primary dark:focus:outline-primary-light space-x-3 rounded-xl hover:bg-gray-600/5 dark:hover:bg-gray-200/5 text-gray-700 hover:text-gray-900 dark:text-gray-400 dark:hover:text-gray-300" style="padding-left:1rem" href="conversation-labeling.html"><div class="flex-1 flex items-center space-x-2.5"><div>Conversation Labeling</div></div></a></li><li id="/multi-agents-chatbot" class="scroll-m-4 first:scroll-m-20"><a class="group mt-2 lg:mt-0 flex items-center pr-3 py-1.5 cursor-pointer focus:outline-primary dark:focus:outline-primary-light space-x-3 rounded-xl hover:bg-gray-600/5 dark:hover:bg-gray-200/5 text-gray-700 hover:text-gray-900 dark:text-gray-400 dark:hover:text-gray-300" style="padding-left:1rem" href="multi-agents-chatbot.html"><div class="flex-1 flex items-center space-x-2.5"><div>Multi-Agents Chatbot</div></div></a></li><li id="/fine-tuning-agent-intents" class="scroll-m-4 first:scroll-m-20"><a class="group mt-2 lg:mt-0 flex items-center pr-3 py-1.5 cursor-pointer focus:outline-primary dark:focus:outline-primary-light space-x-3 rounded-xl hover:bg-gray-600/5 dark:hover:bg-gray-200/5 text-gray-700 hover:text-gray-900 dark:text-gray-400 dark:hover:text-gray-300" style="padding-left:1rem" href="fine-tuning-agent-intents.html"><div class="flex-1 flex items-center space-x-2.5"><div>Fine Tuning Agent Intents</div></div></a></li><li id="/supervisor-overrides" class="scroll-m-4 first:scroll-m-20"><a class="group mt-2 lg:mt-0 flex items-center pr-3 py-1.5 cursor-pointer focus:outline-primary dark:focus:outline-primary-light space-x-3 rounded-xl hover:bg-gray-600/5 dark:hover:bg-gray-200/5 text-gray-700 hover:text-gray-900 dark:text-gray-400 dark:hover:text-gray-300" style="padding-left:1rem" href="supervisor-overrides.html"><div class="flex-1 flex items-center space-x-2.5"><div>AI Supervisor Overrides</div></div></a></li><li id="/byok-pricing-guide" class="scroll-m-4 first:scroll-m-20"><a class="group mt-2 lg:mt-0 flex items-center pr-3 py-1.5 cursor-pointer focus:outline-primary dark:focus:outline-primary-light space-x-3 rounded-xl bg-primary/10 text-primary font-semibold dark:text-primary-light dark:bg-primary-light/10" style="padding-left:1rem" href="byok-pricing-guide.html"><div class="flex-1 flex items-center space-x-2.5"><div>Bring Your Own Key (BYOK) and Pricing</div></div></a></li><li id="/working-with-tables" class="scroll-m-4 first:scroll-m-20"><a class="group mt-2 lg:mt-0 flex items-center pr-3 py-1.5 cursor-pointer focus:outline-primary dark:focus:outline-primary-light space-x-3 rounded-xl hover:bg-gray-600/5 dark:hover:bg-gray-200/5 text-gray-700 hover:text-gray-900 dark:text-gray-400 dark:hover:text-gray-300" style="padding-left:1rem" href="working-with-tables.html"><div class="flex-1 flex items-center space-x-2.5"><div>Working with Tables and CSV</div></div></a></li><li id="/best-practices" class="scroll-m-4 first:scroll-m-20"><a class="group mt-2 lg:mt-0 flex items-center pr-3 py-1.5 cursor-pointer focus:outline-primary dark:focus:outline-primary-light space-x-3 rounded-xl hover:bg-gray-600/5 dark:hover:bg-gray-200/5 text-gray-700 hover:text-gray-900 dark:text-gray-400 dark:hover:text-gray-300" style="padding-left:1rem" href="best-practices.html"><div class="flex-1 flex items-center space-x-2.5"><div>Best practices for preparing training data</div></div></a></li><li id="/help" class="scroll-m-4 first:scroll-m-20"><a class="group mt-2 lg:mt-0 flex items-center pr-3 py-1.5 cursor-pointer focus:outline-primary dark:focus:outline-primary-light space-x-3 rounded-xl hover:bg-gray-600/5 dark:hover:bg-gray-200/5 text-gray-700 hover:text-gray-900 dark:text-gray-400 dark:hover:text-gray-300" style="padding-left:1rem" href="help.html"><div class="flex-1 flex items-center space-x-2.5"><div>Why does my chatbot not answer correctly?</div></div></a></li></ul></div><div class="mt-12 lg:mt-8"><h5 class="pl-4 mb-3.5 lg:mb-2.5 font-semibold text-gray-900 dark:text-gray-200">Function Calling</h5><ul><li id="/rag-from-external-data-provider" class="scroll-m-4 first:scroll-m-20"><a class="group mt-2 lg:mt-0 flex items-center pr-3 py-1.5 cursor-pointer focus:outline-primary dark:focus:outline-primary-light space-x-3 rounded-xl hover:bg-gray-600/5 dark:hover:bg-gray-200/5 text-gray-700 hover:text-gray-900 dark:text-gray-400 dark:hover:text-gray-300" style="padding-left:1rem" href="rag-from-external-data-provider.html"><div class="flex-1 flex items-center space-x-2.5"><div>RAG from an External Data Provider</div></div></a></li></ul></div><div class="mt-12 lg:mt-8"><h5 class="pl-4 mb-3.5 lg:mb-2.5 font-semibold text-gray-900 dark:text-gray-200">Authentication Webhook</h5><ul><li id="/user-identity" class="scroll-m-4 first:scroll-m-20"><a class="group mt-2 lg:mt-0 flex items-center pr-3 py-1.5 cursor-pointer focus:outline-primary dark:focus:outline-primary-light space-x-3 rounded-xl hover:bg-gray-600/5 dark:hover:bg-gray-200/5 text-gray-700 hover:text-gray-900 dark:text-gray-400 dark:hover:text-gray-300" style="padding-left:1rem" href="user-identity.html"><div class="flex-1 flex items-center space-x-2.5"><div>User Identity Verification</div></div></a></li></ul></div></div></div></div></div><div class="" id="content-container"><div class="flex flex-row gap-12 box-border w-full pt-40 lg:pt-10"><div class="relative grow box-border flex-col w-full mx-auto px-1 lg:pl-[23.7rem] lg:-ml-12 xl:w-[calc(100%-28rem)]" id="content-area"><header id="header" class="relative"><div class="mt-0.5 space-y-2.5"><div class="eyebrow h-5 text-primary dark:text-primary-light text-sm font-semibold">Guides</div><div class="flex items-center"><h1 class="inline-block text-2xl sm:text-3xl font-extrabold text-gray-900 tracking-tight dark:text-gray-200">Bring Your Own Key (BYOK) and Pricing</h1></div></div></header><div class="flex flex-col gap-8"><div class="flex flex-col gap-6 xl:hidden [&amp;:not(:empty)]:mt-8"></div></div><div class="relative mt-8 prose prose-gray dark:prose-invert"><p>GPT-trainer operates on a “pay as you go” basis. Rather than having Message Credits (MCs) baked into existing subscriptions, you
buy them separately via add-ons or connect your own AI API keys to enjoy unlimited usage.</p>
<p>If you use your own AI API keys, then after you exhaust all MCs inside your GPT-trainer account, additional AI usage will be charged
against your own API keys.</p>
<h2 class="flex whitespace-pre-wrap group" id="setting-up-byok"><div class="absolute"><a href="#setting-up-byok" class="-ml-10 flex items-center opacity-0 border-0 group-hover:opacity-100" aria-label="Navigate to header"><div class="w-6 h-6 text-gray-400 rounded-md flex items-center justify-center zinc-box bg-white ring-1 ring-gray-400/30 dark:ring-gray-700/25 hover:ring-gray-400/60 dark:hover:ring-white/20"><svg xmlns="http://www.w3.org/2000/svg" fill="gray" height="12px" viewBox="0 0 576 512"><path d="M0 256C0 167.6 71.6 96 160 96h72c13.3 0 24 10.7 24 24s-10.7 24-24 24H160C98.1 144 48 194.1 48 256s50.1 112 112 112h72c13.3 0 24 10.7 24 24s-10.7 24-24 24H160C71.6 416 0 344.4 0 256zm576 0c0 88.4-71.6 160-160 160H344c-13.3 0-24-10.7-24-24s10.7-24 24-24h72c61.9 0 112-50.1 112-112s-50.1-112-112-112H344c-13.3 0-24-10.7-24-24s10.7-24 24-24h72c88.4 0 160 71.6 160 160zM184 232H392c13.3 0 24 10.7 24 24s-10.7 24-24 24H184c-13.3 0-24-10.7-24-24s10.7-24 24-24z"></path></svg></div></a></div><span class="cursor-pointer">Setting up BYOK</span></h2>
<p>To set up your own API key, you will first need to register on the AI provider’s website. GPT-trainer currently
supports large language models (LLMs) from OpenAI, Anthropic, and Google. In the future, we plan to expand our list of supported
models, potentially incorporating open-source and fine-tuned ones.</p>
<h3 class="flex whitespace-pre-wrap group" id="provider-specific-instructions"><div class="absolute"><a href="#provider-specific-instructions" class="-ml-10 flex items-center opacity-0 border-0 group-hover:opacity-100" aria-label="Navigate to header"><div class="w-6 h-6 text-gray-400 rounded-md flex items-center justify-center zinc-box bg-white ring-1 ring-gray-400/30 dark:ring-gray-700/25 hover:ring-gray-400/60 dark:hover:ring-white/20"><svg xmlns="http://www.w3.org/2000/svg" fill="gray" height="12px" viewBox="0 0 576 512"><path d="M0 256C0 167.6 71.6 96 160 96h72c13.3 0 24 10.7 24 24s-10.7 24-24 24H160C98.1 144 48 194.1 48 256s50.1 112 112 112h72c13.3 0 24 10.7 24 24s-10.7 24-24 24H160C71.6 416 0 344.4 0 256zm576 0c0 88.4-71.6 160-160 160H344c-13.3 0-24-10.7-24-24s10.7-24 24-24h72c61.9 0 112-50.1 112-112s-50.1-112-112-112H344c-13.3 0-24-10.7-24-24s10.7-24 24-24h72c88.4 0 160 71.6 160 160zM184 232H392c13.3 0 24 10.7 24 24s-10.7 24-24 24H184c-13.3 0-24-10.7-24-24s10.7-24 24-24z"></path></svg></div></a></div><span class="cursor-pointer">Provider-specific instructions</span></h3>
<ul>
<li>
<p>OpenAI (GPT models)- <a href="https://platform.openai.com/account/api-keys" target="_blank" rel="noreferrer">https://platform.openai.com/account/api-keys</a></p>
</li>
<li>
<p>Anthropic (Claude models) - <a href="https://console.anthropic.com/settings/keys" target="_blank" rel="noreferrer">https://console.anthropic.com/settings/keys</a></p>
</li>
<li>
<p>Google (Gemini models) - <a href="https://ai.google.dev/gemini-api/docs/api-key" target="_blank" rel="noreferrer">https://ai.google.dev/gemini-api/docs/api-key</a></p>
</li>
</ul>
<p>After you obtain your API key, store it in a <strong>private and secure</strong> location. GPT-trainer recommends that you use a separate API
key for each of your BYOK applications (including GPT-trainer), and to never share your API key with anyone else.</p>
<p>Next, log in to GPT-trainer and go to the top right corner of the UI. Click the “Profile” icon the bring up a dropdown menu.</p>
<div><div class="p-2 not-prose relative bg-gray-50/50 rounded-2xl overflow-hidden dark:bg-gray-800/25"><div style="background-position:10px 10px" class="absolute inset-0 bg-grid-neutral-200/20 [mask-image:linear-gradient(0deg,#fff,rgba(255,255,255,0.6))] dark:bg-grid-white/5 dark:[mask-image:linear-gradient(0deg,rgba(255,255,255,0.1),rgba(255,255,255,0.5))]"></div><div class="relative rounded-xl overflow-hidden flex justify-center"></div><div class="absolute inset-0 pointer-events-none border border-black/5 rounded-2xl dark:border-white/5"></div></div></div>
<p>Then, click “Account”. In the <strong>“AI API Key”</strong> section, pick your AI provider and paste your API key. Then click “Add”.
Please note that this is <strong>not</strong> the “GPT-trainer API keys” section which is used to generate GPT-trainer API keys.</p>
<div><div class="p-2 not-prose relative bg-gray-50/50 rounded-2xl overflow-hidden dark:bg-gray-800/25"><div style="background-position:10px 10px" class="absolute inset-0 bg-grid-neutral-200/20 [mask-image:linear-gradient(0deg,#fff,rgba(255,255,255,0.6))] dark:bg-grid-white/5 dark:[mask-image:linear-gradient(0deg,rgba(255,255,255,0.1),rgba(255,255,255,0.5))]"></div><div class="relative rounded-xl overflow-hidden flex justify-center"></div><div class="absolute inset-0 pointer-events-none border border-black/5 rounded-2xl dark:border-white/5"></div></div></div>
<p>You should be all set.</p>
<div class="my-4 px-5 py-4 overflow-hidden rounded-2xl flex gap-3 border border-sky-500/20 bg-sky-50/50 dark:border-sky-500/30 dark:bg-sky-500/10"><div class="mt-0.5 w-4"><svg width="14" height="14" viewBox="0 0 14 14" fill="currentColor" xmlns="http://www.w3.org/2000/svg" class="w-4 h-4 text-sky-500" aria-label="Note"><path fill-rule="evenodd" clip-rule="evenodd" d="M7 1.3C10.14 1.3 12.7 3.86 12.7 7C12.7 10.14 10.14 12.7 7 12.7C5.48908 12.6974 4.0408 12.096 2.97241 11.0276C1.90403 9.9592 1.30264 8.51092 1.3 7C1.3 3.86 3.86 1.3 7 1.3ZM7 0C3.14 0 0 3.14 0 7C0 10.86 3.14 14 7 14C10.86 14 14 10.86 14 7C14 3.14 10.86 0 7 0ZM8 3H6V8H8V3ZM8 9H6V11H8V9Z"></path></svg></div><div class="text-sm prose min-w-0 text-sky-900 dark:text-sky-200"><p>If this is the first time you create an API key with a provider, there is a
good chance that your key may be rate and feature limited. For example, as of
November 1, 2024, all new API keys registered with new OpenAI accounts are
prohibited from running GPT-4o model series. To lift this restriction, you may
need to add $5 credt and provide verified billing information within your
OpenAI account. Different providers may have different policies regarding
account verification.</p></div></div>
<h2 class="flex whitespace-pre-wrap group" id="budgeting-for-ai-usage"><div class="absolute"><a href="#budgeting-for-ai-usage" class="-ml-10 flex items-center opacity-0 border-0 group-hover:opacity-100" aria-label="Navigate to header"><div class="w-6 h-6 text-gray-400 rounded-md flex items-center justify-center zinc-box bg-white ring-1 ring-gray-400/30 dark:ring-gray-700/25 hover:ring-gray-400/60 dark:hover:ring-white/20"><svg xmlns="http://www.w3.org/2000/svg" fill="gray" height="12px" viewBox="0 0 576 512"><path d="M0 256C0 167.6 71.6 96 160 96h72c13.3 0 24 10.7 24 24s-10.7 24-24 24H160C98.1 144 48 194.1 48 256s50.1 112 112 112h72c13.3 0 24 10.7 24 24s-10.7 24-24 24H160C71.6 416 0 344.4 0 256zm576 0c0 88.4-71.6 160-160 160H344c-13.3 0-24-10.7-24-24s10.7-24 24-24h72c61.9 0 112-50.1 112-112s-50.1-112-112-112H344c-13.3 0-24-10.7-24-24s10.7-24 24-24h72c88.4 0 160 71.6 160 160zM184 232H392c13.3 0 24 10.7 24 24s-10.7 24-24 24H184c-13.3 0-24-10.7-24-24s10.7-24 24-24z"></path></svg></div></a></div><span class="cursor-pointer">Budgeting for AI usage</span></h2>
<p>In general, using your own API key will be more cost efficient than purchasing MC add-ons directly. To help you estimate costs
associated with running your BYOK account, we provide the following references.</p>
<div class="my-4 px-5 py-4 overflow-hidden rounded-2xl flex gap-3 border border-sky-500/20 bg-sky-50/50 dark:border-sky-500/30 dark:bg-sky-500/10"><div class="mt-0.5 w-4"><svg width="14" height="14" viewBox="0 0 14 14" fill="currentColor" xmlns="http://www.w3.org/2000/svg" class="w-4 h-4 text-sky-500" aria-label="Note"><path fill-rule="evenodd" clip-rule="evenodd" d="M7 1.3C10.14 1.3 12.7 3.86 12.7 7C12.7 10.14 10.14 12.7 7 12.7C5.48908 12.6974 4.0408 12.096 2.97241 11.0276C1.90403 9.9592 1.30264 8.51092 1.3 7C1.3 3.86 3.86 1.3 7 1.3ZM7 0C3.14 0 0 3.14 0 7C0 10.86 3.14 14 7 14C10.86 14 14 10.86 14 7C14 3.14 10.86 0 7 0ZM8 3H6V8H8V3ZM8 9H6V11H8V9Z"></path></svg></div><div class="text-sm prose min-w-0 text-sky-900 dark:text-sky-200"><p>LLM providers change their pricing from time to time, so the information we
provide may not always be up-to-date. For latest information on pricing,
please visit:</p><ul>
<li>OpenAI (GPT models): <a href="https://openai.com/pricing" target="_blank" rel="noreferrer">https://openai.com/pricing</a></li>
<li>Anthropic (Claude models): <a href="https://www.anthropic.com/pricing#anthropic-api" target="_blank" rel="noreferrer">https://www.anthropic.com/pricing#anthropic-api</a></li>
<li>Google (Gemini models): <a href="https://ai.google.dev/pricing#1%5C_5flash" target="_blank" rel="noreferrer">https://ai.google.dev/pricing#1\_5flash</a></li>
</ul></div></div>
<p>The simplest estimate you can use is this:</p>
<ul>
<li>Each Message Credit costs ~$0.0032 USD</li>
</ul>
<p>For example, GPT-4-1106-4k uses 20 MCs per query. To estimate the cost in USD, multiply <span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mn>0.0032</mn><mstyle mathcolor="#cc0000"><mtext>\*</mtext></mstyle><mn>20</mn><mo>=</mo></mrow><annotation encoding="application/x-tex">0.0032 \* 20 = </annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em"></span><span class="mord">0.0032</span><span class="mord text" style="color:#cc0000"><span class="mord" style="color:#cc0000">\*</span></span><span class="mord">20</span><span class="mspace" style="margin-right:0.2778em"></span><span class="mrel">=</span></span></span></span>0.064 USD.</p>
<p>This is a very rough estimate. Actual spend may be around +/- 20% of the estimated number. This is because LLM usage is measured
based on “tokens”, and input vs. output tokens cost different amounts even for the same LLM. To understand tokens, we
refer you to this article: <a href="https://help.openai.com/en/articles/4936856-what-are-tokens-and-how-to-count-them." target="_blank" rel="noreferrer">https://help.openai.com/en/articles/4936856-what-are-tokens-and-how-to-count-them.</a></p>
<p>There are many parts to a LLM query. At a high level, they can be broken down into:</p>
<p>Input:</p>
<ul>
<li>
<p>System prompt and metadata</p>
</li>
<li>
<p>User-defined base prompt</p>
</li>
<li>
<p>Variables and definitions</p>
</li>
<li>
<p>Conversation label definitions</p>
</li>
<li>
<p>Function metadata and descriptions</p>
</li>
<li>
<p>Function parameters</p>
</li>
<li>
<p>Function response</p>
</li>
<li>
<p>Static RAG context</p>
</li>
<li>
<p>Conversation memory</p>
</li>
</ul>
<p>Output:</p>
<ul>
<li>
<p>Text response</p>
</li>
<li>
<p>Response metadata</p>
</li>
</ul>
<h3 class="flex whitespace-pre-wrap group" id="openai-pricing-breakdown"><div class="absolute"><a href="#openai-pricing-breakdown" class="-ml-10 flex items-center opacity-0 border-0 group-hover:opacity-100" aria-label="Navigate to header"><div class="w-6 h-6 text-gray-400 rounded-md flex items-center justify-center zinc-box bg-white ring-1 ring-gray-400/30 dark:ring-gray-700/25 hover:ring-gray-400/60 dark:hover:ring-white/20"><svg xmlns="http://www.w3.org/2000/svg" fill="gray" height="12px" viewBox="0 0 576 512"><path d="M0 256C0 167.6 71.6 96 160 96h72c13.3 0 24 10.7 24 24s-10.7 24-24 24H160C98.1 144 48 194.1 48 256s50.1 112 112 112h72c13.3 0 24 10.7 24 24s-10.7 24-24 24H160C71.6 416 0 344.4 0 256zm576 0c0 88.4-71.6 160-160 160H344c-13.3 0-24-10.7-24-24s10.7-24 24-24h72c61.9 0 112-50.1 112-112s-50.1-112-112-112H344c-13.3 0-24-10.7-24-24s10.7-24 24-24h72c88.4 0 160 71.6 160 160zM184 232H392c13.3 0 24 10.7 24 24s-10.7 24-24 24H184c-13.3 0-24-10.7-24-24s10.7-24 24-24z"></path></svg></div></a></div><span class="cursor-pointer">OpenAI pricing breakdown</span></h3>
<p>GPT-trainer supports a variety of OpenAI LLMs, as well as different versions of the same LLM with custom token limit cutoffs.
To help you budget for your usage, we provide a summary based on our <strong>default</strong> split of reserved input vs. output tokens. Please refer
to the following table (all $ are in USD). Please note that this generally represents an upper limit since not all input / output tokens
in the reservation window are used every single LLM query.</p>
<table><thead><tr><th>Model</th><th>Reserved for Input</th><th>Reserved for Output</th><th>Cost / Input Token</th><th>Cost / Output Token</th><th>Total Cost per Query</th></tr></thead><tbody><tr><td>GPT-3.5</td><td>2800</td><td>1200</td><td>0.0000005</td><td>0.0000015</td><td>0.0032</td></tr><tr><td>GPT-3.5-16k</td><td>13600</td><td>2400</td><td>0.0000005</td><td>0.0000015</td><td>0.0104</td></tr><tr><td>GPT-4o-mini-1k</td><td>800</td><td>200</td><td>0.00000015</td><td>0.0000006</td><td>0.00024</td></tr><tr><td>GPT-4o-mini-2k</td><td>1600</td><td>400</td><td>0.00000015</td><td>0.0000006</td><td>0.00048</td></tr><tr><td>GPT-4o-mini-4k</td><td>2800</td><td>1200</td><td>0.00000015</td><td>0.0000006</td><td>0.00114</td></tr><tr><td>GPT-4o-mini-8k</td><td>5600</td><td>2400</td><td>0.00000015</td><td>0.0000006</td><td>0.00228</td></tr><tr><td>GPT-4o-mini-16k</td><td>12800</td><td>3200</td><td>0.00000015</td><td>0.0000006</td><td>0.00384</td></tr><tr><td>GPT-4o-mini-32k</td><td>28000</td><td>4000</td><td>0.00000015</td><td>0.0000006</td><td>0.0066</td></tr><tr><td>GPT-4o-mini-64k</td><td>60000</td><td>4000</td><td>0.00000015</td><td>0.0000006</td><td>0.0114</td></tr><tr><td>GPT-4o-1k</td><td>800</td><td>200</td><td>0.0000025</td><td>0.00001</td><td>0.004</td></tr><tr><td>GPT-4o-2k</td><td>1600</td><td>400</td><td>0.0000025</td><td>0.00001</td><td>0.008</td></tr><tr><td>GPT-4o-4k</td><td>2800</td><td>1200</td><td>0.0000025</td><td>0.00001</td><td>0.019</td></tr><tr><td>GPT-4o-8k</td><td>5600</td><td>2400</td><td>0.0000025</td><td>0.00001</td><td>0.038</td></tr><tr><td>GPT-4o-16k</td><td>12800</td><td>3200</td><td>0.0000025</td><td>0.00001</td><td>0.064</td></tr><tr><td>GPT-4o-32k</td><td>28000</td><td>4000</td><td>0.0000025</td><td>0.00001</td><td>0.11</td></tr><tr><td>GPT-4o-64k</td><td>60000</td><td>4000</td><td>0.0000025</td><td>0.00001</td><td>0.19</td></tr><tr><td>GPT-4-1106-1k</td><td>800</td><td>200</td><td>0.00001</td><td>0.00003</td><td>0.014</td></tr><tr><td>GPT-4-1106-2k</td><td>1600</td><td>400</td><td>0.00001</td><td>0.00003</td><td>0.028</td></tr><tr><td>GPT-4-1106-4k</td><td>2800</td><td>1200</td><td>0.00001</td><td>0.00003</td><td>0.064</td></tr><tr><td>GPT-4-0125-8k</td><td>5600</td><td>2400</td><td>0.00001</td><td>0.00003</td><td>0.128</td></tr><tr><td>GPT-4-1106-16k</td><td>12800</td><td>3200</td><td>0.00001</td><td>0.00003</td><td>0.224</td></tr><tr><td>GPT-4-1106-32k</td><td>28000</td><td>4000</td><td>0.00001</td><td>0.00003</td><td>0.4</td></tr><tr><td>GPT-4-1106-64k</td><td>60000</td><td>4000</td><td>0.00001</td><td>0.00003</td><td>0.72</td></tr></tbody></table>
<h2 class="flex whitespace-pre-wrap group" id="byok-for-white-label-commercial-partners"><div class="absolute"><a href="#byok-for-white-label-commercial-partners" class="-ml-10 flex items-center opacity-0 border-0 group-hover:opacity-100" aria-label="Navigate to header"><div class="w-6 h-6 text-gray-400 rounded-md flex items-center justify-center zinc-box bg-white ring-1 ring-gray-400/30 dark:ring-gray-700/25 hover:ring-gray-400/60 dark:hover:ring-white/20"><svg xmlns="http://www.w3.org/2000/svg" fill="gray" height="12px" viewBox="0 0 576 512"><path d="M0 256C0 167.6 71.6 96 160 96h72c13.3 0 24 10.7 24 24s-10.7 24-24 24H160C98.1 144 48 194.1 48 256s50.1 112 112 112h72c13.3 0 24 10.7 24 24s-10.7 24-24 24H160C71.6 416 0 344.4 0 256zm576 0c0 88.4-71.6 160-160 160H344c-13.3 0-24-10.7-24-24s10.7-24 24-24h72c61.9 0 112-50.1 112-112s-50.1-112-112-112H344c-13.3 0-24-10.7-24-24s10.7-24 24-24h72c88.4 0 160 71.6 160 160zM184 232H392c13.3 0 24 10.7 24 24s-10.7 24-24 24H184c-13.3 0-24-10.7-24-24s10.7-24 24-24z"></path></svg></div></a></div><span class="cursor-pointer">BYOK for white-label commercial partners</span></h2>
<p>In addition to the costs for MC expenditures during LLM queries, you also need to pay to run our AI multi-agent framework using your
own API key. This is independent of whether your users have supplied their API key for their personal accounts. Since the official GPT-trainer
subsidizes its users for all costs associated with running the AI framework, your white-label solution must operate with this premise
as well.</p>
<p>There are three separate workflows that require your own API key to cover your user’s AI expenditures. The conditions under which they apply
are listed below.</p>
<ol>
<li>AI Agent intent generation</li>
</ol>
<ul>
<li>
<p>Applicable if two or more AI Agents are connected</p>
</li>
<li>
<p>Charged whenever a new user-facing AI Agent goes live or an existing one is edited</p>
</li>
</ul>
<ol>
<li>Query intent classification</li>
</ol>
<ul>
<li>
<p>Applicable if two or more user-facing Agents are connected</p>
</li>
<li>
<p>Charged on a per query basis</p>
</li>
</ul>
<ol>
<li>Variable extraction</li>
</ol>
<ul>
<li>
<p>Applicable if AI Agent has one or more variables set up</p>
</li>
<li>
<p>Charged on a per query basis</p>
</li>
</ul>
<p>The estimated costs associated with each workflow is as follows:</p>
<table><thead><tr><th>Workflow</th><th>Average Estimated Input</th><th>Average Estimated Output</th><th>Cost / Input Token</th><th>Cost / Output Token</th><th>Estimated Cost per Run</th></tr></thead><tbody><tr><td>AI Agent intent generation (gpt-4-1106-preview)</td><td>600</td><td>450</td><td>0.00001</td><td>0.00003</td><td>0.0011</td></tr><tr><td>Query intent classification (gpt-3.5-turbo-1106)</td><td>1000</td><td>50</td><td>0.000001</td><td>0.000002</td><td>0.0195</td></tr><tr><td>Variables extraction (gpt-3.5-turbo-1106)</td><td>1000</td><td>100</td><td>0.000001</td><td>0.000002</td><td>0.0012</td></tr></tbody></table>
<p>All costs from the above workflow will be charged directly against your own API key. This cost cannot be transferred onto your clients.</p>
<p>There may be more AI features in the future that require using your own API key. We will update the documentation as needed to help you plan ahead.
However, in general, these extra API costs should be fairly negligible in comparison to AI costs associated with Message Credit expenditures.</p></div><div class="leading-6 mt-14"><div class="mb-12 px-0.5 flex items-center text-sm font-semibold text-gray-700 dark:text-gray-200"><a class="flex items-center space-x-3 group" href="supervisor-overrides.html"><svg viewBox="0 0 3 6" class="h-1.5 stroke-gray-400 overflow-visible group-hover:stroke-gray-600 dark:group-hover:stroke-gray-300"><path d="M3 0L0 3L3 6" fill="none" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"></path></svg><span class="group-hover:text-gray-900 dark:group-hover:text-white">AI Supervisor Overrides</span></a><a class="flex items-center ml-auto space-x-3 group" href="working-with-tables.html"><span class="group-hover:text-gray-900 dark:group-hover:text-white">Working with Tables and CSV</span><svg viewBox="0 0 3 6" class="rotate-180 h-1.5 stroke-gray-400 overflow-visible group-hover:stroke-gray-600 dark:group-hover:stroke-gray-300"><path d="M3 0L0 3L3 6" fill="none" stroke-width="2" stroke-linecap="round" stroke-linejoin="round"></path></svg></a></div><footer id="footer" class="flex gap-12 justify-between pt-10 border-t border-gray-100 sm:flex dark:border-gray-800/50 pb-28"><div class="flex items-center justify-between"><div class="sm:flex"><a href="https://mintlify.com/preview-request?utm_campaign=poweredBy&amp;utm_medium=docs&amp;utm_source=guide.gpt-trainer.com" target="_blank" rel="noreferrer" class="text-sm text-gray-500 dark:text-gray-400 hover:text-gray-700 dark:hover:text-gray-300">Powered by Mintlify</a></div></div></footer></div></div><div class="z-10 hidden xl:flex pl-10 box-border w-[19rem]" id="table-of-contents"><div id="table-of-contents-content" class="fixed text-gray-600 text-sm leading-6 w-[16.5rem] overflow-y-auto space-y-2 h-[calc(100%-7rem)]"><div class="text-gray-700 dark:text-gray-300 font-medium flex items-center space-x-2"><svg width="16" height="16" viewBox="0 0 16 16" fill="none" stroke="currentColor" stroke-width="2" xmlns="http://www.w3.org/2000/svg" class="h-3 w-3"><path d="M2.44434 12.6665H13.5554" stroke-linecap="round" stroke-linejoin="round"></path><path d="M2.44434 3.3335H13.5554" stroke-linecap="round" stroke-linejoin="round"></path><path d="M2.44434 8H7.33323" stroke-linecap="round" stroke-linejoin="round"></path></svg><span>On this page</span></div><ul><li><a href="#setting-up-byok" class="py-1 block font-medium hover:text-gray-900 dark:text-gray-400 dark:hover:text-gray-300">Setting up BYOK</a></li><li style="margin-left:1rem"><a href="#provider-specific-instructions" class="group flex items-start py-1 whitespace-pre-wrap text-gray-500 hover:text-gray-900 dark:text-gray-400 dark:hover:text-gray-300">Provider-specific instructions</a></li><li><a href="#budgeting-for-ai-usage" class="py-1 block font-medium hover:text-gray-900 dark:text-gray-400 dark:hover:text-gray-300">Budgeting for AI usage</a></li><li style="margin-left:1rem"><a href="#openai-pricing-breakdown" class="group flex items-start py-1 whitespace-pre-wrap text-gray-500 hover:text-gray-900 dark:text-gray-400 dark:hover:text-gray-300">OpenAI pricing breakdown</a></li><li><a href="#byok-for-white-label-commercial-partners" class="py-1 block font-medium hover:text-gray-900 dark:text-gray-400 dark:hover:text-gray-300">BYOK for white-label commercial partners</a></li></ul></div></div></div></div></div></div></main></div><script id="__NEXT_DATA__" type="application/json">{"props":{"pageProps":{"mdxSource":{"compiledSource":"\"use strict\";\nconst {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];\nconst {useMDXComponents: _provideComponents} = arguments[0];\nfunction _createMdxContent(props) {\n  const _components = {\n    a: \"a\",\n    annotation: \"annotation\",\n    li: \"li\",\n    math: \"math\",\n    mn: \"mn\",\n    mo: \"mo\",\n    mrow: \"mrow\",\n    mstyle: \"mstyle\",\n    mtext: \"mtext\",\n    ol: \"ol\",\n    p: \"p\",\n    semantics: \"semantics\",\n    span: \"span\",\n    strong: \"strong\",\n    table: \"table\",\n    tbody: \"tbody\",\n    td: \"td\",\n    th: \"th\",\n    thead: \"thead\",\n    tr: \"tr\",\n    ul: \"ul\",\n    ..._provideComponents(),\n    ...props.components\n  }, {Frame, Heading, Note, ZoomImage} = _components;\n  if (!Frame) _missingMdxReference(\"Frame\", true);\n  if (!Heading) _missingMdxReference(\"Heading\", true);\n  if (!Note) _missingMdxReference(\"Note\", true);\n  if (!ZoomImage) _missingMdxReference(\"ZoomImage\", true);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"GPT-trainer operates on a “pay as you go” basis. Rather than having Message Credits (MCs) baked into existing subscriptions, you\\nbuy them separately via add-ons or connect your own AI API keys to enjoy unlimited usage.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"If you use your own AI API keys, then after you exhaust all MCs inside your GPT-trainer account, additional AI usage will be charged\\nagainst your own API keys.\"\n    }), \"\\n\", _jsx(Heading, {\n      level: \"2\",\n      id: \"setting-up-byok\",\n      children: \"Setting up BYOK\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"To set up your own API key, you will first need to register on the AI provider’s website. GPT-trainer currently\\nsupports large language models (LLMs) from OpenAI, Anthropic, and Google. In the future, we plan to expand our list of supported\\nmodels, potentially incorporating open-source and fine-tuned ones.\"\n    }), \"\\n\", _jsx(Heading, {\n      level: \"3\",\n      id: \"provider-specific-instructions\",\n      children: \"Provider-specific instructions\"\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsxs(_components.li, {\n        children: [\"\\n\", _jsxs(_components.p, {\n          children: [\"OpenAI (GPT models)- \", _jsx(_components.a, {\n            href: \"https://platform.openai.com/account/api-keys\",\n            children: \"https://platform.openai.com/account/api-keys\"\n          })]\n        }), \"\\n\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"\\n\", _jsxs(_components.p, {\n          children: [\"Anthropic (Claude models) - \", _jsx(_components.a, {\n            href: \"https://console.anthropic.com/settings/keys\",\n            children: \"https://console.anthropic.com/settings/keys\"\n          })]\n        }), \"\\n\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"\\n\", _jsxs(_components.p, {\n          children: [\"Google (Gemini models) - \", _jsx(_components.a, {\n            href: \"https://ai.google.dev/gemini-api/docs/api-key\",\n            children: \"https://ai.google.dev/gemini-api/docs/api-key\"\n          })]\n        }), \"\\n\"]\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"After you obtain your API key, store it in a \", _jsx(_components.strong, {\n        children: \"private and secure\"\n      }), \" location. GPT-trainer recommends that you use a separate API\\nkey for each of your BYOK applications (including GPT-trainer), and to never share your API key with anyone else.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Next, log in to GPT-trainer and go to the top right corner of the UI. Click the “Profile” icon the bring up a dropdown menu.\"\n    }), \"\\n\", _jsx(Frame, {\n      children: _jsx(ZoomImage, {\n        children: _jsx(\"img\", {\n          src: \"https://mintlify.s3.us-west-1.amazonaws.com/paladinmaxinc/images/byok-pricing-guide-1.png\"\n        })\n      })\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Then, click “Account”. In the \", _jsx(_components.strong, {\n        children: \"“AI API Key”\"\n      }), \" section, pick your AI provider and paste your API key. Then click “Add”.\\nPlease note that this is \", _jsx(_components.strong, {\n        children: \"not\"\n      }), \" the “GPT-trainer API keys” section which is used to generate GPT-trainer API keys.\"]\n    }), \"\\n\", _jsx(Frame, {\n      children: _jsx(ZoomImage, {\n        children: _jsx(\"img\", {\n          src: \"https://mintlify.s3.us-west-1.amazonaws.com/paladinmaxinc/images/byok-pricing-guide-3.png\"\n        })\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"You should be all set.\"\n    }), \"\\n\", _jsx(Note, {\n      children: _jsx(_components.p, {\n        children: \"If this is the first time you create an API key with a provider, there is a\\ngood chance that your key may be rate and feature limited. For example, as of\\nNovember 1, 2024, all new API keys registered with new OpenAI accounts are\\nprohibited from running GPT-4o model series. To lift this restriction, you may\\nneed to add $5 credt and provide verified billing information within your\\nOpenAI account. Different providers may have different policies regarding\\naccount verification.\"\n      })\n    }), \"\\n\", _jsx(Heading, {\n      level: \"2\",\n      id: \"budgeting-for-ai-usage\",\n      children: \"Budgeting for AI usage\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"In general, using your own API key will be more cost efficient than purchasing MC add-ons directly. To help you estimate costs\\nassociated with running your BYOK account, we provide the following references.\"\n    }), \"\\n\", _jsxs(Note, {\n      children: [_jsx(_components.p, {\n        children: \"LLM providers change their pricing from time to time, so the information we\\nprovide may not always be up-to-date. For latest information on pricing,\\nplease visit:\"\n      }), _jsxs(_components.ul, {\n        children: [\"\\n\", _jsxs(_components.li, {\n          children: [\"OpenAI (GPT models): \", _jsx(_components.a, {\n            href: \"https://openai.com/pricing\",\n            children: \"https://openai.com/pricing\"\n          })]\n        }), \"\\n\", _jsxs(_components.li, {\n          children: [\"Anthropic (Claude models): \", _jsx(_components.a, {\n            href: \"https://www.anthropic.com/pricing#anthropic-api\",\n            children: \"https://www.anthropic.com/pricing#anthropic-api\"\n          })]\n        }), \"\\n\", _jsxs(_components.li, {\n          children: [\"Google (Gemini models): \", _jsx(_components.a, {\n            href: \"https://ai.google.dev/pricing#1%5C_5flash\",\n            children: \"https://ai.google.dev/pricing#1\\\\_5flash\"\n          })]\n        }), \"\\n\"]\n      })]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The simplest estimate you can use is this:\"\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Each Message Credit costs ~$0.0032 USD\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"For example, GPT-4-1106-4k uses 20 MCs per query. To estimate the cost in USD, multiply \", _jsxs(_components.span, {\n        className: \"katex\",\n        children: [_jsx(_components.span, {\n          className: \"katex-mathml\",\n          children: _jsx(_components.math, {\n            xmlns: \"http://www.w3.org/1998/Math/MathML\",\n            children: _jsxs(_components.semantics, {\n              children: [_jsxs(_components.mrow, {\n                children: [_jsx(_components.mn, {\n                  children: \"0.0032\"\n                }), _jsx(_components.mstyle, {\n                  mathcolor: \"#cc0000\",\n                  children: _jsx(_components.mtext, {\n                    children: \"\\\\*\"\n                  })\n                }), _jsx(_components.mn, {\n                  children: \"20\"\n                }), _jsx(_components.mo, {\n                  children: \"=\"\n                })]\n              }), _jsx(_components.annotation, {\n                encoding: \"application/x-tex\",\n                children: \"0.0032 \\\\* 20 = \"\n              })]\n            })\n          })\n        }), _jsx(_components.span, {\n          className: \"katex-html\",\n          \"aria-hidden\": \"true\",\n          children: _jsxs(_components.span, {\n            className: \"base\",\n            children: [_jsx(_components.span, {\n              className: \"strut\",\n              style: {\n                height: \"1em\",\n                verticalAlign: \"-0.25em\"\n              }\n            }), _jsx(_components.span, {\n              className: \"mord\",\n              children: \"0.0032\"\n            }), _jsx(_components.span, {\n              className: \"mord text\",\n              style: {\n                color: \"#cc0000\"\n              },\n              children: _jsx(_components.span, {\n                className: \"mord\",\n                style: {\n                  color: \"#cc0000\"\n                },\n                children: \"\\\\*\"\n              })\n            }), _jsx(_components.span, {\n              className: \"mord\",\n              children: \"20\"\n            }), _jsx(_components.span, {\n              className: \"mspace\",\n              style: {\n                marginRight: \"0.2778em\"\n              }\n            }), _jsx(_components.span, {\n              className: \"mrel\",\n              children: \"=\"\n            })]\n          })\n        })]\n      }), \"0.064 USD.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"This is a very rough estimate. Actual spend may be around +/- 20% of the estimated number. This is because LLM usage is measured\\nbased on “tokens”, and input vs. output tokens cost different amounts even for the same LLM. To understand tokens, we\\nrefer you to this article: \", _jsx(_components.a, {\n        href: \"https://help.openai.com/en/articles/4936856-what-are-tokens-and-how-to-count-them.\",\n        children: \"https://help.openai.com/en/articles/4936856-what-are-tokens-and-how-to-count-them.\"\n      })]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"There are many parts to a LLM query. At a high level, they can be broken down into:\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Input:\"\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsxs(_components.li, {\n        children: [\"\\n\", _jsx(_components.p, {\n          children: \"System prompt and metadata\"\n        }), \"\\n\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"\\n\", _jsx(_components.p, {\n          children: \"User-defined base prompt\"\n        }), \"\\n\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"\\n\", _jsx(_components.p, {\n          children: \"Variables and definitions\"\n        }), \"\\n\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"\\n\", _jsx(_components.p, {\n          children: \"Conversation label definitions\"\n        }), \"\\n\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"\\n\", _jsx(_components.p, {\n          children: \"Function metadata and descriptions\"\n        }), \"\\n\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"\\n\", _jsx(_components.p, {\n          children: \"Function parameters\"\n        }), \"\\n\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"\\n\", _jsx(_components.p, {\n          children: \"Function response\"\n        }), \"\\n\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"\\n\", _jsx(_components.p, {\n          children: \"Static RAG context\"\n        }), \"\\n\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"\\n\", _jsx(_components.p, {\n          children: \"Conversation memory\"\n        }), \"\\n\"]\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Output:\"\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsxs(_components.li, {\n        children: [\"\\n\", _jsx(_components.p, {\n          children: \"Text response\"\n        }), \"\\n\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"\\n\", _jsx(_components.p, {\n          children: \"Response metadata\"\n        }), \"\\n\"]\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(Heading, {\n      level: \"3\",\n      id: \"openai-pricing-breakdown\",\n      children: \"OpenAI pricing breakdown\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"GPT-trainer supports a variety of OpenAI LLMs, as well as different versions of the same LLM with custom token limit cutoffs.\\nTo help you budget for your usage, we provide a summary based on our \", _jsx(_components.strong, {\n        children: \"default\"\n      }), \" split of reserved input vs. output tokens. Please refer\\nto the following table (all $ are in USD). Please note that this generally represents an upper limit since not all input / output tokens\\nin the reservation window are used every single LLM query.\"]\n    }), \"\\n\", _jsxs(_components.table, {\n      children: [_jsx(_components.thead, {\n        children: _jsxs(_components.tr, {\n          children: [_jsx(_components.th, {\n            children: \"Model\"\n          }), _jsx(_components.th, {\n            children: \"Reserved for Input\"\n          }), _jsx(_components.th, {\n            children: \"Reserved for Output\"\n          }), _jsx(_components.th, {\n            children: \"Cost / Input Token\"\n          }), _jsx(_components.th, {\n            children: \"Cost / Output Token\"\n          }), _jsx(_components.th, {\n            children: \"Total Cost per Query\"\n          })]\n        })\n      }), _jsxs(_components.tbody, {\n        children: [_jsxs(_components.tr, {\n          children: [_jsx(_components.td, {\n            children: \"GPT-3.5\"\n          }), 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This cost cannot be transferred onto your clients.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"There may be more AI features in the future that require using your own API key. 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https://app.gpt-trainer.com/api/v1/agent/{uuid}/update","href":"/api-reference/agents/update"},{"title":"Fetch all Agents","description":"Fetch the list of agents for a chatbot specified by chatbot uuid","api":"GET https://app.gpt-trainer.com/api/v1/chatbot/{uuid}/agents","href":"/api-reference/agents/fetch_multi"},{"title":"Delete Agent","description":"Delete single agent base on uuid","api":"DELETE https://app.gpt-trainer.com/api/v1/agent/{uuid}/delete","href":"/api-reference/agents/delete"}]},{"group":"Chatbot Sessions","pages":[{"title":"Session Properties","description":"Detailed explanation of sessions's properties","href":"/api-reference/sessions/properties-reference"},{"title":"Create Session","description":"Create a chat session for a chatbot specified by chatbot uuid","api":"POST https://app.gpt-trainer.com/api/v1/chatbot/{uuid}/session/create","href":"/api-reference/sessions/create"},{"title":"Fetch a Session","description":"Fetch single chatbot session base on 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mstyle: \"mstyle\",\n    mtext: \"mtext\",\n    ol: \"ol\",\n    p: \"p\",\n    semantics: \"semantics\",\n    span: \"span\",\n    strong: \"strong\",\n    table: \"table\",\n    tbody: \"tbody\",\n    td: \"td\",\n    th: \"th\",\n    thead: \"thead\",\n    tr: \"tr\",\n    ul: \"ul\",\n    ..._provideComponents(),\n    ...props.components\n  }, {Frame, Heading, Note, ZoomImage} = _components;\n  if (!Frame) _missingMdxReference(\"Frame\", true);\n  if (!Heading) _missingMdxReference(\"Heading\", true);\n  if (!Note) _missingMdxReference(\"Note\", true);\n  if (!ZoomImage) _missingMdxReference(\"ZoomImage\", true);\n  return _jsxs(_Fragment, {\n    children: [_jsx(_components.p, {\n      children: \"GPT-trainer operates on a “pay as you go” basis. Rather than having Message Credits (MCs) baked into existing subscriptions, you\\nbuy them separately via add-ons or connect your own AI API keys to enjoy unlimited usage.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"If you use your own AI API keys, then after you exhaust all MCs inside your GPT-trainer account, additional AI usage will be charged\\nagainst your own API keys.\"\n    }), \"\\n\", _jsx(Heading, {\n      level: \"2\",\n      id: \"setting-up-byok\",\n      children: \"Setting up BYOK\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"To set up your own API key, you will first need to register on the AI provider’s website. GPT-trainer currently\\nsupports large language models (LLMs) from OpenAI, Anthropic, and Google. In the future, we plan to expand our list of supported\\nmodels, potentially incorporating open-source and fine-tuned ones.\"\n    }), \"\\n\", _jsx(Heading, {\n      level: \"3\",\n      id: \"provider-specific-instructions\",\n      children: \"Provider-specific instructions\"\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsxs(_components.li, {\n        children: [\"\\n\", _jsxs(_components.p, {\n          children: [\"OpenAI (GPT models)- \", _jsx(_components.a, {\n            href: \"https://platform.openai.com/account/api-keys\",\n            children: \"https://platform.openai.com/account/api-keys\"\n          })]\n        }), \"\\n\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"\\n\", _jsxs(_components.p, {\n          children: [\"Anthropic (Claude models) - \", _jsx(_components.a, {\n            href: \"https://console.anthropic.com/settings/keys\",\n            children: \"https://console.anthropic.com/settings/keys\"\n          })]\n        }), \"\\n\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"\\n\", _jsxs(_components.p, {\n          children: [\"Google (Gemini models) - \", _jsx(_components.a, {\n            href: \"https://ai.google.dev/gemini-api/docs/api-key\",\n            children: \"https://ai.google.dev/gemini-api/docs/api-key\"\n          })]\n        }), \"\\n\"]\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"After you obtain your API key, store it in a \", _jsx(_components.strong, {\n        children: \"private and secure\"\n      }), \" location. GPT-trainer recommends that you use a separate API\\nkey for each of your BYOK applications (including GPT-trainer), and to never share your API key with anyone else.\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Next, log in to GPT-trainer and go to the top right corner of the UI. Click the “Profile” icon the bring up a dropdown menu.\"\n    }), \"\\n\", _jsx(Frame, {\n      children: _jsx(ZoomImage, {\n        children: _jsx(\"img\", {\n          src: \"https://mintlify.s3.us-west-1.amazonaws.com/paladinmaxinc/images/byok-pricing-guide-1.png\"\n        })\n      })\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"Then, click “Account”. In the \", _jsx(_components.strong, {\n        children: \"“AI API Key”\"\n      }), \" section, pick your AI provider and paste your API key. Then click “Add”.\\nPlease note that this is \", _jsx(_components.strong, {\n        children: \"not\"\n      }), \" the “GPT-trainer API keys” section which is used to generate GPT-trainer API keys.\"]\n    }), \"\\n\", _jsx(Frame, {\n      children: _jsx(ZoomImage, {\n        children: _jsx(\"img\", {\n          src: \"https://mintlify.s3.us-west-1.amazonaws.com/paladinmaxinc/images/byok-pricing-guide-3.png\"\n        })\n      })\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"You should be all set.\"\n    }), \"\\n\", _jsx(Note, {\n      children: _jsx(_components.p, {\n        children: \"If this is the first time you create an API key with a provider, there is a\\ngood chance that your key may be rate and feature limited. For example, as of\\nNovember 1, 2024, all new API keys registered with new OpenAI accounts are\\nprohibited from running GPT-4o model series. To lift this restriction, you may\\nneed to add $5 credt and provide verified billing information within your\\nOpenAI account. Different providers may have different policies regarding\\naccount verification.\"\n      })\n    }), \"\\n\", _jsx(Heading, {\n      level: \"2\",\n      id: \"budgeting-for-ai-usage\",\n      children: \"Budgeting for AI usage\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"In general, using your own API key will be more cost efficient than purchasing MC add-ons directly. To help you estimate costs\\nassociated with running your BYOK account, we provide the following references.\"\n    }), \"\\n\", _jsxs(Note, {\n      children: [_jsx(_components.p, {\n        children: \"LLM providers change their pricing from time to time, so the information we\\nprovide may not always be up-to-date. For latest information on pricing,\\nplease visit:\"\n      }), _jsxs(_components.ul, {\n        children: [\"\\n\", _jsxs(_components.li, {\n          children: [\"OpenAI (GPT models): \", _jsx(_components.a, {\n            href: \"https://openai.com/pricing\",\n            children: \"https://openai.com/pricing\"\n          })]\n        }), \"\\n\", _jsxs(_components.li, {\n          children: [\"Anthropic (Claude models): \", _jsx(_components.a, {\n            href: \"https://www.anthropic.com/pricing#anthropic-api\",\n            children: \"https://www.anthropic.com/pricing#anthropic-api\"\n          })]\n        }), \"\\n\", _jsxs(_components.li, {\n          children: [\"Google (Gemini models): \", _jsx(_components.a, {\n            href: \"https://ai.google.dev/pricing#1%5C_5flash\",\n            children: \"https://ai.google.dev/pricing#1\\\\_5flash\"\n          })]\n        }), \"\\n\"]\n      })]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The simplest estimate you can use is this:\"\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Each Message Credit costs ~$0.0032 USD\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"For example, GPT-4-1106-4k uses 20 MCs per query. To estimate the cost in USD, multiply \", _jsxs(_components.span, {\n        className: \"katex\",\n        children: [_jsx(_components.span, {\n          className: \"katex-mathml\",\n          children: _jsx(_components.math, {\n            xmlns: \"http://www.w3.org/1998/Math/MathML\",\n            children: _jsxs(_components.semantics, {\n              children: [_jsxs(_components.mrow, {\n                children: [_jsx(_components.mn, {\n                  children: \"0.0032\"\n                }), _jsx(_components.mstyle, {\n                  mathcolor: \"#cc0000\",\n                  children: _jsx(_components.mtext, {\n                    children: \"\\\\*\"\n                  })\n                }), _jsx(_components.mn, {\n                  children: \"20\"\n                }), _jsx(_components.mo, {\n                  children: \"=\"\n                })]\n              }), _jsx(_components.annotation, {\n                encoding: \"application/x-tex\",\n                children: \"0.0032 \\\\* 20 = \"\n              })]\n            })\n          })\n        }), _jsx(_components.span, {\n          className: \"katex-html\",\n          \"aria-hidden\": \"true\",\n          children: _jsxs(_components.span, {\n            className: \"base\",\n            children: [_jsx(_components.span, {\n              className: \"strut\",\n              style: {\n                height: \"1em\",\n                verticalAlign: \"-0.25em\"\n              }\n            }), _jsx(_components.span, {\n              className: \"mord\",\n              children: \"0.0032\"\n            }), _jsx(_components.span, {\n              className: \"mord text\",\n              style: {\n                color: \"#cc0000\"\n              },\n              children: _jsx(_components.span, {\n                className: \"mord\",\n                style: {\n                  color: \"#cc0000\"\n                },\n                children: \"\\\\*\"\n              })\n            }), _jsx(_components.span, {\n              className: \"mord\",\n              children: \"20\"\n            }), _jsx(_components.span, {\n              className: \"mspace\",\n              style: {\n                marginRight: \"0.2778em\"\n              }\n            }), _jsx(_components.span, {\n              className: \"mrel\",\n              children: \"=\"\n            })]\n          })\n        })]\n      }), \"0.064 USD.\"]\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"This is a very rough estimate. Actual spend may be around +/- 20% of the estimated number. This is because LLM usage is measured\\nbased on “tokens”, and input vs. output tokens cost different amounts even for the same LLM. To understand tokens, we\\nrefer you to this article: \", _jsx(_components.a, {\n        href: \"https://help.openai.com/en/articles/4936856-what-are-tokens-and-how-to-count-them.\",\n        children: \"https://help.openai.com/en/articles/4936856-what-are-tokens-and-how-to-count-them.\"\n      })]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"There are many parts to a LLM query. At a high level, they can be broken down into:\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Input:\"\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsxs(_components.li, {\n        children: [\"\\n\", _jsx(_components.p, {\n          children: \"System prompt and metadata\"\n        }), \"\\n\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"\\n\", _jsx(_components.p, {\n          children: \"User-defined base prompt\"\n        }), \"\\n\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"\\n\", _jsx(_components.p, {\n          children: \"Variables and definitions\"\n        }), \"\\n\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"\\n\", _jsx(_components.p, {\n          children: \"Conversation label definitions\"\n        }), \"\\n\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"\\n\", _jsx(_components.p, {\n          children: \"Function metadata and descriptions\"\n        }), \"\\n\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"\\n\", _jsx(_components.p, {\n          children: \"Function parameters\"\n        }), \"\\n\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"\\n\", _jsx(_components.p, {\n          children: \"Function response\"\n        }), \"\\n\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"\\n\", _jsx(_components.p, {\n          children: \"Static RAG context\"\n        }), \"\\n\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"\\n\", _jsx(_components.p, {\n          children: \"Conversation memory\"\n        }), \"\\n\"]\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"Output:\"\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsxs(_components.li, {\n        children: [\"\\n\", _jsx(_components.p, {\n          children: \"Text response\"\n        }), \"\\n\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"\\n\", _jsx(_components.p, {\n          children: \"Response metadata\"\n        }), \"\\n\"]\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(Heading, {\n      level: \"3\",\n      id: \"openai-pricing-breakdown\",\n      children: \"OpenAI pricing breakdown\"\n    }), \"\\n\", _jsxs(_components.p, {\n      children: [\"GPT-trainer supports a variety of OpenAI LLMs, as well as different versions of the same LLM with custom token limit cutoffs.\\nTo help you budget for your usage, we provide a summary based on our \", _jsx(_components.strong, {\n        children: \"default\"\n      }), \" split of reserved input vs. output tokens. Please refer\\nto the following table (all $ are in USD). Please note that this generally represents an upper limit since not all input / output tokens\\nin the reservation window are used every single LLM query.\"]\n    }), \"\\n\", _jsxs(_components.table, {\n      children: [_jsx(_components.thead, {\n        children: _jsxs(_components.tr, {\n          children: [_jsx(_components.th, {\n            children: \"Model\"\n          }), _jsx(_components.th, {\n            children: \"Reserved for Input\"\n          }), _jsx(_components.th, {\n            children: \"Reserved for Output\"\n          }), _jsx(_components.th, {\n            children: \"Cost / Input Token\"\n          }), _jsx(_components.th, {\n            children: \"Cost / Output Token\"\n          }), _jsx(_components.th, {\n            children: \"Total Cost per Query\"\n          })]\n        })\n      }), _jsxs(_components.tbody, {\n        children: [_jsxs(_components.tr, {\n          children: [_jsx(_components.td, {\n            children: \"GPT-3.5\"\n          }), _jsx(_components.td, {\n            children: \"2800\"\n          }), _jsx(_components.td, {\n            children: \"1200\"\n          }), _jsx(_components.td, {\n            children: \"0.0000005\"\n          }), _jsx(_components.td, {\n            children: \"0.0000015\"\n          }), _jsx(_components.td, {\n            children: \"0.0032\"\n          })]\n        }), _jsxs(_components.tr, {\n          children: [_jsx(_components.td, {\n            children: \"GPT-3.5-16k\"\n          }), _jsx(_components.td, {\n            children: \"13600\"\n          }), _jsx(_components.td, {\n            children: \"2400\"\n          }), _jsx(_components.td, {\n            children: \"0.0000005\"\n          }), _jsx(_components.td, {\n            children: \"0.0000015\"\n          }), _jsx(_components.td, {\n            children: \"0.0104\"\n          })]\n        }), _jsxs(_components.tr, {\n          children: [_jsx(_components.td, {\n            children: \"GPT-4o-mini-1k\"\n          }), _jsx(_components.td, {\n            children: \"800\"\n          }), _jsx(_components.td, {\n            children: \"200\"\n          }), _jsx(_components.td, {\n            children: \"0.00000015\"\n          }), _jsx(_components.td, {\n            children: \"0.0000006\"\n          }), _jsx(_components.td, {\n            children: \"0.00024\"\n          })]\n        }), _jsxs(_components.tr, {\n          children: [_jsx(_components.td, {\n            children: \"GPT-4o-mini-2k\"\n          }), _jsx(_components.td, {\n            children: \"1600\"\n          }), _jsx(_components.td, {\n            children: \"400\"\n          }), _jsx(_components.td, {\n            children: \"0.00000015\"\n          }), _jsx(_components.td, {\n            children: \"0.0000006\"\n          }), _jsx(_components.td, {\n            children: \"0.00048\"\n          })]\n        }), _jsxs(_components.tr, {\n          children: [_jsx(_components.td, {\n            children: \"GPT-4o-mini-4k\"\n          }), _jsx(_components.td, {\n            children: \"2800\"\n          }), _jsx(_components.td, {\n            children: \"1200\"\n          }), _jsx(_components.td, {\n            children: \"0.00000015\"\n          }), _jsx(_components.td, {\n            children: \"0.0000006\"\n          }), _jsx(_components.td, {\n            children: \"0.00114\"\n          })]\n        }), _jsxs(_components.tr, {\n          children: [_jsx(_components.td, {\n            children: \"GPT-4o-mini-8k\"\n          }), _jsx(_components.td, {\n            children: \"5600\"\n          }), _jsx(_components.td, {\n            children: \"2400\"\n          }), _jsx(_components.td, {\n            children: \"0.00000015\"\n          }), _jsx(_components.td, {\n            children: \"0.0000006\"\n          }), _jsx(_components.td, {\n            children: \"0.00228\"\n          })]\n        }), _jsxs(_components.tr, {\n          children: [_jsx(_components.td, {\n            children: \"GPT-4o-mini-16k\"\n          }), _jsx(_components.td, {\n            children: \"12800\"\n          }), _jsx(_components.td, {\n            children: \"3200\"\n          }), _jsx(_components.td, {\n            children: \"0.00000015\"\n          }), _jsx(_components.td, {\n            children: \"0.0000006\"\n          }), _jsx(_components.td, {\n            children: \"0.00384\"\n          })]\n        }), _jsxs(_components.tr, {\n          children: [_jsx(_components.td, {\n            children: \"GPT-4o-mini-32k\"\n          }), _jsx(_components.td, {\n            children: \"28000\"\n          }), _jsx(_components.td, {\n            children: \"4000\"\n          }), _jsx(_components.td, {\n            children: \"0.00000015\"\n          }), _jsx(_components.td, {\n            children: \"0.0000006\"\n          }), _jsx(_components.td, {\n            children: \"0.0066\"\n          })]\n        }), _jsxs(_components.tr, {\n          children: [_jsx(_components.td, {\n            children: \"GPT-4o-mini-64k\"\n          }), _jsx(_components.td, {\n            children: \"60000\"\n          }), _jsx(_components.td, {\n            children: \"4000\"\n          }), _jsx(_components.td, {\n            children: \"0.00000015\"\n          }), _jsx(_components.td, {\n            children: \"0.0000006\"\n          }), _jsx(_components.td, {\n            children: \"0.0114\"\n          })]\n        }), _jsxs(_components.tr, {\n          children: [_jsx(_components.td, {\n            children: \"GPT-4o-1k\"\n          }), _jsx(_components.td, {\n            children: \"800\"\n          }), _jsx(_components.td, {\n            children: \"200\"\n          }), _jsx(_components.td, {\n            children: \"0.0000025\"\n          }), _jsx(_components.td, {\n            children: \"0.00001\"\n          }), _jsx(_components.td, {\n            children: \"0.004\"\n          })]\n        }), _jsxs(_components.tr, {\n          children: [_jsx(_components.td, {\n            children: \"GPT-4o-2k\"\n          }), _jsx(_components.td, {\n            children: \"1600\"\n          }), _jsx(_components.td, {\n            children: \"400\"\n          }), _jsx(_components.td, {\n            children: \"0.0000025\"\n          }), _jsx(_components.td, {\n            children: \"0.00001\"\n          }), _jsx(_components.td, {\n            children: \"0.008\"\n          })]\n        }), _jsxs(_components.tr, {\n          children: [_jsx(_components.td, {\n            children: \"GPT-4o-4k\"\n          }), _jsx(_components.td, {\n            children: \"2800\"\n          }), _jsx(_components.td, {\n            children: \"1200\"\n          }), _jsx(_components.td, {\n            children: \"0.0000025\"\n          }), _jsx(_components.td, {\n            children: \"0.00001\"\n          }), _jsx(_components.td, {\n            children: \"0.019\"\n          })]\n        }), _jsxs(_components.tr, {\n          children: [_jsx(_components.td, {\n            children: \"GPT-4o-8k\"\n 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}), _jsx(_components.td, {\n            children: \"28000\"\n          }), _jsx(_components.td, {\n            children: \"4000\"\n          }), _jsx(_components.td, {\n            children: \"0.0000025\"\n          }), _jsx(_components.td, {\n            children: \"0.00001\"\n          }), _jsx(_components.td, {\n            children: \"0.11\"\n          })]\n        }), _jsxs(_components.tr, {\n          children: [_jsx(_components.td, {\n            children: \"GPT-4o-64k\"\n          }), _jsx(_components.td, {\n            children: \"60000\"\n          }), _jsx(_components.td, {\n            children: \"4000\"\n          }), _jsx(_components.td, {\n            children: \"0.0000025\"\n          }), _jsx(_components.td, {\n            children: \"0.00001\"\n          }), _jsx(_components.td, {\n            children: \"0.19\"\n          })]\n        }), _jsxs(_components.tr, {\n          children: [_jsx(_components.td, {\n            children: \"GPT-4-1106-1k\"\n          }), _jsx(_components.td, {\n            children: \"800\"\n          }), _jsx(_components.td, {\n            children: \"200\"\n          }), _jsx(_components.td, {\n            children: \"0.00001\"\n          }), _jsx(_components.td, {\n            children: \"0.00003\"\n          }), _jsx(_components.td, {\n            children: \"0.014\"\n          })]\n        }), _jsxs(_components.tr, {\n          children: [_jsx(_components.td, {\n            children: \"GPT-4-1106-2k\"\n          }), _jsx(_components.td, {\n            children: \"1600\"\n          }), _jsx(_components.td, {\n            children: \"400\"\n          }), _jsx(_components.td, {\n            children: \"0.00001\"\n          }), _jsx(_components.td, {\n            children: \"0.00003\"\n          }), _jsx(_components.td, {\n            children: \"0.028\"\n          })]\n        }), _jsxs(_components.tr, {\n          children: [_jsx(_components.td, {\n            children: \"GPT-4-1106-4k\"\n          }), _jsx(_components.td, {\n            children: \"2800\"\n          }), _jsx(_components.td, {\n            children: \"1200\"\n          }), _jsx(_components.td, {\n            children: \"0.00001\"\n          }), _jsx(_components.td, {\n            children: \"0.00003\"\n          }), _jsx(_components.td, {\n            children: \"0.064\"\n          })]\n        }), _jsxs(_components.tr, {\n          children: [_jsx(_components.td, {\n            children: \"GPT-4-0125-8k\"\n          }), _jsx(_components.td, {\n            children: \"5600\"\n          }), _jsx(_components.td, {\n            children: \"2400\"\n          }), _jsx(_components.td, {\n            children: \"0.00001\"\n          }), _jsx(_components.td, {\n            children: \"0.00003\"\n          }), _jsx(_components.td, {\n            children: \"0.128\"\n          })]\n        }), _jsxs(_components.tr, {\n          children: [_jsx(_components.td, {\n            children: \"GPT-4-1106-16k\"\n          }), _jsx(_components.td, {\n            children: \"12800\"\n          }), _jsx(_components.td, {\n            children: \"3200\"\n          }), _jsx(_components.td, {\n            children: \"0.00001\"\n          }), _jsx(_components.td, {\n            children: \"0.00003\"\n          }), _jsx(_components.td, {\n            children: \"0.224\"\n          })]\n        }), _jsxs(_components.tr, {\n          children: [_jsx(_components.td, {\n            children: \"GPT-4-1106-32k\"\n          }), _jsx(_components.td, {\n            children: \"28000\"\n          }), _jsx(_components.td, {\n            children: \"4000\"\n          }), _jsx(_components.td, {\n            children: \"0.00001\"\n          }), _jsx(_components.td, {\n            children: \"0.00003\"\n          }), _jsx(_components.td, {\n            children: \"0.4\"\n          })]\n        }), _jsxs(_components.tr, {\n          children: [_jsx(_components.td, {\n            children: \"GPT-4-1106-64k\"\n          }), _jsx(_components.td, {\n            children: \"60000\"\n          }), _jsx(_components.td, {\n            children: \"4000\"\n          }), _jsx(_components.td, {\n            children: \"0.00001\"\n          }), _jsx(_components.td, {\n            children: \"0.00003\"\n          }), _jsx(_components.td, {\n            children: \"0.72\"\n          })]\n        })]\n      })]\n    }), \"\\n\", _jsx(Heading, {\n      level: \"2\",\n      id: \"byok-for-white-label-commercial-partners\",\n      children: \"BYOK for white-label commercial partners\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"In addition to the costs for MC expenditures during LLM queries, you also need to pay to run our AI multi-agent framework using your\\nown API key. This is independent of whether your users have supplied their API key for their personal accounts. Since the official GPT-trainer\\nsubsidizes its users for all costs associated with running the AI framework, your white-label solution must operate with this premise\\nas well.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"There are three separate workflows that require your own API key to cover your user’s AI expenditures. The conditions under which they apply\\nare listed below.\"\n    }), \"\\n\", _jsxs(_components.ol, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"AI Agent intent generation\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsxs(_components.li, {\n        children: [\"\\n\", _jsx(_components.p, {\n          children: \"Applicable if two or more AI Agents are connected\"\n        }), \"\\n\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"\\n\", _jsx(_components.p, {\n          children: \"Charged whenever a new user-facing AI Agent goes live or an existing one is edited\"\n        }), \"\\n\"]\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.ol, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Query intent classification\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsxs(_components.li, {\n        children: [\"\\n\", _jsx(_components.p, {\n          children: \"Applicable if two or more user-facing Agents are connected\"\n        }), \"\\n\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"\\n\", _jsx(_components.p, {\n          children: \"Charged on a per query basis\"\n        }), \"\\n\"]\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.ol, {\n      children: [\"\\n\", _jsx(_components.li, {\n        children: \"Variable extraction\"\n      }), \"\\n\"]\n    }), \"\\n\", _jsxs(_components.ul, {\n      children: [\"\\n\", _jsxs(_components.li, {\n        children: [\"\\n\", _jsx(_components.p, {\n          children: \"Applicable if AI Agent has one or more variables set up\"\n        }), \"\\n\"]\n      }), \"\\n\", _jsxs(_components.li, {\n        children: [\"\\n\", _jsx(_components.p, {\n          children: \"Charged on a per query basis\"\n        }), \"\\n\"]\n      }), \"\\n\"]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"The estimated costs associated with each workflow is as follows:\"\n    }), \"\\n\", _jsxs(_components.table, {\n      children: [_jsx(_components.thead, {\n        children: _jsxs(_components.tr, {\n          children: [_jsx(_components.th, {\n            children: \"Workflow\"\n          }), _jsx(_components.th, {\n            children: \"Average Estimated Input\"\n          }), _jsx(_components.th, {\n            children: \"Average Estimated Output\"\n          }), _jsx(_components.th, {\n            children: \"Cost / Input Token\"\n          }), _jsx(_components.th, {\n            children: \"Cost / Output Token\"\n          }), _jsx(_components.th, {\n            children: \"Estimated Cost per Run\"\n          })]\n        })\n      }), _jsxs(_components.tbody, {\n        children: [_jsxs(_components.tr, {\n          children: [_jsx(_components.td, {\n            children: \"AI Agent intent generation (gpt-4-1106-preview)\"\n          }), _jsx(_components.td, {\n            children: \"600\"\n          }), _jsx(_components.td, {\n            children: \"450\"\n          }), _jsx(_components.td, {\n            children: \"0.00001\"\n          }), _jsx(_components.td, {\n            children: \"0.00003\"\n          }), _jsx(_components.td, {\n            children: \"0.0011\"\n          })]\n        }), _jsxs(_components.tr, {\n          children: [_jsx(_components.td, {\n            children: \"Query intent classification (gpt-3.5-turbo-1106)\"\n          }), _jsx(_components.td, {\n            children: \"1000\"\n          }), _jsx(_components.td, {\n            children: \"50\"\n          }), _jsx(_components.td, {\n            children: \"0.000001\"\n          }), _jsx(_components.td, {\n            children: \"0.000002\"\n          }), _jsx(_components.td, {\n            children: \"0.0195\"\n          })]\n        }), _jsxs(_components.tr, {\n          children: [_jsx(_components.td, {\n            children: \"Variables extraction (gpt-3.5-turbo-1106)\"\n          }), _jsx(_components.td, {\n            children: \"1000\"\n          }), _jsx(_components.td, {\n            children: \"100\"\n          }), _jsx(_components.td, {\n            children: \"0.000001\"\n          }), _jsx(_components.td, {\n            children: \"0.000002\"\n          }), _jsx(_components.td, {\n            children: \"0.0012\"\n          })]\n        })]\n      })]\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"All costs from the above workflow will be charged directly against your own API key. This cost cannot be transferred onto your clients.\"\n    }), \"\\n\", _jsx(_components.p, {\n      children: \"There may be more AI features in the future that require using your own API key. 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