{"id":1314,"date":"2026-06-26T15:58:15","date_gmt":"2026-06-26T15:58:15","guid":{"rendered":"https:\/\/convly.ai\/self-hosting-vs-api-calculator\/"},"modified":"2026-07-12T10:38:22","modified_gmt":"2026-07-12T10:38:22","slug":"self-hosting-vs-api-calculator","status":"publish","type":"page","link":"https:\/\/convly.ai\/pt\/self-hosting-vs-api-calculator\/","title":{"rendered":"Auto-hospedagem versus API: Calculadora de Ponto de Equil\u00edbrio de Custos para LLMs"},"content":{"rendered":"<p>Should you buy a GPU and self-host an open LLM, or just keep paying per token for an API? It comes down to volume. Enter your monthly usage and your hardware, and this calculator shows the break-even point \u2014 the moment owning the GPU becomes cheaper than the API bill.<\/p>\n<div class=\"shc\"id=\"shc\">\n  <div class=\"shc-grid\">\n    <div class=\"shc-col\">\n      <h4>Your usage<\/h4>\n      <label>Input tokens \/ month (millions)<input type=\"number\"id=\"shc-in\"value=\"50\"min=\"0\"step=\"1\"><\/label>\n      <label>Output tokens \/ month (millions)<input type=\"number\"id=\"shc-out\"value=\"10\"min=\"0\"step=\"1\"><\/label>\n      <label>API you'd otherwise pay for\n        <select id=\"shc-model\">\n          <option value=\"0\"data-in=\"10\"data-out=\"50\">Claude Fable 5 ($10\/$50)<\/option><option value=\"1\"data-in=\"1\"data-out=\"5\">Claude Haiku 4.5 ($1\/$5)<\/option><option value=\"2\"data-in=\"5\"data-out=\"25\">Claude Opus 4.8 ($5\/$25)<\/option><option value=\"3\"data-in=\"5\"data-out=\"25\">Claude Opus 5 ($5\/$25)<\/option><option value=\"4\"data-in=\"3\"data-out=\"15\">Claude Sonnet 4.6 ($3\/$15)<\/option><option value=\"5\"data-in=\"2\"data-out=\"10\">Claude Sonnet 5 ($2\/$10)<\/option><option value=\"6\"data-in=\"0.5\"data-out=\"2.15\">DeepSeek R1 ($0.5\/$2.15)<\/option><option value=\"7\"data-in=\"0.8\"data-out=\"0.8\">DeepSeek R1 Distill Llama 70B ($0.8\/$0.8)<\/option><option value=\"8\"data-in=\"0.14\"data-out=\"0.28\">DeepSeek V4-Flash ($0.14\/$0.28)<\/option><option value=\"9\"data-in=\"0.435\"data-out=\"0.87\">DeepSeek V4-Pro ($0.435\/$0.87)<\/option><option value=\"10\"data-in=\"1.25\"data-out=\"10\">Gemini 2.5 Pro ($1.25\/$10)<\/option><option value=\"11\"data-in=\"2\"data-out=\"12\">Gemini 3.1 Pro ($2\/$12)<\/option><option value=\"12\"data-in=\"1.5\"data-out=\"9\">Gemini 3.5 Flash ($1.5\/$9)<\/option><option value=\"13\"data-in=\"1.5\"data-out=\"7.5\">Gemini 3.6 Flash ($1.5\/$7.5)<\/option><option value=\"14\"data-in=\"0.05\"data-out=\"0.15\">Gemma 3 12B ($0.05\/$0.15)<\/option><option value=\"15\"data-in=\"0.08\"data-out=\"0.16\">Gemma 3 27B ($0.08\/$0.16)<\/option><option value=\"16\"data-in=\"0.05\"data-out=\"0.1\">Gemma 3 4B ($0.05\/$0.1)<\/option><option value=\"17\"data-in=\"1.4\"data-out=\"4.4\">GLM 5.2 ($1.4\/$4.4)<\/option><option value=\"18\"data-in=\"5\"data-out=\"30\">GPT-5.5 ($5\/$30)<\/option><option value=\"19\"data-in=\"5\"data-out=\"30\">GPT-5.6 Sol ($5\/$30)<\/option><option value=\"20\"data-in=\"3\"data-out=\"15\">Grok 4 ($3\/$15)<\/option><option value=\"21\"data-in=\"0.6\"data-out=\"2.5\">Kimi K2.7 Code ($0.6\/$2.5)<\/option><option value=\"22\"data-in=\"3\"data-out=\"15\">Kimi K3 ($3\/$15)<\/option><option value=\"23\"data-in=\"0.02\"data-out=\"0.03\">Llama 3.1 8B ($0.02\/$0.03)<\/option><option value=\"24\"data-in=\"0.1\"data-out=\"0.32\">Llama 3.3 70B ($0.1\/$0.32)<\/option><option value=\"25\"data-in=\"0.15\"data-out=\"0.6\">Llama 4 Maverick ($0.15\/$0.6)<\/option><option value=\"26\"data-in=\"0.1\"data-out=\"0.3\">Llama 4 Scout ($0.1\/$0.3)<\/option><option value=\"27\"data-in=\"0.02\"data-out=\"0.03\">Mistral 7B ($0.02\/$0.03)<\/option><option value=\"28\"data-in=\"2\"data-out=\"6\">Mistral Large 3 ($2\/$6)<\/option><option value=\"29\"data-in=\"0.02\"data-out=\"0.04\">Mistral NeMo 12B ($0.02\/$0.04)<\/option><option value=\"30\"data-in=\"0.07\"data-out=\"0.14\">Phi-4 ($0.07\/$0.14)<\/option><option value=\"31\"data-in=\"0.12\"data-out=\"0.24\">Qwen3 14B ($0.12\/$0.24)<\/option><option value=\"32\"data-in=\"0.45\"data-out=\"1.8\">Qwen3 235B-A22B ($0.45\/$1.8)<\/option><option value=\"33\"data-in=\"0.12\"data-out=\"0.5\">Qwen3 30B-A3B ($0.12\/$0.5)<\/option><option value=\"34\"data-in=\"0.08\"data-out=\"0.28\">Qwen3 32B ($0.08\/$0.28)<\/option><option value=\"35\"data-in=\"0.04\"data-out=\"0.14\">Qwen3 8B ($0.04\/$0.14)<\/option>          <option value=\"custom\">Custom blended ($\/1M)\u2026<\/option>\n        <\/select>\n      <\/label>\n      <label id=\"shc-blended-wrap\"style=\"display:none\">Custom blended price ($\/1M)<input type=\"number\"id=\"shc-blended\"value=\"0.50\"min=\"0\"step=\"0.01\"><\/label>\n    <\/div>\n    <div class=\"shc-col\">\n      <h4>Your self-host rig<\/h4>\n      <label>GPU\n        <select id=\"shc-gpu\">\n          <option data-p=\"450\"data-w=\"165\">RTX 4060 Ti 16GB \u2014 $450<\/option>\n          <option data-p=\"1800\"data-w=\"450\"selected>RTX 4090 24GB \u2014 $1,800<\/option>\n          <option data-p=\"2200\"data-w=\"575\">RTX 5090 32GB \u2014 $2,200<\/option>\n          <option data-p=\"6800\"data-w=\"300\">RTX 6000 Ada 48GB \u2014 $6,800<\/option>\n          <option data-p=\"18000\"data-w=\"700\">H100 80GB \u2014 $18,000<\/option>\n          <option value=\"custom\">Custom\u2026<\/option>\n        <\/select>\n      <\/label>\n      <label id=\"shc-gpu-custom\"style=\"display:none\">GPU price ($) \/ power (W)\n        <span style=\"display:flex;gap:8px\"><input type=\"number\"id=\"shc-gpu-price\"value=\"1800\"min=\"0\"><input type=\"number\"id=\"shc-gpu-watts\"value=\"450\"min=\"0\"><\/span>\n      <\/label>\n      <label>Amortize GPU over (months)<input type=\"number\"id=\"shc-amort\"value=\"24\"min=\"1\"step=\"1\"><\/label>\n      <label>Electricity ($\/kWh)<input type=\"number\"id=\"shc-kwh\"value=\"0.15\"min=\"0\"step=\"0.01\"><\/label>\n      <label>Hours\/day GPU active<input type=\"number\"id=\"shc-hours\"value=\"8\"min=\"0\"max=\"24\"step=\"1\"><\/label>\n    <\/div>\n  <\/div>\n\n  <div id=\"shc-verdict\"class=\"shc-verdict\"><\/div>\n  <table class=\"shc-table\">\n    <tbody>\n      <tr><td>API cost (your volume)<\/td><td id=\"shc-api\">\u2014<\/td><\/tr>\n      <tr><td>Self-host cost (GPU amortized)<\/td><td id=\"shc-gpuc\">\u2014<\/td><\/tr>\n      <tr><td>Self-host cost (electricity)<\/td><td id=\"shc-elec\">\u2014<\/td><\/tr>\n      <tr class=\"shc-tot\"><td>Self-host total \/ month<\/td><td id=\"shc-self\">\u2014<\/td><\/tr>\n    <\/tbody>\n  <\/table>\n  <p class=\"shc-note\">Self-hosting runs <strong>open-weight models<\/strong> (free weights), so this compares the per-token API bill against owning hardware. It assumes your GPU can keep up with the volume (a single GPU has a tokens\/sec ceiling) and ignores your setup\/maintenance time. Check what a GPU can actually run in our <a href=\"\/llm-vram-calculator\/\">VRAM calculator<\/a>, and current API prices in the <a href=\"\/ai-api-cost-calculator\/\">cost calculator<\/a>.<\/p>\n<\/div>\n<script>(function(){\n  var $=function(id){return document.getElementById(id);};\n  function val(id){return parseFloat($(id).value)||0;}\n  function gpu(){var s=$('shc-gpu');if(s.value==='custom')return[val('shc-gpu-price'),val('shc-gpu-watts')];var o=s.options[s.selectedIndex];return[parseFloat(o.dataset.p),parseFloat(o.dataset.w)];}\n  function api(){var s=$('shc-model');if(s.value==='custom'){var b=val('shc-blended');return[b,b];}var o=s.options[s.selectedIndex];return[parseFloat(o.dataset.in),parseFloat(o.dataset.out)];}\n  function money(x){return '$'+x.toLocaleString(undefined,{maximumFractionDigits:2});}\n  function render(){\n    var inM=val('shc-in'),outM=val('shc-out'),p=api(),g=gpu();\n    var apiM=inM*p[0]+outM*p[1];\n    var gpuM=g[0]\/Math.max(1,val('shc-amort'));\n    var elecM=(g[1]\/1000)*val('shc-hours')*30*val('shc-kwh');\n    var selfM=gpuM+elecM;\n    $('shc-api').textContent=money(apiM)+'\/mo';\n    $('shc-gpuc').textContent=money(gpuM)+'\/mo';\n    $('shc-elec').textContent=money(elecM)+'\/mo';\n    $('shc-self').textContent=money(selfM)+'\/mo';\n    var v=$('shc-verdict'),save=apiM-selfM;\n    var be=apiM>0?(selfM\/apiM)*(inM+outM):0;\n    if(save>0){v.className='shc-verdict shc-win';v.innerHTML='<b>Self-hosting saves you '+money(save)+'\/month<\/b> at this volume.<br><span>Break-even: self-hosting wins above ~<b>'+be.toFixed(1)+'M tokens\/month<\/b> (at your input:output mix).<\/span>';}\n    else{v.className='shc-verdict shc-lose';v.innerHTML='<b>The API is cheaper by '+money(-save)+'\/month<\/b> at this volume.<br><span>You\\'d need ~<b>'+be.toFixed(1)+'M tokens\/month<\/b> before buying this GPU pays off.<\/span>';}\n  }\n  $('shc-model').addEventListener('change',function(){$('shc-blended-wrap').style.display=this.value==='custom'?'':'none';render();});\n  $('shc-gpu').addEventListener('change',function(){$('shc-gpu-custom').style.display=this.value==='custom'?'':'none';render();});\n  ['shc-in','shc-out','shc-blended','shc-gpu-price','shc-gpu-watts','shc-amort','shc-kwh','shc-hours'].forEach(function(id){$(id).addEventListener('input',render);});\n  render();\n})();<\/script>\n\n<p>Remember: self-hosting runs <a href=\"\/models\/\">open-weight models<\/a>, so factor in the quality difference versus a frontier API \u2014 and use our <a href=\"\/llm-vram-calculator\/\">VRAM calculator<\/a> to confirm your GPU can actually run the model you want.<\/p>\n<p><!--geo-block--><\/p>\n<h2>Quick answer: Is it cheaper to self-host an LLM or use an API?<\/h2>\n<p>It depends almost entirely on your sustained monthly token volume. For most users a hosted API is cheaper: below roughly 50 million tokens a month, per-token pricing beats the cost of buying and running a GPU. Self-hosting only pays off at high, steady volume \u2014 typically tens to hundreds of millions of tokens a month feeding agents, batch jobs or a whole team \u2014 where an owned GPU kept busy amortises its upfront cost against effectively unlimited inference. The break-even is a range, not a fixed line: it moves with model size, GPU tier, electricity price and, above all, how heavily you keep the card utilised.<\/p>\n<ul>\n<li>Low volume (under ~50M tokens\/month): the API wins almost every time.<\/li>\n<li>The decision zone (~50M\u2013500M tokens\/month): a genuine toss-up, but only worth self-hosting if you have dedicated engineering time to run it.<\/li>\n<li>High sustained volume (agents, batch or a whole team, 500M+ tokens\/month): a well-utilised owned GPU can cut inference cost by up to ~5x.<\/li>\n<li>A GPU only pays for itself if it is kept busy \u2014 an idle card still costs its full capex and power.<\/li>\n<li>Budget &#8220;flash&#8221; or small-model APIs are so cheap that self-hosting is rarely justified at any volume.<\/li>\n<\/ul>\n<h2>Frequently asked questions<\/h2>\n<h3>When does a local GPU pay for itself?<\/h3>\n<p>A GPU pays for itself once your steady API spend on an equivalent open model consistently exceeds the all-in cost of owning one. A consumer GPU of roughly $2,000\u2013$2,500 spread over three years works out to about $55\u2013$70 a month, plus $30\u2013$60 in power \u2014 so owning one costs roughly $85\u2013$130 a month all-in. If your comparable API spend stays under that, the API is cheaper; once it runs well above it \u2014 on the order of a few hundred dollars a month \u2014 the card recovers its $2,000\u2013$2,500 upfront cost within about a year and saves money after that.<\/p>\n<h3>How much VRAM do I need to run an LLM locally?<\/h3>\n<p>At 4-bit quantisation, budget roughly 0.5\u20130.6 GB of VRAM per billion parameters, plus extra for the KV cache that grows with context length. In practice a 7B model needs about 8 GB, a 13B model 12\u201316 GB, and a 4-bit 70B model roughly 40\u201348 GB. That means a 7\u201313B model fits on a single 8\u201316 GB card, but a 4-bit 70B is too big for any single consumer GPU \u2014 even a 32 GB card falls short, so it takes two 24 GB cards, a 48 GB workstation card, or a smaller, more aggressively quantised model.<\/p>\n<h3>What are the hidden costs of self-hosting an LLM?<\/h3>\n<p>The GPU price is only part of it \u2014 once you add electricity, cooling and especially engineering time, self-hosting typically costs 3\u20135x the raw GPU rental figure. Plan for 10\u201320 hours a month of setup, monitoring and maintenance; that labour, plus the cost of a card sitting idle between jobs, is what sinks most naive break-even estimates.<\/p>\n<h3>Does the model size change the break-even point?<\/h3>\n<p>Yes. Bigger models need more or pricier GPUs, which raises the capex you have to amortise, but they also carry the highest per-token API prices, which pushes the break-even lower in volume terms. A small 7\u201313B model is cheap to self-host but also cheap via API, so APIs usually win; a 70B-plus open model is where a busy owned GPU tends to deliver the largest savings.<\/p>\n<h3>Does electricity make self-hosting too expensive?<\/h3>\n<p>Usually not \u2014 power is a minor line item next to the hardware. A single desktop GPU run flat out draws roughly 400\u2013600 W, which at typical electricity rates works out to only around $30\u2013$60 a month if it runs continuously. The dominant cost of self-hosting is the upfront hardware and the engineering time to operate it, not the electricity.<\/p>\n<h3>Should a small team or startup self-host or use an API?<\/h3>\n<p>Start with an API. Until you have high, predictable volume and someone to run the infrastructure, per-token pricing is cheaper, faster to ship and carries no upfront risk. Self-hosting is worth revisiting only when your monthly bill is large and stable \u2014 typically once you are consistently past tens of millions of tokens a month on a comparable open model.<\/p>\n<p><!--convly-tools--><br \/>\n<style>.ctools-wrap{margin:36px 0 10px;border-top:1px solid #e6e8ef;padding-top:22px}.ctools-h{font-weight:700;font-size:14px;color:#1a1a2e;margin:0 0 14px;text-transform:uppercase;letter-spacing:.05em}.ctools-grid{display:grid;grid-template-columns:repeat(auto-fit,minmax(210px,1fr));gap:12px}.ctool{display:flex;flex-direction:column;gap:6px;padding:15px 16px;border:1px solid #e6e8ef;border-radius:12px;background:#f8f9fb;text-decoration:none!important;transition:transform .15s,box-shadow .15s,border-color .15s}.ctool:hover{border-color:#6d28d9;background:#fff;box-shadow:0 8px 20px -10px rgba(109,40,217,.35);transform:translateY(-2px)}.ctool-i{display:inline-flex;align-items:center;justify-content:center;width:38px;height:38px;border-radius:10px;background:#f1ecfb;color:#6d28d9;margin-bottom:2px}.ctool:hover .ctool-i{background:#6d28d9;color:#fff}.ctool-t{font-weight:700;font-size:14.5px;color:#1a3ba3}.ctool-d{font-size:12.5px;color:#5a6472;line-height:1.4}<\/style><div class=\"ctools-wrap\"><p class=\"ctools-h\">More free tools from Convly<\/p><div class=\"ctools-grid\"><a class=\"ctool\" href=\"\/models\/\"><span class=\"ctool-i\"><svg viewBox=\"0 0 24 24\" width=\"22\" height=\"22\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.8\" stroke-linecap=\"round\" stroke-linejoin=\"round\" aria-hidden=\"true\"><ellipse cx=\"12\" cy=\"5.5\" rx=\"7.5\" ry=\"3\"\/><path d=\"M4.5 5.5v6c0 1.66 3.36 3 7.5 3s7.5-1.34 7.5-3v-6\"\/><path d=\"M4.5 11.5v6c0 1.66 3.36 3 7.5 3s7.5-1.34 7.5-3v-6\"\/><\/svg><\/span><span class=\"ctool-t\">AI Models Database<\/span><span class=\"ctool-d\">30+ LLMs \u2014 specs, pricing &amp; context, side by side.<\/span><\/a><a class=\"ctool\" href=\"\/llm-leaderboard\/\"><span class=\"ctool-i\"><svg viewBox=\"0 0 24 24\" width=\"22\" height=\"22\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.8\" stroke-linecap=\"round\" stroke-linejoin=\"round\" aria-hidden=\"true\"><rect x=\"9.5\" y=\"4\" width=\"5\" height=\"16\" rx=\"1\"\/><rect x=\"3\" y=\"10\" width=\"5\" height=\"10\" rx=\"1\"\/><rect x=\"16\" y=\"13\" width=\"5\" height=\"7\" rx=\"1\"\/><\/svg><\/span><span class=\"ctool-t\">LLM Leaderboard 2026<\/span><span class=\"ctool-d\">Rank every model by intelligence, price &amp; speed.<\/span><\/a><a class=\"ctool\" href=\"\/ai-api-cost-calculator\/\"><span class=\"ctool-i\"><svg viewBox=\"0 0 24 24\" width=\"22\" height=\"22\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.8\" stroke-linecap=\"round\" stroke-linejoin=\"round\" aria-hidden=\"true\"><rect x=\"5\" y=\"3\" width=\"14\" height=\"18\" rx=\"2\"\/><path d=\"M8.5 7h7\"\/><path d=\"M8.5 11.5h.01M12 11.5h.01M15.5 11.5h.01M8.5 15h.01M12 15h.01M15.5 15h.01M8.5 18h.01M12 18h.01M15.5 18h.01\"\/><\/svg><\/span><span class=\"ctool-t\">AI API Cost Calculator<\/span><span class=\"ctool-d\">Compare what each model costs you per month.<\/span><\/a><a class=\"ctool\" href=\"\/llm-vram-calculator\/\"><span class=\"ctool-i\"><svg viewBox=\"0 0 24 24\" width=\"22\" height=\"22\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.8\" stroke-linecap=\"round\" stroke-linejoin=\"round\" aria-hidden=\"true\"><rect x=\"6\" y=\"6\" width=\"12\" height=\"12\" rx=\"2\"\/><rect x=\"9.5\" y=\"9.5\" width=\"5\" height=\"5\" rx=\"1\"\/><path d=\"M9 3v3M15 3v3M9 18v3M15 18v3M3 9h3M3 15h3M18 9h3M18 15h3\"\/><\/svg><\/span><span class=\"ctool-t\">LLM VRAM Calculator<\/span><span class=\"ctool-d\">Can your GPU run that model locally? Find out.<\/span><\/a><a class=\"ctool\" href=\"\/self-hosting-vs-api-calculator\/\"><span class=\"ctool-i\"><svg viewBox=\"0 0 24 24\" width=\"22\" height=\"22\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.8\" stroke-linecap=\"round\" stroke-linejoin=\"round\" aria-hidden=\"true\"><path d=\"M12 4v16\"\/><path d=\"M5 7h14\"\/><path d=\"M5 7l-2.5 6a3 3 0 0 0 5 0L5 7z\"\/><path d=\"M19 7l-2.5 6a3 3 0 0 0 5 0L19 7z\"\/><path d=\"M8.5 20h7\"\/><\/svg><\/span><span class=\"ctool-t\">Self-Hosting vs API<\/span><span class=\"ctool-d\">Buy a GPU or pay per token? See the break-even.<\/span><\/a><a class=\"ctool\" href=\"\/compare-ai-models-and-gpus-2026\/\"><span class=\"ctool-i\"><svg viewBox=\"0 0 24 24\" width=\"22\" height=\"22\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.8\" stroke-linecap=\"round\" stroke-linejoin=\"round\" aria-hidden=\"true\"><rect x=\"3.5\" y=\"3.5\" width=\"12\" height=\"12\" rx=\"2.5\"\/><path d=\"M8.5 20.5h9.5a2.5 2.5 0 0 0 2.5-2.5V8.5\"\/><\/svg><\/span><span class=\"ctool-t\">Compare Models &amp; GPUs<\/span><span class=\"ctool-d\">Every model paired with the GPU that runs it.<\/span><\/a><a class=\"ctool\" href=\"\/ai-benchmarks\/\"><span class=\"ctool-i\"><svg viewBox=\"0 0 24 24\" width=\"22\" height=\"22\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.8\" stroke-linecap=\"round\" stroke-linejoin=\"round\" aria-hidden=\"true\"><path d=\"M4 17a8 8 0 1 1 16 0\"\/><path d=\"M12 17l4.2-4.6\"\/><circle cx=\"12\" cy=\"17\" r=\"1.4\"\/><path d=\"M4 17h2M18 17h2M12 7V5\"\/><\/svg><\/span><span class=\"ctool-t\">AI Benchmarks Explained<\/span><span class=\"ctool-d\">What each benchmark measures, and who leads.<\/span><\/a><a class=\"ctool\" href=\"\/image-to-prompt\/\"><span class=\"ctool-i\"><svg viewBox=\"0 0 24 24\" width=\"22\" height=\"22\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.8\" stroke-linecap=\"round\" stroke-linejoin=\"round\" aria-hidden=\"true\"><rect x=\"3.5\" y=\"4.5\" width=\"17\" height=\"15\" rx=\"2.5\"\/><circle cx=\"9\" cy=\"10\" r=\"1.6\"\/><path d=\"M3.5 16.5l4.7-4.2a1.8 1.8 0 0 1 2.4 0l5.9 5.2\"\/><path d=\"M14.5 14l1.9-1.7a1.8 1.8 0 0 1 2.4 0l1.7 1.5\"\/><\/svg><\/span><span class=\"ctool-t\">Image-to-Prompt<\/span><span class=\"ctool-d\">Turn any image into an editable AI prompt.<\/span><\/a><\/div><\/div><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Should you buy a GPU and self-host an open LLM, or just keep paying per token for an API? It [&hellip;]<\/p>\n","protected":false},"author":0,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"default","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","ast-disable-related-posts":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"footnotes":""},"class_list":["post-1314","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/convly.ai\/pt\/wp-json\/wp\/v2\/pages\/1314","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/convly.ai\/pt\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/convly.ai\/pt\/wp-json\/wp\/v2\/types\/page"}],"replies":[{"embeddable":true,"href":"https:\/\/convly.ai\/pt\/wp-json\/wp\/v2\/comments?post=1314"}],"version-history":[{"count":2,"href":"https:\/\/convly.ai\/pt\/wp-json\/wp\/v2\/pages\/1314\/revisions"}],"predecessor-version":[{"id":1545,"href":"https:\/\/convly.ai\/pt\/wp-json\/wp\/v2\/pages\/1314\/revisions\/1545"}],"wp:attachment":[{"href":"https:\/\/convly.ai\/pt\/wp-json\/wp\/v2\/media?parent=1314"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}