{"id":7064,"date":"2026-01-08T18:09:04","date_gmt":"2026-01-08T18:09:04","guid":{"rendered":"https:\/\/www.prolimehost.com\/blogs\/?p=7064"},"modified":"2026-01-08T18:30:52","modified_gmt":"2026-01-08T18:30:52","slug":"idle-gpus-are-a-finance-problem-the-true-cost-of-underfed-accelerators","status":"publish","type":"post","link":"https:\/\/www.prolimehost.com\/blogs\/idle-gpus-are-a-finance-problem-the-true-cost-of-underfed-accelerators\/","title":{"rendered":"Idle GPUs Are a Finance Problem: The True Cost of Underfed Accelerators"},"content":{"rendered":"\n<figure class=\"wp-block-image size-large\"><img decoding=\"async\" width=\"1024\" height=\"683\" src=\"https:\/\/www.prolimehost.com\/wp-content\/uploads\/sites\/4\/idle-gpu-1024x683.png\" alt=\"idle gpu\" class=\"wp-image-7066\" srcset=\"https:\/\/www.prolimehost.com\/blogs\/wp-content\/uploads\/sites\/4\/idle-gpu-1024x683.png 1024w, https:\/\/www.prolimehost.com\/blogs\/wp-content\/uploads\/sites\/4\/idle-gpu-300x200.png 300w, https:\/\/www.prolimehost.com\/blogs\/wp-content\/uploads\/sites\/4\/idle-gpu-512x341.png 512w, https:\/\/www.prolimehost.com\/blogs\/wp-content\/uploads\/sites\/4\/idle-gpu-920x613.png 920w, https:\/\/www.prolimehost.com\/blogs\/wp-content\/uploads\/sites\/4\/idle-gpu.png 1536w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n\n\n\n<p>GPU servers are approved as strategic investments. They\u2019re justified with promises of faster model training, accelerated analytics, real-time inference, and competitive advantage. From a finance perspective, they represent serious capital outlay with equally serious expectations for return.<\/p>\n\n\n\n<p>Yet many organizations are discovering an uncomfortable truth: a GPU can be fully paid for and still spend much of its life waiting.<\/p>\n\n\n\n<p>When that happens, the issue isn\u2019t technical. It\u2019s financial.<\/p>\n\n\n\n<p>Idle GPUs represent idle capital. They quietly erode ROI, delay outcomes, and turn approved investment into sunk cost, even as monthly invoices continue to arrive on schedule.<\/p>\n\n\n\n<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_80 counter-hierarchy ez-toc-counter ez-toc-grey ez-toc-container-direction\">\n<div class=\"ez-toc-title-container\">\n<p class=\"ez-toc-title\" style=\"cursor:inherit\">Table of Contents<\/p>\n<span class=\"ez-toc-title-toggle\"><a href=\"#\" class=\"ez-toc-pull-right ez-toc-btn ez-toc-btn-xs ez-toc-btn-default ez-toc-toggle\" aria-label=\"Toggle Table of Content\"><span class=\"ez-toc-js-icon-con\"><span class=\"\"><span class=\"eztoc-hide\" style=\"display:none;\">Toggle<\/span><span class=\"ez-toc-icon-toggle-span\"><svg style=\"fill: #999;color:#999\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" class=\"list-377408\" width=\"20px\" height=\"20px\" viewBox=\"0 0 24 24\" fill=\"none\"><path d=\"M6 6H4v2h2V6zm14 0H8v2h12V6zM4 11h2v2H4v-2zm16 0H8v2h12v-2zM4 16h2v2H4v-2zm16 0H8v2h12v-2z\" fill=\"currentColor\"><\/path><\/svg><svg style=\"fill: #999;color:#999\" class=\"arrow-unsorted-368013\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"10px\" height=\"10px\" viewBox=\"0 0 24 24\" version=\"1.2\" baseProfile=\"tiny\"><path d=\"M18.2 9.3l-6.2-6.3-6.2 6.3c-.2.2-.3.4-.3.7s.1.5.3.7c.2.2.4.3.7.3h11c.3 0 .5-.1.7-.3.2-.2.3-.5.3-.7s-.1-.5-.3-.7zM5.8 14.7l6.2 6.3 6.2-6.3c.2-.2.3-.5.3-.7s-.1-.5-.3-.7c-.2-.2-.4-.3-.7-.3h-11c-.3 0-.5.1-.7.3-.2.2-.3.5-.3.7s.1.5.3.7z\"\/><\/svg><\/span><\/span><\/span><\/a><\/span><\/div>\n<nav><ul class='ez-toc-list ez-toc-list-level-1 ' ><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"https:\/\/www.prolimehost.com\/blogs\/idle-gpus-are-a-finance-problem-the-true-cost-of-underfed-accelerators\/#GPU_Spend_Is_Easy_to_See_GPU_Output_Is_Not\" >GPU Spend Is Easy to See. GPU Output Is Not.<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/www.prolimehost.com\/blogs\/idle-gpus-are-a-finance-problem-the-true-cost-of-underfed-accelerators\/#What_%E2%80%9CUnderfed%E2%80%9D_GPUs_Really_Mean_in_Financial_Terms\" >What \u201cUnderfed\u201d GPUs Really Mean in Financial Terms<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/www.prolimehost.com\/blogs\/idle-gpus-are-a-finance-problem-the-true-cost-of-underfed-accelerators\/#Idle_Time_Is_More_Than_Lost_Performance_Its_Lost_Opportunity\" >Idle Time Is More Than Lost Performance. It\u2019s Lost Opportunity<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/www.prolimehost.com\/blogs\/idle-gpus-are-a-finance-problem-the-true-cost-of-underfed-accelerators\/#Why_Cloud_GPU_Inefficiency_Is_So_Hard_to_Measure\" >Why Cloud GPU Inefficiency Is So Hard to Measure<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/www.prolimehost.com\/blogs\/idle-gpus-are-a-finance-problem-the-true-cost-of-underfed-accelerators\/#Dedicated_Infrastructure_Turns_GPUs_Back_Into_Financial_Assets\" >Dedicated Infrastructure Turns GPUs Back Into Financial Assets<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"https:\/\/www.prolimehost.com\/blogs\/idle-gpus-are-a-finance-problem-the-true-cost-of-underfed-accelerators\/#The_Shift_Finance_Leaders_Are_Making\" >The Shift Finance Leaders Are Making<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-7\" href=\"https:\/\/www.prolimehost.com\/blogs\/idle-gpus-are-a-finance-problem-the-true-cost-of-underfed-accelerators\/#Turning_GPU_Spend_Into_Predictable_ROI\" >Turning GPU Spend Into Predictable ROI<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-8\" href=\"https:\/\/www.prolimehost.com\/blogs\/idle-gpus-are-a-finance-problem-the-true-cost-of-underfed-accelerators\/#Finance_Takeaway\" >Finance Takeaway<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-9\" href=\"https:\/\/www.prolimehost.com\/blogs\/idle-gpus-are-a-finance-problem-the-true-cost-of-underfed-accelerators\/#Board_Audit_Committee_Takeaway\" >Board &amp; Audit Committee Takeaway<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-10\" href=\"https:\/\/www.prolimehost.com\/blogs\/idle-gpus-are-a-finance-problem-the-true-cost-of-underfed-accelerators\/#The_Audit_Lens_GPU_Spend_Risk_and_Accountability\" >The Audit Lens: GPU Spend, Risk, and Accountability<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-11\" href=\"https:\/\/www.prolimehost.com\/blogs\/idle-gpus-are-a-finance-problem-the-true-cost-of-underfed-accelerators\/#Frequently_Asked_Questions\" >Frequently Asked Questions<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-12\" href=\"https:\/\/www.prolimehost.com\/blogs\/idle-gpus-are-a-finance-problem-the-true-cost-of-underfed-accelerators\/#What_This_Means_for_CFOs_in_2026\" >What This Means for CFOs in 2026<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-13\" href=\"https:\/\/www.prolimehost.com\/blogs\/idle-gpus-are-a-finance-problem-the-true-cost-of-underfed-accelerators\/#Ready_to_Evaluate_Your_GPU_ROI\" >Ready to Evaluate Your GPU ROI?<\/a><\/li><\/ul><\/li><\/ul><\/nav><\/div>\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"GPU_Spend_Is_Easy_to_See_GPU_Output_Is_Not\"><\/span>GPU Spend Is Easy to See. GPU Output Is Not.<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>Most GPU purchases are approved with a clear business outcome in mind: faster time to insight, shorter development cycles, or increased throughput. Finance teams can see exactly what those accelerators cost, whether they\u2019re leased, depreciated, or billed monthly through the cloud.<\/p>\n\n\n\n<p>What\u2019s far less visible is how productive those GPUs actually are.<\/p>\n\n\n\n<p>Utilization metrics tend to live inside engineering dashboards, not financial reports. Storage stalls, network congestion, and performance variability rarely show up as line items. The result is a growing gap between what finance pays for and what the business actually receives in output.<\/p>\n\n\n\n<p>A GPU running at partial utilization still costs full price. From a financial standpoint, that gap is pure inefficiency.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"What_%E2%80%9CUnderfed%E2%80%9D_GPUs_Really_Mean_in_Financial_Terms\"><\/span>What \u201cUnderfed\u201d GPUs Really Mean in Financial Terms<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>When engineers describe underfed GPUs, they\u2019re usually talking about bottlenecks. For finance leaders, those bottlenecks translate directly into wasted spend.<\/p>\n\n\n\n<p>Accelerators often sit idle not because demand is low, but because the surrounding infrastructure can\u2019t keep up. Slow or shared storage delays data delivery. Network contention stalls training pipelines. Virtualized environments introduce unpredictable performance. Cloud throttling obscures where time is actually being lost.<\/p>\n\n\n\n<p>In every case, the GPU waits. And while it waits, finance continues paying for capacity that isn\u2019t producing value.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Idle_Time_Is_More_Than_Lost_Performance_Its_Lost_Opportunity\"><\/span>Idle Time Is More Than Lost Performance. It\u2019s Lost Opportunity<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>The real cost of idle GPUs extends well beyond technical inefficiency. Delayed training cycles slow experimentation. Inference backlogs push results further from decision-makers. Product launches slip because infrastructure \u201cshould have been fast enough.\u201d<\/p>\n\n\n\n<p>Each delay compounds. Revenue opportunities move. Competitive advantages narrow. Forecasts become harder to defend.<\/p>\n\n\n\n<p>From a finance perspective, idle GPU time represents both direct cost and opportunity cost. The organization pays for the accelerator, then pays again for the time lost while it sits underutilized.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Why_Cloud_GPU_Inefficiency_Is_So_Hard_to_Measure\"><\/span>Why Cloud GPU Inefficiency Is So Hard to Measure<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>Cloud platforms excel at reporting consumption. They tell you how long a GPU was allocated, how much storage was used, and how much data moved. What they don\u2019t show is how productive that time actually was.<\/p>\n\n\n\n<p>Costs are fragmented across services, regions, and usage categories. Performance variability hides behind abstraction layers. Finance sees spend, but not throughput. Allocation does not equal output, and invoices offer little insight into where productivity was lost.<\/p>\n\n\n\n<p>That disconnect makes it difficult for finance teams to answer a simple but critical question: are we getting the GPU performance we\u2019re paying for?<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Dedicated_Infrastructure_Turns_GPUs_Back_Into_Financial_Assets\"><\/span>Dedicated Infrastructure Turns GPUs Back Into Financial Assets<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>Dedicated GPU servers change this equation by removing uncertainty. Storage performance is consistent. Network throughput is predictable. GPUs aren\u2019t shared, throttled, or impacted by neighboring workloads.<\/p>\n\n\n\n<p>Just as important, costs are fixed and forecastable. Finance teams can tie infrastructure spend directly to completed workloads, measurable throughput, and defined outcomes. Utilization becomes something that can be tracked, explained, and improved, not guessed at.<\/p>\n\n\n\n<p>In this model, GPUs stop behaving like variable expenses and start functioning as controlled, accountable assets.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"The_Shift_Finance_Leaders_Are_Making\"><\/span>The Shift Finance Leaders Are Making<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>As AI and GPU investments grow, finance teams are asking more pointed questions. They want to understand not just what accelerators cost, but how effectively they\u2019re being used. They\u2019re looking for clarity around productivity, forecasting, and return, not just availability.<\/p>\n\n\n\n<p>This shift isn\u2019t about rejecting the cloud or chasing raw performance. It\u2019s about governance. When GPU spend becomes material, it demands the same financial discipline as any other major investment.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Turning_GPU_Spend_Into_Predictable_ROI\"><\/span>Turning GPU Spend Into Predictable ROI<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>GPUs don\u2019t generate value simply by existing. They generate value when the infrastructure feeding them is fast, stable, and designed for sustained throughput.<\/p>\n\n\n\n<p>When accelerators are underfed, finance pays twice: once for the hardware and again for the opportunities that never fully materialize.<\/p>\n\n\n\n<p>Organizations that treat GPU infrastructure as a financial system, not just a technical one, are the ones turning AI investment into measurable return.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Finance_Takeaway\"><\/span>Finance Takeaway<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p><strong>If your GPUs were financial assets on a balance sheet, could you clearly explain how much value they produce per month or only how much they cost?<\/strong><\/p>\n\n\n\n<p>If the answer is unclear, the issue likely isn\u2019t the GPUs themselves. It\u2019s the infrastructure and visibility around them. Until productivity is as measurable as spend, GPU investments will continue to underperform expectations.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Board_Audit_Committee_Takeaway\"><\/span>Board &amp; Audit Committee Takeaway<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p><strong>Do we have the governance in place to verify that our AI and GPU investments are delivering predictable, auditable returns or are we approving spend without clear accountability for output?<\/strong><\/p>\n\n\n\n<p>As GPU and AI infrastructure becomes material to financial performance, oversight expectations rise. Boards and audit committees increasingly need assurance that <a href=\"https:\/\/www.prolimehost.com\/blogs\/the-web-hosting-industry-outlook-for-2026-where-real-revenue-and-roi-are-headed\/\" target=\"_blank\" rel=\"noopener\" title=\"The Web Hosting Industry Outlook for 2026: Where Real Revenue and ROI Are Headed\">high-cost accelerators<\/a> are producing measurable value, not just consuming budget.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"The_Audit_Lens_GPU_Spend_Risk_and_Accountability\"><\/span>The Audit Lens: GPU Spend, Risk, and Accountability<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>From an audit and risk perspective, GPU investments introduce a growing control gap. While spend is easy to track, productivity and utilization are often opaque, fragmented across platforms, or owned exclusively by engineering teams. That separation makes it difficult to verify whether high-cost accelerators are delivering the outcomes used to justify their approval. As AI infrastructure becomes a material financial commitment, auditors and oversight committees increasingly expect clearer linkage between capital deployed, workloads completed, and results delivered. Infrastructure that provides consistent performance, measurable utilization, and predictable costs reduces not only financial uncertainty, but governance risk as well.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Frequently_Asked_Questions\"><\/span>Frequently Asked Questions<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p><strong>Why are idle GPUs considered a finance problem and not just an IT issue?<\/strong><br>Because GPUs are capital-intensive assets. When they sit idle due to infrastructure bottlenecks, the organization continues paying for them without receiving proportional output. That gap between spend and productivity is a financial inefficiency, not a technical inconvenience.<\/p>\n\n\n\n<p><strong>What typically causes GPUs to be \u201cunderfed\u201d?<\/strong><br>In most cases, the issue isn\u2019t the GPU itself but the systems around it. Storage that can\u2019t deliver data fast enough, congested networks, shared environments, and performance throttling all force accelerators to wait. Every minute spent waiting reduces the return on the investment.<\/p>\n\n\n\n<p><strong>Can\u2019t cloud platforms automatically solve GPU efficiency issues?<\/strong><br>Cloud platforms excel at resource allocation, but allocation does not equal productivity. While usage is easy to measure, actual throughput and performance consistency are harder to see. From a finance perspective, this makes it difficult to connect GPU spend directly to business outcomes.<\/p>\n\n\n\n<p><strong>How does dedicated GPU infrastructure improve ROI visibility?<\/strong><br>Dedicated environments remove performance variability. When storage, network, and compute resources are fixed and predictable, utilization becomes measurable and repeatable. This allows finance teams to forecast costs accurately and tie spend to completed workloads instead of estimated usage.<\/p>\n\n\n\n<p><strong>Is this about replacing the cloud entirely?<\/strong><br>Not necessarily. Many organizations continue to use cloud platforms strategically. The key shift is recognizing when GPU workloads require predictable throughput and stable performance to justify their cost. In those cases, dedicated infrastructure often provides clearer financial control.<\/p>\n\n\n\n<p><strong>What should finance teams ask when evaluating GPU investments?<\/strong><br>Rather than focusing solely on monthly cost, finance leaders should ask how productive GPUs are, where time is being lost, and whether output can be forecast reliably. These questions help turn <a href=\"https:\/\/www.prolimehost.com\/blogs\/how-gpu-servers-are-transforming-healthcare\/\" target=\"_blank\" rel=\"noopener\" title=\"How GPU Servers Are Transforming Healthcare\">GPU<\/a> spending from a variable risk into a governed investment.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"What_This_Means_for_CFOs_in_2026\"><\/span>What This Means for CFOs in 2026<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p><strong>In 2026, CFOs won\u2019t be judged on how much AI or GPU capacity they approved, but on how well that spend was governed, measured, and converted into predictable financial return.<\/strong><\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Ready_to_Evaluate_Your_GPU_ROI\"><\/span>Ready to Evaluate Your GPU ROI?<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>If your finance team can see GPU spend but not GPU output, it may be time to reassess the infrastructure supporting those accelerators.<\/p>\n\n\n\n<p>At <a href=\"https:\/\/www.prolimehost.com\/blogs\/why-prolimehost\/\" target=\"_blank\" rel=\"noopener\" title=\"Why ProlimeHost?\">ProlimeHost<\/a>, we help organizations align GPU performance with financial outcomes through dedicated, high-performance infrastructure built for predictable ROI.<\/p>\n\n\n\n<p>\ud83d\udcde <strong>877-477-9454<\/strong><br>\ud83c\udf10 <strong><a>www.prolimehost.com<\/a><\/strong><\/p>\n","protected":false},"excerpt":{"rendered":"GPU servers are approved as strategic investments. They\u2019re justified with promises of faster model training, accelerated analytics, real-time&hellip;","protected":false},"author":3,"featured_media":7066,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"csco_display_header_overlay":false,"csco_singular_sidebar":"","csco_page_header_type":"","footnotes":""},"categories":[257,11,220,1,265,13,279,10],"tags":[43,24,107,198,139],"class_list":{"0":"post-7064","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-ai-servers","8":"category-around-the-web","9":"category-dedicated-server","10":"category-geneal","11":"category-gpu-servers","12":"category-news-updates","13":"category-prolimehost","14":"category-tutorials-tips","15":"tag-dedicated-server","16":"tag-dedicated-servers","17":"tag-dedicated-servers-usa","18":"tag-gpu-servers","19":"tag-prolimehost","20":"cs-entry"},"aioseo_notices":[],"_links":{"self":[{"href":"https:\/\/www.prolimehost.com\/blogs\/wp-json\/wp\/v2\/posts\/7064","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.prolimehost.com\/blogs\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.prolimehost.com\/blogs\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.prolimehost.com\/blogs\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/www.prolimehost.com\/blogs\/wp-json\/wp\/v2\/comments?post=7064"}],"version-history":[{"count":0,"href":"https:\/\/www.prolimehost.com\/blogs\/wp-json\/wp\/v2\/posts\/7064\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.prolimehost.com\/blogs\/wp-json\/wp\/v2\/media\/7066"}],"wp:attachment":[{"href":"https:\/\/www.prolimehost.com\/blogs\/wp-json\/wp\/v2\/media?parent=7064"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.prolimehost.com\/blogs\/wp-json\/wp\/v2\/categories?post=7064"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.prolimehost.com\/blogs\/wp-json\/wp\/v2\/tags?post=7064"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}