{"id":2111,"date":"2026-07-13T17:04:06","date_gmt":"2026-07-13T17:04:06","guid":{"rendered":"https:\/\/navigotechsolutions.com\/blog\/aws-sagemaker-ai-inference-recommendations-a-guide-for-indian-firms\/"},"modified":"2026-07-13T17:04:08","modified_gmt":"2026-07-13T17:04:08","slug":"aws-sagemaker-ai-inference-recommendations-a-guide-for-indian-firms","status":"publish","type":"post","link":"https:\/\/navigotechsolutions.com\/blog\/aws-sagemaker-ai-inference-recommendations-a-guide-for-indian-firms\/","title":{"rendered":"AWS SageMaker AI Inference Recommendations: A Guide for Indian Firms"},"content":{"rendered":"<style>\n:root{--primary-blue:#1e90ff;--deep-blue:#003C8F;--accent-orange:#1e90ff;--neutral-bg:#F9F9F9;--neutral-white:#FFFFFF;--text-charcoal:#2C2C2C;--text-grey:#555555;--light-blue-bg:#EDF5FF;}\nbody{margin:0;padding:0;font-family:'Open Sans',sans-serif;background-color:var(--neutral-bg);color:var(--text-charcoal);line-height:1.6;}\na{color:var(--primary-blue);font-weight:700;text-decoration:none;border-bottom:2px solid var(--accent-orange);transition:all .3s ease;}\na:hover{color:var(--deep-blue);border-bottom-color:var(--primary-blue);background-color:var(--light-blue-bg);}\n.navigo-container{font-family:'Open Sans',sans-serif;background-color:var(--neutral-bg);background-image:radial-gradient(#e5e5e5 1px,transparent 1px);background-size:20px 20px;max-width:900px;margin:40px auto;padding:40px;border-radius:20px;box-shadow:0 10px 30px 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p{font-size:1.05rem;color:var(--text-grey);line-height:1.7;margin:0 0 12px;max-width:760px;}\n.navigo-article{background:var(--neutral-white);padding:35px;border-radius:12px;border:1px solid #e5e5e5;line-height:1.9;font-size:1.06rem;color:var(--text-grey);position:relative;z-index:1;}\n.navigo-article h2{font-family:'Montserrat',sans-serif;color:var(--deep-blue);font-size:1.6rem;margin-top:34px;margin-bottom:12px;}\n.navigo-article h3{font-family:'Montserrat',sans-serif;color:var(--text-charcoal);font-size:1.2rem;margin-top:22px;margin-bottom:8px;}\n.navigo-article p{margin-bottom:14px;}\n.navigo-article ul{margin-left:18px;margin-bottom:14px;}\n.navigo-article table{width:100%;border-collapse:collapse;margin:20px 0;}\n.navigo-article th,.navigo-article td{border:1px solid #e5e5e5;padding:12px;text-align:left;}\n.navigo-article th{background-color:#f2f2f2;color:var(--deep-blue);}\n.key-takeaways{background:var(--light-blue-bg);border-left:6px solid var(--primary-blue);padding:18px 22px;border-radius:8px;margin:28px 0;}\n.toc{background:#fafafa;border:1px solid #eee;padding:20px;border-radius:8px;margin:25px 0;}\n.toc strong{display:block;margin-bottom:10px;font-family:'Montserrat',sans-serif;color:var(--deep-blue);}\n.toc ul{list-style-type:none;padding-left:0;margin:0;}\n.toc li{margin-bottom:8px;padding-left:15px;position:relative;}\n.toc li::before{content:\"\u2022\";color:var(--primary-blue);position:absolute;left:0;top:0;}\n.navigo-faq-header{text-align:center;margin-top:42px;margin-bottom:22px;}\n.navigo-faq-header h2{font-family:'Montserrat',sans-serif;font-size:1.9rem;color:var(--text-charcoal);}\n.navigo-faq-details{background:var(--neutral-white);margin-bottom:12px;border-radius:8px;border:1px solid #e5e5e5;overflow:hidden;position:relative;z-index:1;}\n.navigo-faq-summary{padding:16px 20px;font-family:'Montserrat',sans-serif;font-weight:700;color:var(--deep-blue);cursor:pointer;display:flex;justify-content:space-between;align-items:center;list-style:none;}\n.navigo-faq-summary::after{content:'';width:10px;height:10px;border-right:3px solid var(--primary-blue);border-bottom:3px solid var(--primary-blue);transform:rotate(45deg);flex-shrink:0;}\n.navigo-faq-details[open] .navigo-faq-summary::after{transform:rotate(-135deg);}\n.navigo-faq-answer{padding:0 20px 18px;color:var(--text-grey);}\n.navigo-footer{margin-top:36px;padding-top:22px;border-top:2px solid #eee;text-align:center;color:var(--text-grey);font-size:.95rem;position:relative;z-index:1;}\n@media(max-width:768px){.navigo-container{padding:20px;margin:0;border-radius:0;}.navigo-hero{padding:25px;}.navigo-article{padding:20px;font-size:1rem;}.navigo-shape-top-right,.navigo-shape-bottom-left{display:none;}}\n<\/style>\n<link href=\"https:\/\/fonts.googleapis.com\/css2?family=Montserrat:wght@400;700;800&#038;family=Open+Sans:wght@400;600;700&#038;display=swap\" rel=\"stylesheet\">\n<div class=\"navigo-container\">\n<div class=\"navigo-shape-top-right\"><\/div>\n<div class=\"navigo-shape-bottom-left\"><\/div>\n<div class=\"navigo-hero\">\n<div class=\"navigo-logo\">NaviGo Tech Solutions<\/div>\n<h1>AWS SageMaker AI Just Launched Inference Recommendations for Indian Firms<\/h1>\n<p><strong>AWS SageMaker AI<\/strong> has released a new feature called <strong>optimised generative AI inference recommendations<\/strong> that eliminates weeks of manual tuning. For Indian small business owners and startups who want to deploy AI models without a data science team, this tool is a game-changer.<\/p>\n<p>This guide covers:<\/p>\n<ul>\n<li>What inference recommendations do and why they matter for Indian firms<\/li>\n<li>How the three-stage process works step by step<\/li>\n<li>Real cost savings with examples like GPT-OSS-20B on H100 GPUs<\/li>\n<li>Common pitfalls to avoid when adopting this tool<\/li>\n<\/ul>\n<p>Let us break it down in plain language so you can decide if this is right for your business.<\/p>\n<\/p><\/div>\n<div class=\"navigo-article\">\n<div class=\"key-takeaways\"><strong>What You&#8217;ll Learn:<\/strong><\/p>\n<ul>\n<li>Inference recommendations cut deployment from weeks to hours<\/li>\n<li>Three-stage process: narrow configuration space, apply goal-aligned optimisations, benchmark with NVIDIA AIPerf<\/li>\n<li>Indian businesses can halve inference costs with throughput optimisation<\/li>\n<li>Available in 7 AWS regions, including Asia Pacific (Singapore)<\/li>\n<\/ul><\/div>\n<div class=\"toc\"><strong>Table of Contents<\/strong><\/p>\n<ul>\n<li><a href=\"#section-1\">What Are AWS SageMaker AI Inference Recommendations?<\/a><\/li>\n<li><a href=\"#section-2\">Why Indian Firms Need This Tool Now<\/a><\/li>\n<li><a href=\"#section-3\">How to Use Inference Recommendations Step by Step<\/a><\/li>\n<li><a href=\"#section-4\">Common Mistakes to Avoid<\/a><\/li>\n<li><a href=\"#section-5\">Inference Recommendations vs Traditional Deployment<\/a><\/li>\n<\/ul><\/div>\n<h2 id=\"section-1\">What Are AWS SageMaker AI Inference Recommendations?<\/h2>\n<p>AWS SageMaker AI inference recommendations are a new feature launched on April 22, 2026. They automate the process of finding the best configuration to run a generative AI model in production. Instead of spending weeks manually testing different GPU types, batch sizes, and parallelisation settings, you now get validated recommendations with performance metrics in hours.<\/p>\n<p>The tool uses a three-stage process. First, it narrows the configuration space by eliminating impossible or inefficient options. Second, it applies goal-aligned optimisations based on whether you care more about throughput, latency, or tensor parallelism. Third, it uses <strong>NVIDIA AIPerf<\/strong> \u2014 a modular component of NVIDIA Dynamo \u2014 to benchmark and rank the top configurations. AWS contributed to make the results statistically rigorous.<\/p>\n<p>For example, a GPT-OSS-20B model running on a single ml.p5en.48xlarge instance (with H100 GPUs) can serve 2x more tokens at the same request latency. This effectively halves inference cost per token. The output is a SageMaker Model Package with instance-specific deployment configurations. You deploy it as a standard real-time endpoint or Inference Component.<\/p>\n<p>Currently available in 7 AWS regions including Asia Pacific (Singapore), this is relevant for Indian firms using AWS Mumbai or Singapore endpoints. For <strong>AI strategy consulting<\/strong> tailored to your business, NaviGo Tech Solutions offers <a href=\"https:\/\/navigotechsolutions.com\/services.html#consulting\">AI Strategy Consulting<\/a> to help you plan your deployment.<\/p>\n<h2 id=\"section-2\">Why Indian Firms Need This Tool Now<\/h2>\n<h3>Reducing Deployment Time from Weeks to Hours<\/h3>\n<p>Indian startups and SMBs often lack dedicated MLOps teams. A typical AI model deployment takes 2 to 4 weeks of manual testing across GPU instances, batch sizes, and parallelism strategies. Inference recommendations collapse that into a single run of a few hours. For a Chennai-based edtech company launching a chatbot, this means going from concept to production in days.<\/p>\n<h3>Cutting Infrastructure Costs<\/h3>\n<p>GPU instances are expensive. An ml.p5en.48xlarge costs around \u20b91,500 per hour on-demand. Over-provisioning \u2014 buying more GPU than needed \u2014 is common when teams guess optimal settings. Inference recommendations give you exact configurations, so you never waste compute. The throughput optimisation example with GPT-OSS-20B halving cost per token is a direct benefit for Indian firms on tight budgets.<\/p>\n<h3>Building with Confidence<\/h3>\n<p>The integration with NVIDIA AIPerf provides statistically grounded results \u2014 time to first token, inter-token latency, request latency percentiles (P50\/P90\/P99), throughput, and cost projections. Indian banks and fintechs that need predictable latency can rely on these numbers for compliance and SLAs.<\/p>\n<h3>Supporting India&#8217;s Move to Production AI<\/h3>\n<p>At AWS Summit 2026, the narrative shifted from pilot hype to production discipline. Indian companies are moving beyond experiments. Inference recommendations align with this by eliminating the guesswork. For businesses using <a href=\"https:\/\/navigotechsolutions.com\/services.html#seo\">SEO Optimization<\/a> or other digital services, integrating AI inference can improve customer-facing responses without a big team.<\/p>\n<figure class=\"wp-block-image size-large\" style=\"margin: 32px 0; text-align: center;\">\n                      <img decoding=\"async\" src=\"https:\/\/navigotechsolutions.com\/blog\/wp-content\/uploads\/2026\/07\/aws-sagemaker-ai-inference-recommendations-1.jpg\" alt=\"list-based infographic of 4 reasons why Indian firms need inference recommendations, with numbered colored icons: 1. Faster deployment (clock icon), 2. Lower costs (rupee icon), 3. Confidence with metrics (chart icon), 4. Production readiness (gear icon). Clean white background, premium blue and green colours, short labels like \"Weeks to Hours\" and \"Halve Token Cost\".\" style=\"border-radius: 12px; max-width: 100%; height: auto; box-shadow: 0 4px 15px rgba(0,0,0,0.08);\" \/><br \/>\n                    <\/figure>\n<h2 id=\"section-2\">How to Use Inference Recommendations Step by Step<\/h2>\n<p>Follow these five steps to deploy your model with optimised inference recommendations on SageMaker AI.<\/p>\n<ul>\n<li><strong>Step 1: Prepare your model.<\/strong> Upload your generative AI model \u2014 like GPT-OSS-20B or a fine-tuned LLaMA variant \u2014 to Amazon S3 in a format SageMaker AI supports. Ensure your model artefacts are accessible from your AWS account.<\/li>\n<li><strong>Step 2: Create a SageMaker AI notebook or use the SDK.<\/strong> Start a SageMaker Studio notebook or run the AWS SDK for Python (boto3). Import the SageMaker SDK and set up a session with your AWS region. For Indian firms, choose ap-south-1 (Mumbai) or ap-southeast-1 (Singapore) for low latency.<\/li>\n<li><strong>Step 3: Initiate the inference recommendation job.<\/strong> Call the <code>sagemaker.create_inference_recommendations_job<\/code> API. Provide your model&#8217;s S3 path, the framework (PyTorch or TensorFlow), and a target metric \u2014 either throughput or latency. The tool will auto-discover eligible instance types.<\/li>\n<li><strong>Step 4: Review the ranked recommendations.<\/strong> Once the job completes, examine the output. You will see a list of instance types with performance metrics: time to first token, inter-token latency, P50\/P90\/P99 latency, throughput, and cost per thousand tokens. Pick the one that matches your budget and speed requirements.<\/li>\n<li><strong>Step 5: Deploy using the Model Package.<\/strong> The recommendation output includes a SageMaker Model Package with pre-configured deployment settings. Use the standard <code>sagemaker.create_endpoint<\/code> API. Your model is now live with validated optimisations.<\/li>\n<\/ul>\n<p>For deeper integration with AI-powered automation, explore <a href=\"https:\/\/navigotechsolutions.com\/services.html#ai-agents\">AI Agents &#038; Bots<\/a> from NaviGo Tech Solutions to extend your inference endpoint into customer-facing tools.<\/p>\n<h2 id=\"section-4\">Common Mistakes to Avoid<\/h2>\n<h3>Ignoring the Cost Projection Reports<\/h3>\n<p>Many Indian firms skip the cost projection column in the recommendation output. This leads to choosing a high-throughput instance that costs 3x more than a balanced option. Always check the cost per thousand tokens before finalising. For example, an ml.p5en.48xlarge might offer lower latency but at \u20b91,500\/hour versus a p4d instance at \u20b9800\/hour. The tool provides both numbers, so use them.<\/p>\n<h3>Deploying Without Validating in a Staging Environment<\/h3>\n<p>Inference recommendations are statistically rigorous, but production traffic patterns can vary. Deploying directly to a live endpoint without a staging test is risky. Use SageMaker&#8217;s A\/B testing feature with Inference Components to send a small percentage of traffic to the new endpoint first. Monitor latency and error rates for at least 24 hours before cutting over.<\/p>\n<h3>Overlooking Regional Availability<\/h3>\n<p>As of launch, the feature is available in only 7 AWS regions. Indian firms must ensure their model and data stay in Mumbai (ap-south-1) or use Singapore. If you use a non-supported region, the inference recommendation job will fail. Check the AWS documentation for the latest regional updates.<\/p>\n<h3>Not Aligning Optimisation Goals with Business Needs<\/h3>\n<p>If you run a real-time customer chatbot, you want low latency. If you process batch responses, you want high throughput. The tool asks for a target metric \u2014 choose wisely. Many firms pick &#8220;latency&#8221; because it sounds better, but that can increase costs unnecessarily. For most Indian SMBs, a balanced throughput optimisation delivers the best ROI.<\/p>\n<figure class=\"wp-block-image size-large\" style=\"margin: 32px 0; text-align: center;\">\n                      <img decoding=\"async\" src=\"https:\/\/navigotechsolutions.com\/blog\/wp-content\/uploads\/2026\/07\/aws-sagemaker-ai-inference-recommendations-2.jpg\" alt=\"2-column comparison grid, Mistakes (red X) with examples like \"Ignore cost\" and \"Skip staging\" vs Best Practices (green check) with actions like \"Check cost column\" and \"A\/B test first\". Clean white background, simple icons, premium colours red and green, short labels for readability.\" style=\"border-radius: 12px; max-width: 100%; height: auto; box-shadow: 0 4px 15px rgba(0,0,0,0.08);\" \/><br \/>\n                    <\/figure>\n<h2 id=\"section-5\">Inference Recommendations vs Traditional Deployment<\/h2>\n<p>Traditional AI deployment involves manual benchmarking across GPU instance types, batch sizes, and parallelism settings. Teams often spend 2 to 4 weeks guessing the right configuration, leading to over-provisioned infrastructure or poor performance. Inference recommendations replace this with an automated, data-driven approach that delivers validated configurations in hours.<\/p>\n<p>The table below compares the two methods across key metrics important for Indian businesses.<\/p>\n<table>\n<thead>\n<tr>\n<th>Metric<\/th>\n<th>Traditional Deployment<\/th>\n<th>Inference Recommendations<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Time to production<\/td>\n<td>2 to 4 weeks<\/td>\n<td>2 to 4 hours<\/td>\n<\/tr>\n<tr>\n<td>Cost estimation<\/td>\n<td>Manual guesswork<\/td>\n<td>Automated cost per token<\/td>\n<\/tr>\n<tr>\n<td>Performance metrics<\/td>\n<td>Basic latency only<\/td>\n<td>P50\/P90\/P99, throughput, TTFT, inter-token latency<\/td>\n<\/tr>\n<tr>\n<td>Optimisation goal<\/td>\n<td>One-size-fits-all<\/td>\n<td>Throughput or latency aligned<\/td>\n<\/tr>\n<tr>\n<td>Risk of over-provisioning<\/td>\n<td>High (40-60% waste)<\/td>\n<td>Low (validated config)<\/td>\n<\/tr>\n<tr>\n<td>Expertise needed<\/td>\n<td>MLOps or data science team<\/td>\n<td>Basic AWS SDK knowledge<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>For Indian firms using <a href=\"https:\/\/navigotechsolutions.com\/services.html#digital-marketing\">AI Digital Marketing<\/a>, this tool means you can deploy customer-facing AI tools faster and cheaper. NaviGo Tech Solutions can help you integrate these recommendations into your existing workflow.<\/p>\n<\/p><\/div>\n<div style=\"background:linear-gradient(135deg,#25D366 0%,#128C7E 100%);border-radius:12px;padding:24px 28px;margin:32px 0;text-align:center;position:relative;z-index:1;\">\n<p style=\"color:#fff;font-family:'Montserrat',sans-serif;font-weight:800;font-size:1.15rem;margin:0 0 8px;\">Not sure which tool fits your business?<\/p>\n<p style=\"color:rgba(255,255,255,0.9);font-size:0.95rem;margin:0 0 16px;\">Our team at NaviGo Tech Solutions will set it up for you \u2014 free 30-minute strategy call.<\/p>\n<p>  <a href=\"https:\/\/wa.me\/916380853075?text=Hi%2C%20I%20read%20your%20blog%20and%20want%20a%20free%20strategy%20call\" target=\"_blank\" rel=\"noopener\" style=\"background:#fff;color:#128C7E;font-family:&#039;Montserrat&#039;,sans-serif;font-weight:800;padding:12px 28px;border-radius:50px;text-decoration:none;font-size:1rem;border-bottom:none;display:inline-block;\">WhatsApp Us Now \u2014 It&#8217;s Free<\/a>\n<\/div>\n<div class=\"navigo-faq-header\">\n<h2>Frequently Asked Questions<\/h2>\n<\/div>\n<details class=\"navigo-faq-details\">\n<summary class=\"navigo-faq-summary\">How much does it cost to use AWS SageMaker AI inference recommendations?<\/summary>\n<div class=\"navigo-faq-answer\">The inference recommendation job itself incurs a fee based on the compute instances used during benchmarking. You pay for the GPU hours your job runs. The cost varies by instance type and job duration, typically ranging from Rs 2,000 to Rs 10,000 for a standard GPT-OSS-20B model. However, the savings from avoiding over-provisioning often exceed this cost within a month of production usage.<\/div>\n<\/details>\n<details class=\"navigo-faq-details\">\n<summary class=\"navigo-faq-summary\">Can I use this with custom fine-tuned models?<\/summary>\n<div class=\"navigo-faq-answer\">Yes. The tool works with any generative AI model that you can deploy as a SageMaker endpoint. For fine-tuned models, you need to provide the model artefacts in a supported framework like PyTorch or TensorFlow. The tool automatically discovers instance types and optimises for your specific model weights and architecture.<\/div>\n<\/details>\n<details class=\"navigo-faq-details\">\n<summary class=\"navigo-faq-summary\">Is this available in the Mumbai AWS region?<\/summary>\n<div class=\"navigo-faq-answer\">At launch in April 2026, inference recommendations are available in 7 AWS regions. The closest supported region for Indian firms is Asia Pacific (Singapore). AWS has announced plans to expand to more regions, and Mumbai is likely to be added soon. Check the AWS SageMaker AI documentation for the latest regional updates.<\/div>\n<\/details>\n<details class=\"navigo-faq-details\">\n<summary class=\"navigo-faq-summary\">What models have been tested with this feature?<\/summary>\n<div class=\"navigo-faq-answer\">AWS publicly demonstrated testing with GPT-OSS-20B, a 20-billion-parameter model. The tool works with many popular generative AI models including LLaMA, Falcon, and Mistral variants. The three-stage process is model-agnostic, so you can use it with any model that fits within SageMaker&#8217;s deployment constraints. For specialised use cases, consider consulting with NaviGo Tech Solutions.<\/div>\n<\/details>\n<div class=\"navigo-footer\">\n<p>Inference recommendations slash deployment time, cut GPU costs, and give Indian firms production-ready AI in hours. <strong>Stop guessing your configurations and start saving today.<\/strong><\/p>\n<p><a href=\"https:\/\/navigotechsolutions.com\/contact.html\" target=\"_blank\">WhatsApp Us Now \u2014 It&#8217;s Free \u2014 NaviGo Tech Solutions<\/a><\/p>\n<\/p><\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":"<p>AWS SageMaker AI just launched inference recommendations for Indian firms. Learn how this tool cuts deployment time, reduces cost, and optimises AI performance.<\/p>\n","protected":false},"author":1,"featured_media":2108,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","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":""}},"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[216],"tags":[1110,950,98,1364,1362,1365,1367,1267,21,1366],"class_list":["post-2111","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai-tools","tag-ai-deployment","tag-ai-inference","tag-ai-tools-2026","tag-aws-sagemaker-ai","tag-cost-reduction","tag-gpt-oss-20b","tag-gpu-optimisation","tag-indian-firms","tag-navigo-tech-solutions","tag-nvidia-aiperf"],"jetpack_featured_media_url":"https:\/\/navigotechsolutions.com\/blog\/wp-content\/uploads\/2026\/07\/aws-sagemaker-ai-inference-recommendations.jpg","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/navigotechsolutions.com\/blog\/wp-json\/wp\/v2\/posts\/2111","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/navigotechsolutions.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/navigotechsolutions.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/navigotechsolutions.com\/blog\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/navigotechsolutions.com\/blog\/wp-json\/wp\/v2\/comments?post=2111"}],"version-history":[{"count":1,"href":"https:\/\/navigotechsolutions.com\/blog\/wp-json\/wp\/v2\/posts\/2111\/revisions"}],"predecessor-version":[{"id":2112,"href":"https:\/\/navigotechsolutions.com\/blog\/wp-json\/wp\/v2\/posts\/2111\/revisions\/2112"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/navigotechsolutions.com\/blog\/wp-json\/wp\/v2\/media\/2108"}],"wp:attachment":[{"href":"https:\/\/navigotechsolutions.com\/blog\/wp-json\/wp\/v2\/media?parent=2111"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/navigotechsolutions.com\/blog\/wp-json\/wp\/v2\/categories?post=2111"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/navigotechsolutions.com\/blog\/wp-json\/wp\/v2\/tags?post=2111"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}