{"id":53071,"date":"2025-09-16T12:47:15","date_gmt":"2025-09-16T12:47:15","guid":{"rendered":"https:\/\/www.devopsschool.com\/blog\/?p=53071"},"modified":"2026-02-21T08:26:10","modified_gmt":"2026-02-21T08:26:10","slug":"top-10-ai-fairness-assessment-tools-solutions-in-2025-features-pros-cons-comparison","status":"publish","type":"post","link":"https:\/\/www.devopsschool.com\/blog\/top-10-ai-fairness-assessment-tools-solutions-in-2025-features-pros-cons-comparison\/","title":{"rendered":"Top 10 AI Fairness Assessment Tools Solutions in 2026: Features, Pros, Cons &amp; Comparison"},"content":{"rendered":"\n<h1 class=\"wp-block-heading\">Introduction<\/h1>\n\n\n\n<p>As artificial intelligence continues to shape decision-making across industries in 2026, <strong>AI fairness assessment tools solutions<\/strong> have become critical for ensuring responsible, unbiased, and trustworthy AI systems. These tools help organizations detect, measure, and mitigate bias in AI models\u2014whether in hiring, finance, healthcare, law enforcement, or customer engagement.<\/p>\n\n\n\n<p>Bias in AI can lead to reputational damage, regulatory non-compliance, and even legal risks. That\u2019s why companies are increasingly turning to AI fairness solutions that offer transparency, fairness metrics, bias detection frameworks, and explainability dashboards.<\/p>\n\n\n\n<p>When choosing the <strong>best AI fairness assessment tools software<\/strong>, decision-makers should look for ease of integration with ML workflows, support for multiple fairness metrics, compatibility with regulatory frameworks (like EU AI Act or EEOC guidelines), visualization capabilities, and scalability for enterprise use.<\/p>\n\n\n\n<p>In this blog, we\u2019ll explore the <strong>top 10 AI fairness assessment tools solutions in 2026<\/strong>, highlighting their features, pros, cons, and comparisons\u2014so you can find the right fit for your organization.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\">\n\n\n\n<h2 class=\"wp-block-heading\">Top 10 AI Fairness Assessment Tools Solutions (2026)<\/h2>\n\n\n\n<figure class=\"wp-block-image size-full is-resized\"><img loading=\"lazy\" decoding=\"async\" width=\"800\" height=\"704\" src=\"https:\/\/www.devopsschool.com\/blog\/wp-content\/uploads\/2025\/09\/5_compressed-9.jpg\" alt=\"\" class=\"wp-image-53695\" style=\"width:840px;height:auto\" srcset=\"https:\/\/www.devopsschool.com\/blog\/wp-content\/uploads\/2025\/09\/5_compressed-9.jpg 800w, https:\/\/www.devopsschool.com\/blog\/wp-content\/uploads\/2025\/09\/5_compressed-9-300x264.jpg 300w, https:\/\/www.devopsschool.com\/blog\/wp-content\/uploads\/2025\/09\/5_compressed-9-768x676.jpg 768w\" sizes=\"auto, (max-width: 800px) 100vw, 800px\" \/><\/figure>\n\n\n\n<h3 class=\"wp-block-heading\">1. <strong>IBM AI Fairness 360 (AIF360)<\/strong><\/h3>\n\n\n\n<p><strong>Short Description:<\/strong> IBM\u2019s open-source toolkit designed for researchers and enterprises to detect and mitigate bias in AI models.<\/p>\n\n\n\n<p><strong>Key Features:<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>70+ fairness metrics and bias detection algorithms<\/li>\n\n\n\n<li>Pre-processing, in-processing, and post-processing debiasing methods<\/li>\n\n\n\n<li>Python-based library for data scientists<\/li>\n\n\n\n<li>Comprehensive documentation and tutorials<\/li>\n\n\n\n<li>Works with Scikit-learn, TensorFlow, and PyTorch<\/li>\n\n\n\n<li>Active open-source community support<\/li>\n<\/ul>\n\n\n\n<p><strong>Pros:<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Open-source and free<\/li>\n\n\n\n<li>Rich set of fairness metrics<\/li>\n\n\n\n<li>Backed by IBM\u2019s enterprise credibility<\/li>\n<\/ul>\n\n\n\n<p><strong>Cons:<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Steeper learning curve for non-technical teams<\/li>\n\n\n\n<li>Limited UI\u2014mostly code-based<\/li>\n<\/ul>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\">\n\n\n\n<h3 class=\"wp-block-heading\">2. <strong>Microsoft Fairlearn<\/strong><\/h3>\n\n\n\n<p><strong>Short Description:<\/strong> An open-source toolkit for assessing and improving fairness in AI, integrated with Microsoft Azure ML.<\/p>\n\n\n\n<p><strong>Key Features:<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Fairness metrics dashboard for visual reporting<\/li>\n\n\n\n<li>Mitigation algorithms like reweighting and reductions<\/li>\n\n\n\n<li>Easy integration with Azure ML pipelines<\/li>\n\n\n\n<li>Jupyter Notebook support for experimentation<\/li>\n\n\n\n<li>Bias analysis across multiple sensitive features<\/li>\n<\/ul>\n\n\n\n<p><strong>Pros:<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Strong visualization capabilities<\/li>\n\n\n\n<li>Seamless Azure ecosystem integration<\/li>\n\n\n\n<li>Active community contributions<\/li>\n<\/ul>\n\n\n\n<p><strong>Cons:<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Best suited for Microsoft ecosystem users<\/li>\n\n\n\n<li>Limited advanced explainability compared to competitors<\/li>\n<\/ul>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\">\n\n\n\n<h3 class=\"wp-block-heading\">3. <strong>Google What-If Tool<\/strong><\/h3>\n\n\n\n<p><strong>Short Description:<\/strong> An interactive visualization tool for TensorFlow and other ML models to test fairness and performance.<\/p>\n\n\n\n<p><strong>Key Features:<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>No-code interface for fairness testing<\/li>\n\n\n\n<li>Counterfactual testing (what-if scenarios)<\/li>\n\n\n\n<li>Supports fairness slicing by demographic groups<\/li>\n\n\n\n<li>TensorBoard integration<\/li>\n\n\n\n<li>Interactive dashboards for quick analysis<\/li>\n<\/ul>\n\n\n\n<p><strong>Pros:<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Very user-friendly for beginners<\/li>\n\n\n\n<li>Strong visualization and interactive features<\/li>\n\n\n\n<li>Free and open-source<\/li>\n<\/ul>\n\n\n\n<p><strong>Cons:<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Limited to certain frameworks (best with TensorFlow)<\/li>\n\n\n\n<li>Lacks enterprise-grade compliance reporting<\/li>\n<\/ul>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\">\n\n\n\n<h3 class=\"wp-block-heading\">4. <strong>Fiddler AI Fairness<\/strong><\/h3>\n\n\n\n<p><strong>Short Description:<\/strong> A model monitoring and explainability platform with built-in fairness assessment.<\/p>\n\n\n\n<p><strong>Key Features:<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Real-time fairness monitoring in production<\/li>\n\n\n\n<li>Explainable AI dashboards for transparency<\/li>\n\n\n\n<li>Bias and drift detection across time<\/li>\n\n\n\n<li>Compliance-ready reporting<\/li>\n\n\n\n<li>Multi-cloud and hybrid deployment options<\/li>\n<\/ul>\n\n\n\n<p><strong>Pros:<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Strong production monitoring features<\/li>\n\n\n\n<li>Enterprise-grade security and compliance<\/li>\n\n\n\n<li>Rich explainability and visualization tools<\/li>\n<\/ul>\n\n\n\n<p><strong>Cons:<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Premium pricing for enterprises<\/li>\n\n\n\n<li>Setup complexity for small teams<\/li>\n<\/ul>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\">\n\n\n\n<h3 class=\"wp-block-heading\">5. <strong>Arthur AI<\/strong><\/h3>\n\n\n\n<p><strong>Short Description:<\/strong> A monitoring platform offering fairness, bias detection, and explainability for deployed AI systems.<\/p>\n\n\n\n<p><strong>Key Features:<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Bias detection dashboards with alerts<\/li>\n\n\n\n<li>Multi-dimensional fairness assessment<\/li>\n\n\n\n<li>Root cause analysis of bias issues<\/li>\n\n\n\n<li>Real-time monitoring at scale<\/li>\n\n\n\n<li>Cloud-native deployment<\/li>\n<\/ul>\n\n\n\n<p><strong>Pros:<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Focused on deployed\/production AI fairness<\/li>\n\n\n\n<li>Strong real-time monitoring capabilities<\/li>\n\n\n\n<li>Easy integration with enterprise ML pipelines<\/li>\n<\/ul>\n\n\n\n<p><strong>Cons:<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Pricing tailored to mid-to-large enterprises<\/li>\n\n\n\n<li>Requires robust data infrastructure<\/li>\n<\/ul>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\">\n\n\n\n<h3 class=\"wp-block-heading\">6. <strong>Truera Fairness<\/strong><\/h3>\n\n\n\n<p><strong>Short Description:<\/strong> A model intelligence platform that provides fairness and explainability insights pre- and post-deployment.<\/p>\n\n\n\n<p><strong>Key Features:<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Fairness scorecards and benchmarking<\/li>\n\n\n\n<li>Root cause bias identification<\/li>\n\n\n\n<li>Model explainability for regulators<\/li>\n\n\n\n<li>Multi-model comparisons<\/li>\n\n\n\n<li>Governance and compliance workflows<\/li>\n<\/ul>\n\n\n\n<p><strong>Pros:<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Enterprise-focused compliance features<\/li>\n\n\n\n<li>Excellent for regulated industries<\/li>\n\n\n\n<li>Combines fairness with model explainability<\/li>\n<\/ul>\n\n\n\n<p><strong>Cons:<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Not open-source<\/li>\n\n\n\n<li>Steeper pricing compared to community tools<\/li>\n<\/ul>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\">\n\n\n\n<h3 class=\"wp-block-heading\">7. <strong>H2O.ai Responsible AI Toolkit<\/strong><\/h3>\n\n\n\n<p><strong>Short Description:<\/strong> A suite of responsible AI tools within H2O.ai\u2019s AutoML platform, focusing on bias and fairness.<\/p>\n\n\n\n<p><strong>Key Features:<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Bias detection across sensitive attributes<\/li>\n\n\n\n<li>Explainability and SHAP value visualizations<\/li>\n\n\n\n<li>Integration with AutoML pipelines<\/li>\n\n\n\n<li>Open-source extensions available<\/li>\n\n\n\n<li>Works with major ML frameworks<\/li>\n<\/ul>\n\n\n\n<p><strong>Pros:<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Integrated into AutoML workflow<\/li>\n\n\n\n<li>Strong visualization features<\/li>\n\n\n\n<li>Flexible (open-source + enterprise options)<\/li>\n<\/ul>\n\n\n\n<p><strong>Cons:<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Best value if using H2O.ai ecosystem<\/li>\n\n\n\n<li>May require technical expertise for setup<\/li>\n<\/ul>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\">\n\n\n\n<h3 class=\"wp-block-heading\">8. <strong>DataRobot Bias and Fairness Toolkit<\/strong><\/h3>\n\n\n\n<p><strong>Short Description:<\/strong> Bias and fairness features built into DataRobot\u2019s enterprise AI lifecycle management platform.<\/p>\n\n\n\n<p><strong>Key Features:<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Bias detection during model development<\/li>\n\n\n\n<li>Automated fairness testing reports<\/li>\n\n\n\n<li>Pre-built compliance templates<\/li>\n\n\n\n<li>Integrates with model governance workflows<\/li>\n\n\n\n<li>Scalable for enterprise AI teams<\/li>\n<\/ul>\n\n\n\n<p><strong>Pros:<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Enterprise-ready with compliance focus<\/li>\n\n\n\n<li>Easy integration for existing DataRobot users<\/li>\n\n\n\n<li>Automated reports save time<\/li>\n<\/ul>\n\n\n\n<p><strong>Cons:<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Requires DataRobot subscription<\/li>\n\n\n\n<li>Less flexibility for open-source customization<\/li>\n<\/ul>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\">\n\n\n\n<h3 class=\"wp-block-heading\">9. <strong>FairML<\/strong><\/h3>\n\n\n\n<p><strong>Short Description:<\/strong> A Python library for auditing black-box classifiers for fairness.<\/p>\n\n\n\n<p><strong>Key Features:<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Audits bias via input sensitivity analysis<\/li>\n\n\n\n<li>Model-agnostic (works across frameworks)<\/li>\n\n\n\n<li>Lightweight Python package<\/li>\n\n\n\n<li>Academic and research community adoption<\/li>\n<\/ul>\n\n\n\n<p><strong>Pros:<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Free and open-source<\/li>\n\n\n\n<li>Simple implementation<\/li>\n\n\n\n<li>Good for researchers and small teams<\/li>\n<\/ul>\n\n\n\n<p><strong>Cons:<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Limited visualization and reporting tools<\/li>\n\n\n\n<li>Not enterprise-focused<\/li>\n<\/ul>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\">\n\n\n\n<h3 class=\"wp-block-heading\">10. <strong>Monitaur AI Fairness<\/strong><\/h3>\n\n\n\n<p><strong>Short Description:<\/strong> A governance and monitoring platform emphasizing ethical AI, compliance, and fairness.<\/p>\n\n\n\n<p><strong>Key Features:<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Fairness risk assessments<\/li>\n\n\n\n<li>Compliance audit logs<\/li>\n\n\n\n<li>Bias monitoring and drift detection<\/li>\n\n\n\n<li>Collaboration features for governance teams<\/li>\n\n\n\n<li>Reporting aligned with AI regulations<\/li>\n<\/ul>\n\n\n\n<p><strong>Pros:<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Compliance-first approach<\/li>\n\n\n\n<li>Strong governance workflows<\/li>\n\n\n\n<li>Tailored for regulated industries (finance, healthcare)<\/li>\n<\/ul>\n\n\n\n<p><strong>Cons:<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Premium cost<\/li>\n\n\n\n<li>Less flexible for startups or small-scale use<\/li>\n<\/ul>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\">\n\n\n\n<h2 class=\"wp-block-heading\">Comparison Table<\/h2>\n\n\n\n<figure class=\"wp-block-table\"><table class=\"has-fixed-layout\"><thead><tr><th>Tool Name<\/th><th>Best For<\/th><th>Platforms Supported<\/th><th>Standout Feature<\/th><th>Pricing<\/th><th>Rating (avg)<\/th><\/tr><\/thead><tbody><tr><td>IBM AIF360<\/td><td>Researchers, developers<\/td><td>Python, open-source<\/td><td>70+ fairness metrics<\/td><td>Free<\/td><td>4.5\/5<\/td><\/tr><tr><td>Microsoft Fairlearn<\/td><td>Azure users, enterprises<\/td><td>Python, Azure ML<\/td><td>Fairness dashboard<\/td><td>Free<\/td><td>4.4\/5<\/td><\/tr><tr><td>Google What-If Tool<\/td><td>Beginners, educators<\/td><td>TensorFlow, Jupyter<\/td><td>Interactive visualization<\/td><td>Free<\/td><td>4.6\/5<\/td><\/tr><tr><td>Fiddler AI<\/td><td>Enterprises, production AI<\/td><td>Multi-cloud, on-prem<\/td><td>Real-time fairness monitoring<\/td><td>Custom pricing<\/td><td>4.7\/5<\/td><\/tr><tr><td>Arthur AI<\/td><td>Enterprise ML monitoring<\/td><td>Cloud-native<\/td><td>Bias monitoring + drift alerts<\/td><td>Custom pricing<\/td><td>4.6\/5<\/td><\/tr><tr><td>Truera<\/td><td>Regulated industries<\/td><td>Multi-cloud<\/td><td>Fairness + explainability<\/td><td>Enterprise pricing<\/td><td>4.5\/5<\/td><\/tr><tr><td>H2O.ai Toolkit<\/td><td>AutoML + fairness teams<\/td><td>H2O.ai, Python<\/td><td>Bias detection in AutoML<\/td><td>Free\/Enterprise<\/td><td>4.5\/5<\/td><\/tr><tr><td>DataRobot Toolkit<\/td><td>Enterprises with governance needs<\/td><td>DataRobot platform<\/td><td>Compliance templates<\/td><td>Enterprise subscription<\/td><td>4.6\/5<\/td><\/tr><tr><td>FairML<\/td><td>Researchers, small teams<\/td><td>Python<\/td><td>Auditing black-box models<\/td><td>Free<\/td><td>4.2\/5<\/td><\/tr><tr><td>Monitaur<\/td><td>Compliance-heavy organizations<\/td><td>SaaS\/Cloud<\/td><td>Governance workflows<\/td><td>Premium<\/td><td>4.6\/5<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\">\n\n\n\n<h2 class=\"wp-block-heading\">Which AI Fairness Assessment Tools Solution is Right for You?<\/h2>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>For researchers and students:<\/strong> IBM AIF360, FairML, or Google What-If Tool (free, open-source, academic-friendly).<\/li>\n\n\n\n<li><strong>For Microsoft\/Azure users:<\/strong> Microsoft Fairlearn (easy integration with Azure ML).<\/li>\n\n\n\n<li><strong>For startups and small businesses:<\/strong> H2O.ai Toolkit (open-source flexibility with AutoML support).<\/li>\n\n\n\n<li><strong>For enterprises in production:<\/strong> Fiddler AI or Arthur AI (strong real-time monitoring).<\/li>\n\n\n\n<li><strong>For highly regulated industries (finance, healthcare, insurance):<\/strong> Truera or Monitaur (compliance-first features).<\/li>\n\n\n\n<li><strong>For organizations already using enterprise AI platforms:<\/strong> DataRobot Bias &amp; Fairness Toolkit (seamless integration).<\/li>\n<\/ul>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\">\n\n\n\n<h2 class=\"wp-block-heading\">Conclusion<\/h2>\n\n\n\n<p>In 2026, <strong>AI fairness assessment tools solutions<\/strong> are no longer optional\u2014they\u2019re essential. With regulations tightening and AI adoption accelerating, businesses must ensure their AI systems are <strong>transparent, unbiased, and compliant<\/strong>.<\/p>\n\n\n\n<p>Whether you\u2019re a researcher experimenting with fairness metrics or an enterprise deploying large-scale AI, there\u2019s a solution that fits your needs. The best approach is to start with free\/open-source options like AIF360 or Fairlearn, then scale to enterprise-grade platforms like Fiddler, Truera, or Monitaur as compliance demands grow.<\/p>\n\n\n\n<p>Explore demos, run pilot projects, and choose the tool that aligns with your <strong>industry, budget, and compliance requirements<\/strong>.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\">\n\n\n\n<h2 class=\"wp-block-heading\">FAQs<\/h2>\n\n\n\n<p><strong>Q1. What are AI fairness assessment tools solutions?<\/strong><br>They are platforms and toolkits designed to detect, measure, and mitigate bias in AI and machine learning models.<\/p>\n\n\n\n<p><strong>Q2. Why are AI fairness tools important in 2026?<\/strong><br>With stricter AI regulations (like EU AI Act) and rising concerns about algorithmic bias, fairness tools ensure compliance, trust, and responsible AI adoption.<\/p>\n\n\n\n<p><strong>Q3. Are these tools only for large enterprises?<\/strong><br>No. Open-source options like IBM AIF360, Fairlearn, and FairML are free and widely used by startups, researchers, and educators.<\/p>\n\n\n\n<p><strong>Q4. Can these tools guarantee 100% fairness?<\/strong><br>No tool can guarantee complete fairness. They provide detection, metrics, and mitigation methods to reduce bias but human oversight is always necessary.<\/p>\n\n\n\n<p><strong>Q5. How do I choose the best AI fairness assessment tool?<\/strong><br>Consider your <strong>company size, industry regulations, budget, and technical expertise<\/strong>. For compliance-heavy industries, choose enterprise-grade solutions; for experimentation, choose open-source tools.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\">\n\n\n\n<h2 class=\"wp-block-heading\">Meta Description<\/h2>\n\n\n\n<p>Discover the top 10 AI fairness assessment tools solutions in 2026. Compare features, pros &amp; cons, pricing, and ratings to find the best software for unbiased AI.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Introduction As artificial intelligence continues to shape decision-making across industries in 2026, AI fairness assessment tools solutions have become critical for ensuring responsible, unbiased, and trustworthy AI systems. These tools&#8230; <\/p>\n","protected":false},"author":54,"featured_media":0,"comment_status":"open","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"_joinchat":[],"footnotes":""},"categories":[2],"tags":[],"class_list":["post-53071","post","type-post","status-publish","format-standard","hentry","category-uncategorised"],"_links":{"self":[{"href":"https:\/\/www.devopsschool.com\/blog\/wp-json\/wp\/v2\/posts\/53071","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.devopsschool.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.devopsschool.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.devopsschool.com\/blog\/wp-json\/wp\/v2\/users\/54"}],"replies":[{"embeddable":true,"href":"https:\/\/www.devopsschool.com\/blog\/wp-json\/wp\/v2\/comments?post=53071"}],"version-history":[{"count":4,"href":"https:\/\/www.devopsschool.com\/blog\/wp-json\/wp\/v2\/posts\/53071\/revisions"}],"predecessor-version":[{"id":59801,"href":"https:\/\/www.devopsschool.com\/blog\/wp-json\/wp\/v2\/posts\/53071\/revisions\/59801"}],"wp:attachment":[{"href":"https:\/\/www.devopsschool.com\/blog\/wp-json\/wp\/v2\/media?parent=53071"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.devopsschool.com\/blog\/wp-json\/wp\/v2\/categories?post=53071"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.devopsschool.com\/blog\/wp-json\/wp\/v2\/tags?post=53071"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}