{"id":58192,"date":"2025-12-25T18:59:08","date_gmt":"2025-12-25T18:59:08","guid":{"rendered":"https:\/\/www.devopsschool.com\/blog\/?p=58192"},"modified":"2026-01-18T19:01:28","modified_gmt":"2026-01-18T19:01:28","slug":"top-10-semantic-search-platforms-features-pros-cons-comparison","status":"publish","type":"post","link":"https:\/\/www.devopsschool.com\/blog\/top-10-semantic-search-platforms-features-pros-cons-comparison\/","title":{"rendered":"Top 10 Semantic Search Platforms: Features, Pros, Cons & Comparison"},"content":{"rendered":"\n
\"\"<\/figure>\n\n\n\n

Introduction<\/h2>\n\n\n\n

Semantic Search Platforms are advanced search technologies designed to understand the meaning and intent behind user queries<\/strong>, rather than relying solely on keyword matching. Unlike traditional search systems, semantic search leverages natural language processing (NLP), machine learning, vector embeddings, and contextual analysis<\/strong> to deliver more accurate, relevant, and personalized results.<\/p>\n\n\n\n

In today\u2019s data-driven world\u2014where organizations manage massive volumes of unstructured content such as documents, chats, emails, knowledge bases, and product catalogs\u2014semantic search has become mission-critical. It enables users to find answers<\/em>, not just documents, and supports intelligent use cases like AI-powered customer support, enterprise knowledge discovery, recommendation engines, and conversational assistants.<\/p>\n\n\n\n

When choosing a Semantic Search Platform, buyers should evaluate factors such as search accuracy, scalability, integration capabilities, ease of implementation, security posture, and cost-effectiveness<\/strong>. The right platform can dramatically improve productivity, customer satisfaction, and decision-making quality.<\/p>\n\n\n\n

Best for:<\/strong>
Semantic Search Platforms are ideal for enterprises, SaaS companies, data-heavy organizations, product teams, customer support teams, and AI\/ML engineers<\/strong> looking to improve information retrieval and user experience across large datasets.<\/p>\n\n\n\n

Not ideal for:<\/strong>
They may not be suitable for very small teams with minimal data<\/strong>, basic websites that only need keyword search, or organizations without the technical capacity to maintain AI-driven systems.<\/p>\n\n\n\n

\n\n\n\n

Top 10 Semantic Search Platforms Tools<\/h2>\n\n\n\n
\n\n\n\n

1 \u2014 Elastic<\/h3>\n\n\n\n

Short description:<\/strong>
Elastic provides powerful semantic and vector search capabilities through its search engine, widely used for enterprise search and observability use cases.<\/p>\n\n\n\n

Key features:<\/strong><\/p>\n\n\n\n

    \n
  • Vector and hybrid (keyword + semantic) search<\/li>\n\n\n\n
  • NLP and embedding-based relevance<\/li>\n\n\n\n
  • Scalable distributed architecture<\/li>\n\n\n\n
  • Advanced analytics and dashboards<\/li>\n\n\n\n
  • Custom ranking and relevance tuning<\/li>\n\n\n\n
  • Real-time indexing and querying<\/li>\n<\/ul>\n\n\n\n

    Pros:<\/strong><\/p>\n\n\n\n

      \n
    • Extremely scalable and flexible<\/li>\n\n\n\n
    • Strong ecosystem and tooling<\/li>\n<\/ul>\n\n\n\n

      Cons:<\/strong><\/p>\n\n\n\n

        \n
      • Requires operational expertise<\/li>\n\n\n\n
      • Complex pricing structure<\/li>\n<\/ul>\n\n\n\n

        Security & compliance:<\/strong>
        SSO, RBAC, encryption, audit logs, GDPR support<\/p>\n\n\n\n

        Support & community:<\/strong>
        Excellent documentation, large global community, enterprise support plans available<\/p>\n\n\n\n

        \n\n\n\n

        2 \u2014 Algolia<\/h3>\n\n\n\n

        Short description:<\/strong>
        Algolia offers fast, developer-friendly semantic and AI-powered search optimized for user-facing applications.<\/p>\n\n\n\n

        Key features:<\/strong><\/p>\n\n\n\n

          \n
        • AI-powered relevance and ranking<\/li>\n\n\n\n
        • Neural search and query understanding<\/li>\n\n\n\n
        • Real-time indexing<\/li>\n\n\n\n
        • Personalization and analytics<\/li>\n\n\n\n
        • Easy APIs and SDKs<\/li>\n\n\n\n
        • High availability cloud infrastructure<\/li>\n<\/ul>\n\n\n\n

          Pros:<\/strong><\/p>\n\n\n\n

            \n
          • Very fast response times<\/li>\n\n\n\n
          • Easy to implement and maintain<\/li>\n<\/ul>\n\n\n\n

            Cons:<\/strong><\/p>\n\n\n\n

              \n
            • Costly at scale<\/li>\n\n\n\n
            • Limited deep customization<\/li>\n<\/ul>\n\n\n\n

              Security & compliance:<\/strong>
              SOC 2, GDPR, encryption, SSO<\/p>\n\n\n\n

              Support & community:<\/strong>
              Strong onboarding, responsive support, good developer documentation<\/p>\n\n\n\n

              \n\n\n\n

              3 \u2014 Amazon OpenSearch<\/h3>\n\n\n\n

              Short description:<\/strong>
              Amazon OpenSearch enables semantic and vector search within the AWS ecosystem, suitable for cloud-native applications.<\/p>\n\n\n\n

              Key features:<\/strong><\/p>\n\n\n\n

                \n
              • Vector search with k-NN<\/li>\n\n\n\n
              • Integration with AWS AI services<\/li>\n\n\n\n
              • Fully managed or self-managed options<\/li>\n\n\n\n
              • Scalable cluster management<\/li>\n\n\n\n
              • Advanced security controls<\/li>\n\n\n\n
              • Open-source compatibility<\/li>\n<\/ul>\n\n\n\n

                Pros:<\/strong><\/p>\n\n\n\n

                  \n
                • Deep AWS integration<\/li>\n\n\n\n
                • Highly scalable<\/li>\n<\/ul>\n\n\n\n

                  Cons:<\/strong><\/p>\n\n\n\n

                    \n
                  • AWS lock-in<\/li>\n\n\n\n
                  • Requires cloud expertise<\/li>\n<\/ul>\n\n\n\n

                    Security & compliance:<\/strong>
                    IAM, encryption, audit logs, HIPAA, GDPR<\/p>\n\n\n\n

                    Support & community:<\/strong>
                    Strong AWS documentation, enterprise support via AWS<\/p>\n\n\n\n

                    \n\n\n\n

                    4 \u2014 Pinecone<\/h3>\n\n\n\n

                    Short description:<\/strong>
                    Pinecone is a managed vector database built specifically for semantic search and AI applications.<\/p>\n\n\n\n

                    Key features:<\/strong><\/p>\n\n\n\n

                      \n
                    • High-performance vector similarity search<\/li>\n\n\n\n
                    • Fully managed infrastructure<\/li>\n\n\n\n
                    • Real-time updates<\/li>\n\n\n\n
                    • Horizontal scalability<\/li>\n\n\n\n
                    • API-first design<\/li>\n\n\n\n
                    • ML-friendly architecture<\/li>\n<\/ul>\n\n\n\n

                      Pros:<\/strong><\/p>\n\n\n\n

                        \n
                      • Purpose-built for semantic search<\/li>\n\n\n\n
                      • Minimal operational overhead<\/li>\n<\/ul>\n\n\n\n

                        Cons:<\/strong><\/p>\n\n\n\n

                          \n
                        • Limited beyond vector use cases<\/li>\n\n\n\n
                        • Pricing can increase with scale<\/li>\n<\/ul>\n\n\n\n

                          Security & compliance:<\/strong>
                          Encryption, SOC 2, GDPR (varies by plan)<\/p>\n\n\n\n

                          Support & community:<\/strong>
                          Good documentation, growing developer community, enterprise support<\/p>\n\n\n\n

                          \n\n\n\n

                          5 \u2014 Weaviate<\/h3>\n\n\n\n

                          Short description:<\/strong>
                          Weaviate is an open-source semantic search engine and vector database with built-in ML integrations.<\/p>\n\n\n\n

                          Key features:<\/strong><\/p>\n\n\n\n

                            \n
                          • Hybrid search (vector + keyword)<\/li>\n\n\n\n
                          • Graph-like data modeling<\/li>\n\n\n\n
                          • Open-source flexibility<\/li>\n\n\n\n
                          • Built-in NLP modules<\/li>\n\n\n\n
                          • Cloud and self-hosted options<\/li>\n\n\n\n
                          • Schema-based data organization<\/li>\n<\/ul>\n\n\n\n

                            Pros:<\/strong><\/p>\n\n\n\n

                              \n
                            • Open-source and transparent<\/li>\n\n\n\n
                            • Strong semantic relevance<\/li>\n<\/ul>\n\n\n\n

                              Cons:<\/strong><\/p>\n\n\n\n

                                \n
                              • Requires setup and tuning<\/li>\n\n\n\n
                              • Smaller enterprise ecosystem<\/li>\n<\/ul>\n\n\n\n

                                Security & compliance:<\/strong>
                                Encryption, role-based access, GDPR support<\/p>\n\n\n\n

                                Support & community:<\/strong>
                                Active open-source community, professional support available<\/p>\n\n\n\n

                                \n\n\n\n

                                6 \u2014 Azure Cognitive Search<\/h3>\n\n\n\n

                                Short description:<\/strong>
                                Azure Cognitive Search combines semantic ranking with AI enrichment for enterprise-grade search solutions.<\/p>\n\n\n\n

                                Key features:<\/strong><\/p>\n\n\n\n

                                  \n
                                • Semantic ranking and captions<\/li>\n\n\n\n
                                • AI-powered indexing<\/li>\n\n\n\n
                                • Integration with Azure AI services<\/li>\n\n\n\n
                                • Enterprise-scale indexing<\/li>\n\n\n\n
                                • Rich query syntax<\/li>\n\n\n\n
                                • Built-in analytics<\/li>\n<\/ul>\n\n\n\n

                                  Pros:<\/strong><\/p>\n\n\n\n

                                    \n
                                  • Excellent for Microsoft-centric stacks<\/li>\n\n\n\n
                                  • Enterprise-ready security<\/li>\n<\/ul>\n\n\n\n

                                    Cons:<\/strong><\/p>\n\n\n\n

                                      \n
                                    • Azure dependency<\/li>\n\n\n\n
                                    • Pricing complexity<\/li>\n<\/ul>\n\n\n\n

                                      Security & compliance:<\/strong>
                                      SSO, encryption, ISO, SOC, GDPR<\/p>\n\n\n\n

                                      Support & community:<\/strong>
                                      Strong Microsoft documentation, enterprise support<\/p>\n\n\n\n

                                      \n\n\n\n

                                      7 \u2014 Coveo<\/h3>\n\n\n\n

                                      Short description:<\/strong>
                                      Coveo focuses on AI-powered relevance and personalization for enterprise search and commerce.<\/p>\n\n\n\n

                                      Key features:<\/strong><\/p>\n\n\n\n

                                        \n
                                      • Machine learning relevance models<\/li>\n\n\n\n
                                      • Contextual and personalized search<\/li>\n\n\n\n
                                      • Content recommendation engine<\/li>\n\n\n\n
                                      • Analytics and insights<\/li>\n\n\n\n
                                      • Multi-source indexing<\/li>\n\n\n\n
                                      • Enterprise integrations<\/li>\n<\/ul>\n\n\n\n

                                        Pros:<\/strong><\/p>\n\n\n\n

                                          \n
                                        • Excellent personalization<\/li>\n\n\n\n
                                        • Strong business use cases<\/li>\n<\/ul>\n\n\n\n

                                          Cons:<\/strong><\/p>\n\n\n\n

                                            \n
                                          • High cost<\/li>\n\n\n\n
                                          • Less developer-centric<\/li>\n<\/ul>\n\n\n\n

                                            Security & compliance:<\/strong>
                                            SOC 2, GDPR, encryption, SSO<\/p>\n\n\n\n

                                            Support & community:<\/strong>
                                            Enterprise-grade support, guided onboarding<\/p>\n\n\n\n

                                            \n\n\n\n

                                            8 \u2014 IBM Watson Discovery<\/h3>\n\n\n\n

                                            Short description:<\/strong>
                                            IBM Watson Discovery provides AI-driven semantic search with deep analytics for regulated industries.<\/p>\n\n\n\n

                                            Key features:<\/strong><\/p>\n\n\n\n

                                              \n
                                            • NLP-based document understanding<\/li>\n\n\n\n
                                            • Semantic enrichment<\/li>\n\n\n\n
                                            • Smart document extraction<\/li>\n\n\n\n
                                            • Advanced analytics<\/li>\n\n\n\n
                                            • Industry-specific models<\/li>\n\n\n\n
                                            • Secure deployment options<\/li>\n<\/ul>\n\n\n\n

                                              Pros:<\/strong><\/p>\n\n\n\n

                                                \n
                                              • Strong in regulated environments<\/li>\n\n\n\n
                                              • Deep analytics capabilities<\/li>\n<\/ul>\n\n\n\n

                                                Cons:<\/strong><\/p>\n\n\n\n

                                                  \n
                                                • Complex setup<\/li>\n\n\n\n
                                                • Less modern UI<\/li>\n<\/ul>\n\n\n\n

                                                  Security & compliance:<\/strong>
                                                  HIPAA, GDPR, ISO, encryption, audit logs<\/p>\n\n\n\n

                                                  Support & community:<\/strong>
                                                  Enterprise support, extensive documentation<\/p>\n\n\n\n

                                                  \n\n\n\n

                                                  9 \u2014 Sinequa<\/h3>\n\n\n\n

                                                  Short description:<\/strong>
                                                  Sinequa delivers advanced semantic search and analytics for large enterprises.<\/p>\n\n\n\n

                                                  Key features:<\/strong><\/p>\n\n\n\n

                                                    \n
                                                  • NLP and deep learning models<\/li>\n\n\n\n
                                                  • Knowledge graph integration<\/li>\n\n\n\n
                                                  • Multilingual support<\/li>\n\n\n\n
                                                  • Advanced relevance tuning<\/li>\n\n\n\n
                                                  • Big data scalability<\/li>\n\n\n\n
                                                  • Rich analytics dashboards<\/li>\n<\/ul>\n\n\n\n

                                                    Pros:<\/strong><\/p>\n\n\n\n

                                                      \n
                                                    • Powerful enterprise features<\/li>\n\n\n\n
                                                    • High search accuracy<\/li>\n<\/ul>\n\n\n\n

                                                      Cons:<\/strong><\/p>\n\n\n\n

                                                        \n
                                                      • Expensive<\/li>\n\n\n\n
                                                      • Requires expert setup<\/li>\n<\/ul>\n\n\n\n

                                                        Security & compliance:<\/strong>
                                                        Enterprise-grade security, GDPR, audit logging<\/p>\n\n\n\n

                                                        Support & community:<\/strong>
                                                        Strong enterprise services, professional consulting<\/p>\n\n\n\n

                                                        \n\n\n\n

                                                        10 \u2014 Qdrant<\/h3>\n\n\n\n

                                                        Short description:<\/strong>
                                                        Qdrant is an open-source vector similarity search engine optimized for performance and scalability.<\/p>\n\n\n\n

                                                        Key features:<\/strong><\/p>\n\n\n\n

                                                          \n
                                                        • High-speed vector search<\/li>\n\n\n\n
                                                        • Filtering and payload indexing<\/li>\n\n\n\n
                                                        • Open-source and cloud options<\/li>\n\n\n\n
                                                        • REST and gRPC APIs<\/li>\n\n\n\n
                                                        • Horizontal scalability<\/li>\n\n\n\n
                                                        • Lightweight deployment<\/li>\n<\/ul>\n\n\n\n

                                                          Pros:<\/strong><\/p>\n\n\n\n

                                                            \n
                                                          • High performance<\/li>\n\n\n\n
                                                          • Flexible deployment<\/li>\n<\/ul>\n\n\n\n

                                                            Cons:<\/strong><\/p>\n\n\n\n

                                                              \n
                                                            • Limited enterprise tooling<\/li>\n\n\n\n
                                                            • Smaller ecosystem<\/li>\n<\/ul>\n\n\n\n

                                                              Security & compliance:<\/strong>
                                                              Encryption, basic access controls, GDPR (varies)<\/p>\n\n\n\n

                                                              Support & community:<\/strong>
                                                              Active open-source community, commercial support available<\/p>\n\n\n\n

                                                              \n\n\n\n

                                                              Comparison Table<\/h2>\n\n\n\n
                                                              Tool Name<\/th>Best For<\/th>Platform(s) Supported<\/th>Standout Feature<\/th>Rating<\/th><\/tr><\/thead>
                                                              Elastic<\/td>Enterprise-scale search<\/td>Cloud \/ On-prem<\/td>Hybrid semantic search<\/td>N\/A<\/td><\/tr>
                                                              Algolia<\/td>UX-focused applications<\/td>Cloud<\/td>Speed and relevance<\/td>N\/A<\/td><\/tr>
                                                              Amazon OpenSearch<\/td>AWS-native teams<\/td>Cloud<\/td>AWS integration<\/td>N\/A<\/td><\/tr>
                                                              Pinecone<\/td>AI-driven applications<\/td>Cloud<\/td>Vector performance<\/td>N\/A<\/td><\/tr>
                                                              Weaviate<\/td>Open-source adopters<\/td>Cloud \/ On-prem<\/td>Hybrid search<\/td>N\/A<\/td><\/tr>
                                                              Azure Cognitive Search<\/td>Microsoft ecosystem<\/td>Cloud<\/td>AI enrichment<\/td>N\/A<\/td><\/tr>
                                                              Coveo<\/td>Personalized search<\/td>Cloud<\/td>AI relevance<\/td>N\/A<\/td><\/tr>
                                                              IBM Watson Discovery<\/td>Regulated industries<\/td>Cloud \/ On-prem<\/td>Deep analytics<\/td>N\/A<\/td><\/tr>
                                                              Sinequa<\/td>Large enterprises<\/td>Cloud \/ On-prem<\/td>Knowledge graphs<\/td>N\/A<\/td><\/tr>
                                                              Qdrant<\/td>Developers & startups<\/td>Cloud \/ On-prem<\/td>Speed<\/td>N\/A<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
                                                              \n\n\n\n

                                                              Evaluation & Scoring of Semantic Search Platforms<\/h2>\n\n\n\n
                                                              Criteria<\/th>Weight<\/th>Description<\/th><\/tr><\/thead>
                                                              Core features<\/td>25%<\/td>Semantic accuracy, relevance, AI depth<\/td><\/tr>
                                                              Ease of use<\/td>15%<\/td>Setup, APIs, learning curve<\/td><\/tr>
                                                              Integrations & ecosystem<\/td>15%<\/td>Connectors, SDKs, tools<\/td><\/tr>
                                                              Security & compliance<\/td>10%<\/td>Enterprise readiness<\/td><\/tr>
                                                              Performance & reliability<\/td>10%<\/td>Speed, uptime<\/td><\/tr>
                                                              Support & community<\/td>10%<\/td>Help, documentation<\/td><\/tr>
                                                              Price \/ value<\/td>15%<\/td>Cost vs capability<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
                                                              \n\n\n\n

                                                              Which Semantic Search Platforms Tool Is Right for You?<\/h2>\n\n\n\n
                                                                \n
                                                              • Solo users & startups:<\/strong> Open-source or managed vector databases like Weaviate or Qdrant<\/li>\n\n\n\n
                                                              • SMBs:<\/strong> Algolia or Pinecone for fast implementation<\/li>\n\n\n\n
                                                              • Mid-market:<\/strong> Azure Cognitive Search or Amazon OpenSearch<\/li>\n\n\n\n
                                                              • Enterprise:<\/strong> Elastic, Sinequa, Coveo, or IBM Watson Discovery<\/li>\n<\/ul>\n\n\n\n

                                                                Budget-conscious teams should favor open-source or usage-based pricing<\/strong>, while enterprises with strict compliance needs should prioritize security certifications and vendor support<\/strong>.<\/p>\n\n\n\n

                                                                \n\n\n\n

                                                                Frequently Asked Questions (FAQs)<\/h2>\n\n\n\n
                                                                  \n
                                                                1. What makes semantic search different from keyword search?<\/strong>
                                                                  Semantic search understands intent and context, not just exact words.<\/li>\n\n\n\n
                                                                2. Do I need AI expertise to use semantic search?<\/strong>
                                                                  Some platforms are fully managed and require minimal ML knowledge.<\/li>\n\n\n\n
                                                                3. Is semantic search suitable for small websites?<\/strong>
                                                                  Usually no; keyword search may be sufficient.<\/li>\n\n\n\n
                                                                4. Can semantic search handle multiple languages?<\/strong>
                                                                  Yes, many platforms support multilingual models.<\/li>\n\n\n\n
                                                                5. How secure are semantic search platforms?<\/strong>
                                                                  Enterprise tools offer strong encryption and compliance.<\/li>\n\n\n\n
                                                                6. Is vector search mandatory for semantic search?<\/strong>
                                                                  Modern semantic search typically relies on vectors.<\/li>\n\n\n\n
                                                                7. Can I combine keyword and semantic search?<\/strong>
                                                                  Yes, hybrid search is a best practice.<\/li>\n\n\n\n
                                                                8. What are common implementation mistakes?<\/strong>
                                                                  Poor data preparation and ignoring relevance tuning.<\/li>\n\n\n\n
                                                                9. Are open-source options reliable?<\/strong>
                                                                  Yes, with proper setup and monitoring.<\/li>\n\n\n\n
                                                                10. How long does implementation take?<\/strong>
                                                                  From days (managed tools) to weeks (enterprise systems).<\/li>\n<\/ol>\n\n\n\n
                                                                  \n\n\n\n

                                                                  Conclusion<\/h2>\n\n\n\n

                                                                  Semantic Search Platforms are transforming how users discover and interact with information. By focusing on meaning, context, and intent<\/strong>, these tools deliver faster, more relevant, and more intelligent search experiences.<\/p>\n\n\n\n

                                                                  When selecting a platform, prioritize relevance quality, scalability, integration flexibility, security, and total cost of ownership<\/strong>. There is no single \u201cbest\u201d solution\u2014only the one that best fits your data, team, and business goals<\/strong>. Choosing wisely can unlock powerful insights and significantly elevate user experience.<\/p>\n","protected":false},"excerpt":{"rendered":"

                                                                  Introduction Semantic Search Platforms are advanced search technologies designed to understand the meaning and intent behind user queries, rather than relying solely on keyword matching. Unlike traditional search systems, semantic… <\/p>\n","protected":false},"author":58,"featured_media":0,"comment_status":"open","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"_joinchat":[],"footnotes":""},"categories":[11138],"tags":[23370,23362,23367,15437,23372,23366,23369,23373,23363,23368,23364,23371,15435,23365],"class_list":["post-58192","post","type-post","status-publish","format-standard","hentry","category-best-tools","tag-ai-information-retrieval","tag-ai-powered-search","tag-contextual-search-technology","tag-enterprise-search-solutions","tag-hybrid-search-solutions","tag-intelligent-search-systems","tag-knowledge-discovery-tools","tag-machine-learning-search","tag-natural-language-search","tag-nlp-search-platforms","tag-semantic-ai-tools","tag-semantic-data-search","tag-semantic-search-platforms","tag-vector-search-engines"],"_links":{"self":[{"href":"https:\/\/www.devopsschool.com\/blog\/wp-json\/wp\/v2\/posts\/58192","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\/58"}],"replies":[{"embeddable":true,"href":"https:\/\/www.devopsschool.com\/blog\/wp-json\/wp\/v2\/comments?post=58192"}],"version-history":[{"count":1,"href":"https:\/\/www.devopsschool.com\/blog\/wp-json\/wp\/v2\/posts\/58192\/revisions"}],"predecessor-version":[{"id":58194,"href":"https:\/\/www.devopsschool.com\/blog\/wp-json\/wp\/v2\/posts\/58192\/revisions\/58194"}],"wp:attachment":[{"href":"https:\/\/www.devopsschool.com\/blog\/wp-json\/wp\/v2\/media?parent=58192"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.devopsschool.com\/blog\/wp-json\/wp\/v2\/categories?post=58192"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.devopsschool.com\/blog\/wp-json\/wp\/v2\/tags?post=58192"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}