{"id":58261,"date":"2025-12-29T02:17:29","date_gmt":"2025-12-29T02:17:29","guid":{"rendered":"https:\/\/www.devopsschool.com\/blog\/?p=58261"},"modified":"2026-01-19T02:21:00","modified_gmt":"2026-01-19T02:21:00","slug":"top-10-multi-party-computation-mpc-toolkits-features-pros-cons-comparison","status":"publish","type":"post","link":"https:\/\/www.devopsschool.com\/blog\/top-10-multi-party-computation-mpc-toolkits-features-pros-cons-comparison\/","title":{"rendered":"Top 10 Multi-party Computation (MPC) Toolkits: Features, Pros, Cons & Comparison"},"content":{"rendered":"\n
\"\"<\/figure>\n\n\n\n

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

Multi-party Computation (MPC) toolkits enable multiple parties to jointly compute results over their private data without revealing that data to one another<\/strong>. In a world where data collaboration is essential but privacy regulations are tightening, MPC has emerged as a foundational privacy-preserving technology<\/strong> alongside homomorphic encryption and secure enclaves.<\/p>\n\n\n\n

MPC is important because it allows organizations to unlock insights from sensitive data<\/strong>\u2014financial records, healthcare data, user behavior, or cryptographic keys\u2014without creating a single point of exposure. This capability is increasingly critical in regulated industries, cross-company analytics, decentralized finance, and secure AI training.<\/p>\n\n\n\n

Real-world use cases include<\/strong>:<\/p>\n\n\n\n

    \n
  • Secure key management in crypto custody<\/li>\n\n\n\n
  • Privacy-preserving data analytics across organizations<\/li>\n\n\n\n
  • Confidential machine learning training and inference<\/li>\n\n\n\n
  • Fraud detection without sharing raw customer data<\/li>\n\n\n\n
  • Joint risk modeling in finance and insurance<\/li>\n<\/ul>\n\n\n\n

    When choosing an MPC toolkit, users should evaluate cryptographic robustness, performance, ease of integration, supported protocols, scalability, and security guarantees<\/strong>. The maturity of documentation, community adoption, and enterprise readiness are equally important.<\/p>\n\n\n\n

    Best for:<\/strong>
    Cryptography engineers, blockchain developers, security teams, fintech firms, healthcare analytics teams, AI researchers, and enterprises handling highly sensitive data.<\/p>\n\n\n\n

    Not ideal for:<\/strong>
    Teams without cryptographic expertise, low-risk applications where traditional encryption is sufficient, or use cases requiring real-time latency at massive scale without optimization.<\/p>\n\n\n\n

    \n\n\n\n

    Top 10 Multi-party Computation (MPC) Toolkits Tools<\/strong><\/h2>\n\n\n\n
    \n\n\n\n

    1 \u2014 MP-SPDZ<\/strong><\/h3>\n\n\n\n

    Short description:<\/strong>
    A research-grade MPC framework supporting a wide range of secure computation protocols, widely used in academia and advanced industry research.<\/p>\n\n\n\n

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

      \n
    • Supports arithmetic and boolean MPC<\/li>\n\n\n\n
    • Multiple protocols (SPDZ, MASCOT, semi-honest, malicious)<\/li>\n\n\n\n
    • High performance for complex computations<\/li>\n\n\n\n
    • Python-like high-level language<\/li>\n\n\n\n
    • Flexible backend compilation<\/li>\n\n\n\n
    • Active and passive security models<\/li>\n<\/ul>\n\n\n\n

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

        \n
      • Extremely powerful and flexible<\/li>\n\n\n\n
      • Strong academic validation<\/li>\n<\/ul>\n\n\n\n

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

          \n
        • Steep learning curve<\/li>\n\n\n\n
        • Limited enterprise tooling<\/li>\n<\/ul>\n\n\n\n

          Security & compliance:<\/strong> Cryptographically strong MPC; compliance varies by deployment
          Support & community:<\/strong> Strong academic community, limited commercial support<\/p>\n\n\n\n

          \n\n\n\n

          2 \u2014 SCALE-MAMBA<\/strong><\/h3>\n\n\n\n

          Short description:<\/strong>
          An MPC framework focused on malicious security<\/strong> and scalable performance, often used for high-assurance secure computations.<\/p>\n\n\n\n

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

            \n
          • Actively secure MPC protocols<\/li>\n\n\n\n
          • Optimized preprocessing<\/li>\n\n\n\n
          • Strong adversarial resistance<\/li>\n\n\n\n
          • Designed for large computations<\/li>\n\n\n\n
          • Flexible deployment models<\/li>\n<\/ul>\n\n\n\n

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

              \n
            • High security guarantees<\/li>\n\n\n\n
            • Efficient at scale<\/li>\n<\/ul>\n\n\n\n

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

                \n
              • Complex setup<\/li>\n\n\n\n
              • Less beginner-friendly<\/li>\n<\/ul>\n\n\n\n

                Security & compliance:<\/strong> Strong cryptographic guarantees; compliance varies
                Support & community:<\/strong> Research-oriented documentation and community<\/p>\n\n\n\n

                \n\n\n\n

                3 \u2014 EMP Toolkit<\/strong><\/h3>\n\n\n\n

                Short description:<\/strong>
                A collection of MPC libraries optimized for two-party and multi-party computation<\/strong>, emphasizing performance.<\/p>\n\n\n\n

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

                  \n
                • Multiple MPC primitives<\/li>\n\n\n\n
                • Highly optimized C++ implementation<\/li>\n\n\n\n
                • Support for boolean and arithmetic circuits<\/li>\n\n\n\n
                • Benchmark-friendly design<\/li>\n\n\n\n
                • Modular components<\/li>\n<\/ul>\n\n\n\n

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

                    \n
                  • Excellent performance<\/li>\n\n\n\n
                  • Well-structured libraries<\/li>\n<\/ul>\n\n\n\n

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

                      \n
                    • Low-level development required<\/li>\n\n\n\n
                    • Smaller enterprise ecosystem<\/li>\n<\/ul>\n\n\n\n

                      Security & compliance:<\/strong> Protocol-level security; compliance depends on usage
                      Support & community:<\/strong> Active academic contributors<\/p>\n\n\n\n

                      \n\n\n\n

                      4 \u2014 ABY<\/strong><\/h3>\n\n\n\n

                      Short description:<\/strong>
                      A specialized MPC framework optimized for two-party secure computation<\/strong> with mixed protocols.<\/p>\n\n\n\n

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

                        \n
                      • Arithmetic, boolean, and Yao sharing<\/li>\n\n\n\n
                      • Efficient protocol switching<\/li>\n\n\n\n
                      • Optimized for low latency<\/li>\n\n\n\n
                      • Open research-driven design<\/li>\n\n\n\n
                      • Strong benchmarking results<\/li>\n<\/ul>\n\n\n\n

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

                          \n
                        • Very efficient for 2PC<\/li>\n\n\n\n
                        • Well-studied cryptography<\/li>\n<\/ul>\n\n\n\n

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

                            \n
                          • Limited to two parties<\/li>\n\n\n\n
                          • Not enterprise-ready out of the box<\/li>\n<\/ul>\n\n\n\n

                            Security & compliance:<\/strong> Cryptographic security; compliance varies
                            Support & community:<\/strong> Academic support and documentation<\/p>\n\n\n\n

                            \n\n\n\n

                            5 \u2014 FATE<\/strong><\/h3>\n\n\n\n

                            Short description:<\/strong>
                            A federated learning platform with MPC components, designed for privacy-preserving machine learning<\/strong>.<\/p>\n\n\n\n

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

                              \n
                            • MPC-based secure aggregation<\/li>\n\n\n\n
                            • Federated learning workflows<\/li>\n\n\n\n
                            • Large-scale ML support<\/li>\n\n\n\n
                            • Modular architecture<\/li>\n\n\n\n
                            • Strong industry adoption<\/li>\n<\/ul>\n\n\n\n

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

                                \n
                              • Ideal for privacy-preserving ML<\/li>\n\n\n\n
                              • Production-oriented<\/li>\n<\/ul>\n\n\n\n

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

                                  \n
                                • Heavy infrastructure<\/li>\n\n\n\n
                                • Less general-purpose MPC<\/li>\n<\/ul>\n\n\n\n

                                  Security & compliance:<\/strong> GDPR-aligned design; enterprise controls vary
                                  Support & community:<\/strong> Active open-source and enterprise adoption<\/p>\n\n\n\n

                                  \n\n\n\n

                                  6 \u2014 PySyft<\/strong><\/h3>\n\n\n\n

                                  Short description:<\/strong>
                                  A Python-centric framework enabling MPC and federated learning for data science teams.<\/p>\n\n\n\n

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

                                    \n
                                  • Python-friendly APIs<\/li>\n\n\n\n
                                  • MPC and federated learning<\/li>\n\n\n\n
                                  • Secure data sharing abstractions<\/li>\n\n\n\n
                                  • Integration with ML workflows<\/li>\n\n\n\n
                                  • Rapid prototyping support<\/li>\n<\/ul>\n\n\n\n

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

                                      \n
                                    • Easy for data scientists<\/li>\n\n\n\n
                                    • Strong educational resources<\/li>\n<\/ul>\n\n\n\n

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

                                        \n
                                      • Performance overhead<\/li>\n\n\n\n
                                      • Still evolving maturity<\/li>\n<\/ul>\n\n\n\n

                                        Security & compliance:<\/strong> Privacy-focused design; compliance varies
                                        Support & community:<\/strong> Active community and learning materials<\/p>\n\n\n\n

                                        \n\n\n\n

                                        7 \u2014 TF Encrypted<\/strong><\/h3>\n\n\n\n

                                        Short description:<\/strong>
                                        An MPC-based extension to TensorFlow enabling secure machine learning computation<\/strong>.<\/p>\n\n\n\n

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

                                          \n
                                        • MPC-backed TensorFlow graphs<\/li>\n\n\n\n
                                        • Secure inference and training<\/li>\n\n\n\n
                                        • Familiar ML development model<\/li>\n\n\n\n
                                        • Research-grade cryptography<\/li>\n\n\n\n
                                        • Protocol abstraction layer<\/li>\n<\/ul>\n\n\n\n

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

                                            \n
                                          • Natural fit for ML teams<\/li>\n\n\n\n
                                          • Strong conceptual model<\/li>\n<\/ul>\n\n\n\n

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

                                              \n
                                            • Performance trade-offs<\/li>\n\n\n\n
                                            • Limited long-term maintenance<\/li>\n<\/ul>\n\n\n\n

                                              Security & compliance:<\/strong> Cryptographic security; compliance varies
                                              Support & community:<\/strong> Research-driven, limited enterprise support<\/p>\n\n\n\n

                                              \n\n\n\n

                                              8 \u2014 Jiff<\/strong><\/h3>\n\n\n\n

                                              Short description:<\/strong>
                                              A JavaScript-based MPC library focused on web and educational use cases<\/strong>.<\/p>\n\n\n\n

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

                                                \n
                                              • Browser-compatible MPC<\/li>\n\n\n\n
                                              • Easy setup for demos<\/li>\n\n\n\n
                                              • Flexible protocol options<\/li>\n\n\n\n
                                              • Client-server MPC models<\/li>\n\n\n\n
                                              • Teaching-friendly design<\/li>\n<\/ul>\n\n\n\n

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

                                                  \n
                                                • Accessible and simple<\/li>\n\n\n\n
                                                • Great for prototyping<\/li>\n<\/ul>\n\n\n\n

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

                                                    \n
                                                  • Not enterprise-grade<\/li>\n\n\n\n
                                                  • Performance limitations<\/li>\n<\/ul>\n\n\n\n

                                                    Security & compliance:<\/strong> MPC security; compliance N\/A
                                                    Support & community:<\/strong> Small but active academic community<\/p>\n\n\n\n

                                                    \n\n\n\n

                                                    9 \u2014 SPDZ<\/strong><\/h3>\n\n\n\n

                                                    Short description:<\/strong>
                                                    A foundational MPC protocol family that underpins many modern MPC frameworks.<\/p>\n\n\n\n

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

                                                      \n
                                                    • Malicious security<\/li>\n\n\n\n
                                                    • Offline\/online computation split<\/li>\n\n\n\n
                                                    • Proven cryptographic foundations<\/li>\n\n\n\n
                                                    • Flexible arithmetic computation<\/li>\n\n\n\n
                                                    • Widely cited protocol<\/li>\n<\/ul>\n\n\n\n

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

                                                        \n
                                                      • Strong theoretical guarantees<\/li>\n\n\n\n
                                                      • Highly extensible<\/li>\n<\/ul>\n\n\n\n

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

                                                          \n
                                                        • Not a turnkey toolkit<\/li>\n\n\n\n
                                                        • Requires deep cryptographic expertise<\/li>\n<\/ul>\n\n\n\n

                                                          Security & compliance:<\/strong> Strong cryptographic security; compliance varies
                                                          Support & community:<\/strong> Academic research community<\/p>\n\n\n\n

                                                          \n\n\n\n

                                                          10 \u2014 Sharemind<\/strong><\/h3>\n\n\n\n

                                                          Short description:<\/strong>
                                                          A commercial MPC platform designed for secure data analytics across organizations<\/strong>.<\/p>\n\n\n\n

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

                                                            \n
                                                          • Production-ready MPC engine<\/li>\n\n\n\n
                                                          • Secure data collaboration<\/li>\n\n\n\n
                                                          • Performance-optimized architecture<\/li>\n\n\n\n
                                                          • Enterprise deployment models<\/li>\n\n\n\n
                                                          • Audit-friendly workflows<\/li>\n<\/ul>\n\n\n\n

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

                                                              \n
                                                            • Enterprise-ready<\/li>\n\n\n\n
                                                            • Strong real-world adoption<\/li>\n<\/ul>\n\n\n\n

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

                                                                \n
                                                              • Commercial licensing<\/li>\n\n\n\n
                                                              • Less open customization<\/li>\n<\/ul>\n\n\n\n

                                                                Security & compliance:<\/strong> Enterprise-grade security; compliance varies by deployment
                                                                Support & community:<\/strong> Professional enterprise support<\/p>\n\n\n\n

                                                                \n\n\n\n

                                                                Comparison Table<\/strong><\/h2>\n\n\n\n
                                                                Tool Name<\/th>Best For<\/th>Platform(s) Supported<\/th>Standout Feature<\/th>Rating<\/th><\/tr><\/thead>
                                                                MP-SPDZ<\/td>Advanced MPC research<\/td>Linux<\/td>Multi-protocol support<\/td>N\/A<\/td><\/tr>
                                                                SCALE-MAMBA<\/td>High-assurance MPC<\/td>Linux<\/td>Malicious security<\/td>N\/A<\/td><\/tr>
                                                                EMP Toolkit<\/td>Performance-critical MPC<\/td>C++<\/td>Speed optimization<\/td>N\/A<\/td><\/tr>
                                                                ABY<\/td>Two-party computation<\/td>C++<\/td>Mixed protocol switching<\/td>N\/A<\/td><\/tr>
                                                                FATE<\/td>Privacy-preserving ML<\/td>Distributed<\/td>Federated MPC<\/td>N\/A<\/td><\/tr>
                                                                PySyft<\/td>Data science teams<\/td>Python<\/td>Ease of use<\/td>N\/A<\/td><\/tr>
                                                                TF Encrypted<\/td>Secure ML<\/td>TensorFlow<\/td>ML-native MPC<\/td>N\/A<\/td><\/tr>
                                                                Jiff<\/td>Web-based MPC<\/td>JavaScript<\/td>Browser MPC<\/td>N\/A<\/td><\/tr>
                                                                SPDZ<\/td>Cryptography research<\/td>Protocol-level<\/td>Strong theory<\/td>N\/A<\/td><\/tr>
                                                                Sharemind<\/td>Enterprise analytics<\/td>Multi-platform<\/td>Production readiness<\/td>N\/A<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
                                                                \n\n\n\n

                                                                Evaluation & Scoring of Multi-party Computation (MPC) Toolkits<\/strong><\/h2>\n\n\n\n
                                                                Tool<\/th>Core Features (25%)<\/th>Ease of Use (15%)<\/th>Integrations (15%)<\/th>Security (10%)<\/th>Performance (10%)<\/th>Support (10%)<\/th>Price \/ Value (15%)<\/th>Total<\/th><\/tr><\/thead>
                                                                MP-SPDZ<\/td>23<\/td>8<\/td>10<\/td>9<\/td>9<\/td>7<\/td>12<\/td>78<\/td><\/tr>
                                                                SCALE-MAMBA<\/td>22<\/td>7<\/td>9<\/td>10<\/td>9<\/td>7<\/td>11<\/td>75<\/td><\/tr>
                                                                EMP Toolkit<\/td>21<\/td>9<\/td>9<\/td>9<\/td>10<\/td>7<\/td>12<\/td>77<\/td><\/tr>
                                                                ABY<\/td>18<\/td>10<\/td>8<\/td>8<\/td>9<\/td>7<\/td>13<\/td>73<\/td><\/tr>
                                                                FATE<\/td>20<\/td>12<\/td>13<\/td>8<\/td>8<\/td>9<\/td>12<\/td>82<\/td><\/tr>
                                                                PySyft<\/td>18<\/td>14<\/td>12<\/td>7<\/td>7<\/td>9<\/td>13<\/td>80<\/td><\/tr>
                                                                TF Encrypted<\/td>17<\/td>11<\/td>11<\/td>8<\/td>7<\/td>7<\/td>12<\/td>73<\/td><\/tr>
                                                                Jiff<\/td>14<\/td>13<\/td>8<\/td>6<\/td>6<\/td>6<\/td>14<\/td>67<\/td><\/tr>
                                                                SPDZ<\/td>19<\/td>6<\/td>7<\/td>10<\/td>8<\/td>6<\/td>11<\/td>67<\/td><\/tr>
                                                                Sharemind<\/td>22<\/td>13<\/td>14<\/td>9<\/td>9<\/td>10<\/td>10<\/td>87<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
                                                                \n\n\n\n

                                                                Which Multi-party Computation (MPC) Toolkits Tool Is Right for You?<\/strong><\/h2>\n\n\n\n
                                                                  \n
                                                                • Solo developers & researchers:<\/strong> MP-SPDZ, EMP Toolkit<\/li>\n\n\n\n
                                                                • SMBs & ML teams:<\/strong> PySyft, FATE<\/li>\n\n\n\n
                                                                • Mid-market analytics:<\/strong> FATE, Sharemind<\/li>\n\n\n\n
                                                                • Enterprises & regulated industries:<\/strong> Sharemind<\/li>\n<\/ul>\n\n\n\n

                                                                  Budget-conscious:<\/strong> Open-source toolkits with in-house expertise
                                                                  Premium solutions:<\/strong> Commercial platforms with enterprise support
                                                                  Feature depth vs ease:<\/strong> Research frameworks offer depth; ML-focused tools offer usability
                                                                  Security needs:<\/strong> Malicious-secure protocols for high-risk environments<\/p>\n\n\n\n

                                                                  \n\n\n\n

                                                                  Frequently Asked Questions (FAQs)<\/strong><\/h2>\n\n\n\n

                                                                  1. What problem does MPC solve?<\/strong>
                                                                  It allows joint computation on private data without revealing the data itself.<\/p>\n\n\n\n

                                                                  2. Is MPC production-ready?<\/strong>
                                                                  Yes, but mainly for specialized, high-value use cases.<\/p>\n\n\n\n

                                                                  3. Is MPC slower than traditional computation?<\/strong>
                                                                  Generally yes, but optimization can reduce overhead significantly.<\/p>\n\n\n\n

                                                                  4. Do I need cryptography expertise?<\/strong>
                                                                  Most toolkits require moderate to advanced cryptographic knowledge.<\/p>\n\n\n\n

                                                                  5. Is MPC compliant with GDPR?<\/strong>
                                                                  It supports privacy principles but compliance depends on implementation.<\/p>\n\n\n\n

                                                                  6. Can MPC replace encryption?<\/strong>
                                                                  No, it complements encryption for collaborative computation.<\/p>\n\n\n\n

                                                                  7. Is MPC suitable for AI training?<\/strong>
                                                                  Yes, especially in federated and privacy-sensitive ML.<\/p>\n\n\n\n

                                                                  8. How many parties can MPC support?<\/strong>
                                                                  From two parties to dozens, depending on protocol.<\/p>\n\n\n\n

                                                                  9. What are common mistakes?<\/strong>
                                                                  Ignoring performance trade-offs and underestimating complexity.<\/p>\n\n\n\n

                                                                  10. Are there alternatives to MPC?<\/strong>
                                                                  Yes\u2014homomorphic encryption, secure enclaves, and data anonymization.<\/p>\n\n\n\n

                                                                  \n\n\n\n

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

                                                                  Multi-party Computation toolkits play a critical role in enabling privacy-preserving collaboration<\/strong> across organizations and systems. While the technology is powerful, it is also complex, making careful tool selection essential.<\/p>\n\n\n\n

                                                                  The right MPC toolkit depends on use case complexity, security requirements, performance expectations, and team expertise<\/strong>. There is no universal winner\u2014only the best fit for your specific goals.<\/p>\n","protected":false},"excerpt":{"rendered":"

                                                                  Introduction Multi-party Computation (MPC) toolkits enable multiple parties to jointly compute results over their private data without revealing that data to one another. In a world where data collaboration is essential but privacy regulations are tightening, MPC has emerged as a foundational privacy-preserving technology alongside homomorphic encryption and secure enclaves. MPC is important because it…<\/p>\n","protected":false},"author":58,"featured_media":0,"comment_status":"open","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"_kad_post_transparent":"","_kad_post_title":"","_kad_post_layout":"","_kad_post_sidebar_id":"","_kad_post_content_style":"","_kad_post_vertical_padding":"","_kad_post_feature":"","_kad_post_feature_position":"","_kad_post_header":false,"_kad_post_footer":false,"_kad_post_classname":"","_joinchat":[],"footnotes":""},"categories":[11138],"tags":[23620,23615,23621,23618,23616,23614,23613,23623,23619,23569,23622,23557,23617,23561],"class_list":["post-58261","post","type-post","status-publish","format-standard","hentry","category-best-tools","tag-confidential-computing","tag-cryptographic-computation","tag-cryptography-tools","tag-federated-learning-mpc","tag-mpc-frameworks","tag-mpc-toolkits","tag-multi-party-computation","tag-privacy-enhancing-technologies-2","tag-privacy-preserving-analytics-2","tag-privacy-preserving-computation","tag-secure-computation-protocols","tag-secure-data-collaboration","tag-secure-data-sharing","tag-secure-multi-party-computation"],"_links":{"self":[{"href":"https:\/\/www.devopsschool.com\/blog\/wp-json\/wp\/v2\/posts\/58261","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=58261"}],"version-history":[{"count":1,"href":"https:\/\/www.devopsschool.com\/blog\/wp-json\/wp\/v2\/posts\/58261\/revisions"}],"predecessor-version":[{"id":58263,"href":"https:\/\/www.devopsschool.com\/blog\/wp-json\/wp\/v2\/posts\/58261\/revisions\/58263"}],"wp:attachment":[{"href":"https:\/\/www.devopsschool.com\/blog\/wp-json\/wp\/v2\/media?parent=58261"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.devopsschool.com\/blog\/wp-json\/wp\/v2\/categories?post=58261"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.devopsschool.com\/blog\/wp-json\/wp\/v2\/tags?post=58261"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}