{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T13:54:49Z","timestamp":1781618089987,"version":"3.54.5"},"reference-count":42,"publisher":"Sociedade Brasileira de Computa\u00e7\u00e3o","issue":"1","license":[{"start":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T00:00:00Z","timestamp":1778198400000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JSERD"],"abstract":"<jats:p>In recent years, LLM-based AI development platforms have gained widespread adoption, enabling both IT professionals and citizen developers to create AI-powered applications. However, the landscape\u00a0remains\u00a0fragmented, with a variety of API-based platforms, AI development frameworks, Low Code\/No Code (LCNC) platforms, and Domain-Specific AI as a Service (AIaaS) solution, each offering varying levels of accessibility and customization. Due to the recency of\u00a0the interest\u00a0in LLM-based AI development platforms, there is limited systematic research categorizing these tools based on their functionalities and intended user groups. This paper addresses this gap by proposing a structured, feature-based categorization framework, distinguishing between platforms based on criteria such as primary target group, degree of customization,\u00a0and level of abstraction.\u00a0Methodologically, we apply a feature-driven analysis grounded in documented capabilities and design affordances across a representative set of tools, and we operationalize the two core dimensions (customization and abstraction) through an anchored ordinal scoring rubric to produce a visual map of categories and overlaps.\u00a0However, further empirical research is needed to\u00a0validate\u00a0the attitude of users towards\u00a0the different\u00a0tools in the categories. By providing a clearer understanding of AI development tools, this research supports more informed decision-making and contributes to the democratization of AI adoption across industries.<\/jats:p>","DOI":"10.5753\/jserd.2026.5969","type":"journal-article","created":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T13:04:54Z","timestamp":1781615094000},"page":"62-87","source":"Crossref","is-referenced-by-count":0,"title":["Democratizing AI Development: A Feature-Based Categorization of API Platforms, Development Frameworks, LCNC and AIaaS Platforms for LLM-Based Applications"],"prefix":"10.5753","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-2115-9096","authenticated-orcid":false,"given":"Dimitrios","family":"Tolis","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-8269-5645","authenticated-orcid":false,"given":"Juuso","family":"Rytilahti","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-1684-239X","authenticated-orcid":false,"given":"Oshani","family":"Weerakoon","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-6418-1063","authenticated-orcid":false,"given":"Panu","family":"Puhtila","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2407-9492","authenticated-orcid":false,"given":"Erkki","family":"Kaila","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8799-185X","authenticated-orcid":false,"given":"Tuomas","family":"M\u00e4kil\u00e4","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"3742","published-online":{"date-parts":[[2026,5,8]]},"reference":[{"key":"1","doi-asserted-by":"crossref","unstructured":"Ait, A., Izquierdo, J. L. C., & Cabot, J. (2023, March). Hfcommunity: A tool to analyze the hugging face hub community. In 2023 IEEE international conference on software analysis, evolution and reengineering (SANER) (pp. 728-732). IEEE.","DOI":"10.1109\/SANER56733.2023.00080"},{"key":"2","doi-asserted-by":"crossref","unstructured":"Ajimati, M. O., Carroll, N., & Maher, M. (2025). Adoption of low-code and no-code development: A systematic literature review and future research agenda. Journal of Systems and Software, 222, 112300. https:\/\/doi.org\/10.1016\/j.jss.2024.112300","DOI":"10.1016\/j.jss.2024.112300"},{"key":"3","doi-asserted-by":"crossref","unstructured":"Azizyan, G., Magarian, M. K., & Kajko-Matsson, M. (2011, August). Survey of agile tool usage and needs. In 2011 agile conference (pp. 29-38). IEEE.","DOI":"10.1109\/AGILE.2011.30"},{"key":"4","unstructured":"Bam, L., & Jewell, W. (2005, June). Review: Power system analysis software tools. In IEEE Power Engineering Society General Meeting, 2005 (pp. 139-144). IEEE."},{"key":"5","doi-asserted-by":"crossref","unstructured":"Binzer, B., & Winkler, T. J. (2022). Democratizing Software Development: A Systematic Multivocal Literature Review and Research Agenda on Citizen Development. Lecture Notes in Business Information Processing, 463 LNBIP, 244\u2013259. https:\/\/doi.org\/10.1007\/978-3-031-20706-8_17","DOI":"10.1007\/978-3-031-20706-8_17"},{"key":"6","doi-asserted-by":"crossref","unstructured":"Casta\u00f1o, J., Mart\u00ednez-Fern\u00e1ndez, S., Franch, X., & Bogner, J. (2024, April). Analyzing the evolution and maintenance of ml models on hugging face. In Proceedings of the 21st International Conference on Mining Software Repositories (pp. 607-618).","DOI":"10.1145\/3643991.3644898"},{"key":"7","doi-asserted-by":"crossref","unstructured":"Cowie, K., Rahmatullah, A., Hardy, N., Holub, K., & Kallmes, K. (2022). Web-based software tools for systematic literature review in medicine: systematic search and feature analysis. JMIR Medical Informatics, 10(5), e33219.","DOI":"10.2196\/33219"},{"key":"8","doi-asserted-by":"crossref","unstructured":"Gen\u00e7, A.C., Turkoglu Genc, F., Kaya, Z.N., G\u00f6n\u00fcll\u00fc, E.: AB1701 HOW TO MAKE A VIRTUAL PRESENTATION USING ARTIFICIAL INTELLIGENCE? Ann Rheum Dis. 82, 2088\u20132089 (2023). https:\/\/doi.org\/10.1136\/annrheumdis-2023-eular.6257.","DOI":"10.1136\/annrheumdis-2023-eular.6257"},{"key":"9","doi-asserted-by":"crossref","unstructured":"Da Costa, L. A. L. F., Melchiades, M. B., Girelli, V. S., Colombelli, F., de Ara\u00fajo, D. A., Rigo, S. J., Ramos, G. de O., da Costa, C. A., Righi, R. da R., & Barbosa, J. L. V. (2024). Advancing Chatbot Conversations: A Review of Knowledge Update Approaches. Journal of the Brazilian Computer Society, 30(1), 55\u201368. https:\/\/doi.org\/10.5753\/jbcs.2024.2882","DOI":"10.5753\/jbcs.2024.2882"},{"key":"10","doi-asserted-by":"crossref","unstructured":"Ehsani, K.L., Rhythm, E.R., Mehedi, M.H.K., Rasel, A.A.: A Comparative Analysis of Customer Service Chatbots: Efficiency, Usability and Application. In: 2023 Computer Applications and Technological Solutions, CATS 2023. Institute of Electrical and Electronics Engineers Inc. (2023). https:\/\/doi.org\/10.1109\/CATS58046.2023.10424303.","DOI":"10.1109\/CATS58046.2023.10424303"},{"key":"11","doi-asserted-by":"crossref","unstructured":"Esposito, A., Calvano, M., Curci, A., Desolda, G., Lanzilotti, R., Lorusso, C., & Piccinno, A. (2023). End-User Development for Artificial Intelligence: A Systematic Literature Review. In 29th American Conference on Information Systems (pp. 19\u201334). https:\/\/doi.org\/10.1007\/978-3-031-34433-6_2","DOI":"10.1007\/978-3-031-34433-6_2"},{"key":"12","unstructured":"Gartner Research. (2021). Gartner Forecasts Worldwide Low-Code Development Technologies Market to Grow 23% in 2021. [<a href=\"https:\/\/www.gartner.com\/en\/newsroom\/press-releases\/2021-02-15-gartner-forecasts-worldwide-low-code-development-technologies-market-to-grow-23-percent-in-2021 \">link<\/a>]."},{"key":"13","unstructured":"Gartner Research. (2024). Risk and Opportunity Index: Low-Code Application Platforms. [<a href=\"https:\/\/www.gartner.com\/en\/documents\/5459763\">link<\/a>]."},{"key":"14","doi-asserted-by":"crossref","unstructured":"Hassan, A., Mohammed, F.A., Seyadi, A.Y.: Artificial Intelligence Applications for Marketing. In: Artificial Intelligence and Economic Sustainability in the Era of Industrial Revolution 5.0. pp. 607\u2013618. Springer (2024). https:\/\/doi.org\/10.1007\/978-3-031-56586-1_43.","DOI":"10.1007\/978-3-031-56586-1_43"},{"key":"15","unstructured":"Holkar, A.,  Bhosale, S., Harpale, A., Pachangane, V.H. (2024), UNLOCKING THE DEPTH ANALYSIS IF PDF USING ARTIFICIAL INTELLIGENCE, LARGE LANGUAGE MODEL, LANGCHAIN. International Research Journal of Modernization in Engineering Technology and Science"},{"key":"16","doi-asserted-by":"crossref","unstructured":"Jain, S. M. (2022). Hugging face. In Introduction to transformers for NLP: With the hugging face library and models to solve problems (pp. 51-67). Berkeley, CA: Apress.","DOI":"10.1007\/978-1-4842-8844-3_4"},{"key":"17","doi-asserted-by":"crossref","unstructured":"Joachimiak MP, Miller MA, Caufield JH, et al (2024) The Artificial Intelligence Ontology: LLM-assisted construction of AI concept hierarchies","DOI":"10.1177\/15705838241304103"},{"key":"18","doi-asserted-by":"crossref","unstructured":"Jones, J., Jiang, W., Synovic, N., Thiruvathukal, G., & Davis, J. (2024, October). What do we know about Hugging Face? A systematic literature review and quantitative validation of qualitative claims. In Proceedings of the 18th ACM\/IEEE International Symposium on Empirical Software Engineering and Measurement (pp. 13-24).","DOI":"10.1145\/3674805.3686665"},{"key":"19","doi-asserted-by":"crossref","unstructured":"Kaliuta, K.: Integration of AI for Routine Tasks Using Salesforce. Asian Journal of Research in Computer Science. 16, 119\u2013127 (2023). https:\/\/doi.org\/10.9734\/ajrcos\/2023\/v16i3350.","DOI":"10.9734\/ajrcos\/2023\/v16i3350"},{"key":"20","doi-asserted-by":"crossref","unstructured":"K\u00e4ss, S., Strahringer, S., & Westner, M. (2023). Practitioners\u2019 Perceptions on the Adoption of Low Code Development Platforms. IEEE Access, 11, 29009\u201329034. https:\/\/doi.org\/10.1109\/ACCESS.2023.3258539","DOI":"10.1109\/ACCESS.2023.3258539"},{"key":"21","doi-asserted-by":"crossref","unstructured":"Li, M., Zhao, Y., Yu, B., Song, F., Li, H., Yu, H., ... & Li, Y. (2023). Api-bank: A comprehensive benchmark for tool-augmented llms. arXiv preprint arXiv:2304.08244.","DOI":"10.18653\/v1\/2023.emnlp-main.187"},{"key":"22","doi-asserted-by":"crossref","unstructured":"Long, D. (2021). ACM Reference format: Duri Long, Takeria Blunt, and Brian Magerko. 2021. Co-Designing AI Literacy Exhibits for Informal Learning Spaces. Article, 5(CSCW2). https:\/\/doi.org\/10.1145\/3476034","DOI":"10.1145\/3476034"},{"key":"23","unstructured":"McKinsey & Company. (2024). The state of AI in early 2024: Gen AI adoption spikes and starts to generate value. [<a href=\"https:\/\/www.mckinsey.com\/capabilities\/quantumblack\/our-insights\/the-state-of-ai\">link<\/a>]. Accessed 6 Aug 2024."},{"key":"24","doi-asserted-by":"crossref","unstructured":"Moch, E., & Oberdieck, T. (2024). Strategies for Securing and Further Developing AI Expertise: Measures to Avoid a Shortage of Skilled Workers in the Artificial Intelligence Industry. International Journal of Academic Research and Reflection, 12(1). [<a href=\"www.idpublications.org\">link<\/a>].","DOI":"10.52403\/ijrr.20250849"},{"key":"25","doi-asserted-by":"crossref","unstructured":"Morales-Chan, M.,  Amado-Salvatierra, H. R., Medina, J.A.,  Barchino R., Hern\u00e1ndez-Rizzardini R., Teixeira, A. M. 2024, Personalized Feedback in Massive Open Online Courses: Harnessing the Power of LangChain and OpenAI API, Electronics.","DOI":"10.3390\/electronics13101960"},{"key":"26","doi-asserted-by":"crossref","unstructured":"Ng, D. T. K., Leung, J. K. L., Su, J., Ng, R. C. W., & Chu, S. K. W. (2023). Teachers\u2019 AI digital competencies and twenty-first century skills in the post-pandemic world. Educational Technology Research and Development, 71(1), 137\u2013161. https:\/\/doi.org\/10.1007\/S11423-023-10203-6\/FIGURES\/2","DOI":"10.1007\/s11423-023-10203-6"},{"key":"27","doi-asserted-by":"crossref","unstructured":"Ozkaya, M. (2018). The analysis of architectural languages for the needs of practitioners. Software: Practice and Experience, 48(5), 985-1018.","DOI":"10.1002\/spe.2561"},{"key":"28","doi-asserted-by":"crossref","unstructured":"Ozkaya, M. (2019). Are the UML modelling tools powerful enough for practitioners? A literature review. IEt software, 13(5), 338-354.","DOI":"10.1049\/iet-sen.2018.5409"},{"key":"29","unstructured":"Parthasarathy, V. B., Zafar, A., Khan, A., & Shahid, A. (2024). The ultimate guide to fine-tuning llms from basics to breakthroughs: An exhaustive review of technologies, research, best practices, applied research challenges and opportunities. arXiv preprint arXiv:2408.13296."},{"key":"30","doi-asserted-by":"crossref","unstructured":"Passerini, A., Gema, A., Minervini, P., Sayin, B., & Tentori, K. (2025). Fostering effective hybrid human-LLM reasoning and decision making. Frontiers in Artificial Intelligence, 7, 1464690.","DOI":"10.3389\/frai.2024.1464690"},{"key":"31","doi-asserted-by":"crossref","unstructured":"Prinz N, Huber M, Leonhardt J, & Riedinger C. (2024). Unleash the Power of Citizen Development: Leveraging Organizational Capabilities for Successful Low-Code Development Platform Adoption. In Proceedings of the 57th Hawaii International Conference on System Sciences. University of Hawaii at Manoa.","DOI":"10.24251\/HICSS.2024.069"},{"key":"32","doi-asserted-by":"crossref","unstructured":"Rios-Campos, C., Vega, S.M.Z., Tejada-Castro, M.I., Zambrano, E.O.G., Perez, D.J.G.B., Calder\u00f3n, E.V., Rojas, L.M.F., Alcantara, I.M.B.: Artificial Intelligence and Business. South Florida Journal of Development. 4, 3547\u20133564 (2023). https:\/\/doi.org\/10.46932\/sfjdv4n9-015.","DOI":"10.46932\/sfjdv4n9-015"},{"key":"33","doi-asserted-by":"crossref","unstructured":"Russo, D. (2024). Navigating the complexity of generative ai adoption in software engineering. ACM Transactions on Software Engineering and Methodology, 33(5), 1-50.","DOI":"10.1145\/3652154"},{"key":"34","unstructured":"Shlomov, S., Yaeli, A., Marreed, S., Schwartz, S., Eder, N., Akrabi, O., & Zeltyn, S. (2024). IDA: Breaking Barriers in No-code UI Automation Through Large Language Models and Human-Centric Design. arXiv.Org, abs\/2407.15673. https:\/\/doi.org\/10.48550\/arxiv.2407.15673"},{"key":"35","doi-asserted-by":"crossref","unstructured":"Strobel G, Banh L, M\u00f6ller F, Schoormann T (2024) Exploring Generative Artificial Intelligence: A Taxonomy and Types","DOI":"10.24251\/HICSS.2024.546"},{"key":"36","doi-asserted-by":"crossref","unstructured":"Taheri, M., & Sadjadi, S. M. (2015, July). A Feature-Based Tool-Selection Classification for Agile Software Development. In SEKE (pp. 700-704).","DOI":"10.18293\/SEKE2015-234"},{"key":"37","doi-asserted-by":"crossref","unstructured":"Tolis D, Mystakidis S, Christopoulos A (2025) Generative AI Applications in Education: A Low-Code Approach. Springer Nature Switzerland","DOI":"10.1007\/978-3-031-86551-0_8"},{"key":"38","doi-asserted-by":"crossref","unstructured":"Topsakal, Oguzhan & Akinci, T. Cetin (2023). Creating Large Language Model Applications Utilizing LangChain: A Primer on Developing LLM Apps Fast. International Conference on Applied Engineering and Natural Sciences","DOI":"10.59287\/icaens.1127"},{"key":"39","unstructured":"Viljoen, A., Alt\u0131n, E. N., Hein, A., & Krcmar, H. (2024). Beyond Citizen Development: Exploring Low-Code Platform Adoption by Professional Software Developers. [<a href=\"https:\/\/aisel.aisnet.org\/amcis2024\">link<\/a>]"},{"key":"40","unstructured":"Webb. M. 2024. Mapping the landscape of gen-AI product user experience. [<a href=\"https:\/\/interconnected.org\/home\/2024\/07\/19\/ai-landscape\">link<\/a>]. [Accessed 18-9-2025]."},{"key":"41","unstructured":"Weber I (2024) Large Language Models as Software Components: A Taxonomy for LLM-Integrated Applications"},{"key":"42","unstructured":"Wisskirchen, G., Thibault Biacabe, B., Bormann, U., Muntz, A., Niehaus, G., Soler, G. J., & Von Brauchitsch, B. (2017). Artificial Intelligence and Robotics and Their Impact on the Workplace. IBA Global Employment Institute."}],"container-title":["Journal of Software Engineering Research and Development"],"original-title":[],"link":[{"URL":"https:\/\/journals-sol.sbc.org.br\/index.php\/jserd\/article\/download\/5969\/3997","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals-sol.sbc.org.br\/index.php\/jserd\/article\/download\/5969\/3997","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T13:05:57Z","timestamp":1781615157000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals-sol.sbc.org.br\/index.php\/jserd\/article\/view\/5969"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5,8]]},"references-count":42,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,3,31]]}},"URL":"https:\/\/doi.org\/10.5753\/jserd.2026.5969","relation":{},"ISSN":["2195-1721"],"issn-type":[{"value":"2195-1721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,5,8]]}}}