Scalability and Performance Limitations of Low-Code and No-Code Platforms for Large-Scale Enterprise Applications and Solutions
DOI:
https://doi.org/10.63282/3050-9246.IJETCSIT-V5I3P107Keywords:
Low-Code, No-Code, Scalability, Performance Limitations, Enterprise Applications, Digital Transformation, Platform BottlenecksAbstract
Low-Code and No-Code (LCNC) development platforms have become ubiquitous, and are showing promise as a means of rapid application development, enabling business analysts, citizen developers, and professional software engineers to design, deploy and iterate on applications with little to no hand-coded effort. The growth in LCNC platforms has been impacted by the demand of enterprises to increase their delivery time when it comes to digital transformation, costs, and enhanced agility. Notwithstanding these, scalability and performance constraints have been identified as the primary obstacles to widespread acceptance in mission-critical, high-scale enterprise deployments. In this paper, the author examines these issues in detail, drawing on a review of LCNC architectures, vendor propositions, and practical examples. Performance bottlenecks are examined in terms of the overhead incurred during runtime execution, poor performance of the abstraction layers, poor extensibility, integration overheads, and a lack of suitable governance frameworks. We note that LCNC solutions can support small to medium-scale workloads, but expanding to enterprise workloads comes with design concerns regarding the ability to handle a greater level of concurrency, manage intra-replication transactions, and compliance with regulations. Moreover, dependence on proprietary vendor ecosystems carries long-term risks of lock-in, unpredictable performance, and the inability to potentially port out. This paper summarizes the literature (2015-2023), benchmarks, and industry reports into a comprehensive picture of how to assess the scalability of LCNC. The findings suggest that, despite being able to complement conventional development, LCNC platforms have limitations that necessitate hybrid solutions and other DevOps methods to sustain enterprise adoption. The paper concludes by presenting architectural proposals, governance structures, and future research directions, particularly in light of the challenges associated with edge computing, AI-enhanced LCNC development, and cloud-native scalability
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References
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