The Future of ERP Integrations: Cloud-Native vs. On-Premise Strategies
Bibliographic record
Abstract
Aim: This study aimed to conduct a comparative analysis of cloud-native, on-premise, and hybrid ERP integration models to assess their efficiency, reliability, and total cost of ownership. In turn, the market for ERP software is expected to increase to approximately USD 81.15 billion by 2024, shifting towards cloud-native and on-premise integration strategies. Methods: A comparative experimental design was employed, where simulated ERP workloads were executed across three integration frameworks: cloud-native, on-premise, and hybrid to measure performance, reliability, security, compliance, Total Cost of Ownership (TCO), and speed of delivery. The major environments evaluated included cloud-native (iPaaS + API Gateway + managed event bus), on-premise (ESB + ETL + RDBMS queues), and hybrid (edge agents + cloud broker). Results: A comprehensive workload of datasets (Order-to-Cash, Procure-to-Pay) experiment, along with thorough testing and intensive hands-on statistics, resulted in the provision of data on performance metrics, including latency, throughput, error rate, and system resilience. The primary findings revealed that the cloud solution is faster in terms of latency (-33%) and error rate (-0.39 pp) compared to the on-premise solution and is also more available. The cost of cloud-native systems is usually low compared to TCO. Hybrid systems are not very costly either, although they have greater resilience in terms of flexibility and control over data. The findings suggest that the choice of an integration strategy depends on the organization’s specific requirements. Scalability, costs, and potential downtime are essential aspects. Conclusion: The study concludes that the cloud-native integrations, in both cases of high volume and sufficiently high latency workloads, tend to be more agile, more performant, and more cost-effective, whereas hybrid models present a desirable compromise between scalability and data control to organizations with strict governance needs. Recommendation: Organizations should align their ERP integration strategy with transaction volume, latency tolerance, and data governance requirements to maximize performance and compliance outcomes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".