Cost-effective on-premises multimodal RAG deployment with Kubernetes: a GroundX case study

Introduction: Multimodal systems increasingly run on public cloud infrastructure because it scales on demand. For workloads that are predictable and not time-critical, that convenience carries recurring fees and limited visibility into the platform, and the case for repatriating such workloads to local hardware is under-studied. This paper asks a narrow, practical question. Can a cloud-native multimodal Retrieval-Augmented Generation (RAG) system be moved to on-premises infrastructure with its features intact, and what does the move cost and change in performance? Materials and methods: We present a five-phase framework that maps the cloud-native Kubernetes constructs a multimodal system depends on, node grouping, autoscaling, service exposure, networking, and persistent storage, to on-premises equivalents on a local Kubernetes cluster. We validate the framework as a single-system case study by migrating GroundX, a demanding multimodal RAG system built for the cloud, onto one local Graphics Processing Unit (GPU) workstation, and we measure total cost of ownership, batch ingestion latency, and the response time and request rate of one serving endpoint under concurrent load against cloud baselines. Cost is analyzed with a transparent model whose parameters carry stated ranges, and break-even is reported as a range rather than a point. Results: GroundX ran locally with feature parity on the validated File Ingest pipeline, with all three of its GPU-bound node groups scheduled onto the single local GPU. Under concurrent Application Programming Interface (API) load the on-premises deployment was 11 to 14 percent faster than the cloud on the tested document-list endpoint, with a higher request rate and no failed requests across more than two thousand requests per run, while GPU-bound batch ingestion was slower on-premises, for which the single 48 GB local GPU hosting all three GPU node groups together is a plausible but unverified explanation, since GPU memory use was not measured and the resources of the hosted service were not verified. The two serving paths also differ in network distance, because the local path stayed on the loopback interface while the cloud path crossed the public internet. Set against the cloud estimate with no recurring on-premises cost counted, the one-time hardware outlay is recovered in under four months. A fuller model that adds power, cooling, administration, networking, maintenance, and physical overhead moves the baseline break-even to about four months, and a Monte Carlo over the stated parameter ranges puts the median near four and a half months with 90 percent of outcomes between about four and five months, with administration overhead the dominant driver. Conclusions: For a predictable, non-real-time RAG workload on a single hardware profile, the results indicate that on-premises hosting can be a practical alternative to the cloud, with lower long-run cost past the break-even window and more control over how the system behaves. These findings demonstrate feasibility on the tested configurations and provide a transferable mapping. They do not establish generalization across other multimodal architectures or hardware profiles. The two environments were not resource-matched, the benchmarks were single runs, and the cost and performance comparisons rest on two different cloud baselines, a hosted service for performance and a self-managed cloud footprint for cost, so the comparative figures should be read as observations on the tested configurations rather than as general performance differences or as a measured cost-performance advantage over one matched cloud deployment. Reproduction steps, the pinned GroundX commit, the configurations, the cost model, and the load-testing suite are listed in the data availability statement.