Learn why companies opt for on-premise IoT deployment for their software products, why it’s not a must-take step, and what alternatives you have.
Why are businesses still choosing on-premise IoT deployment over cloud, even when cloud promises scalability and cost-efficiency? That’s because enterprise IoT infrastructure decisions carry real financial stakes and liability. Right, on-premise vs cloud IoT isn’t just about technology preference. There are data security, compliance requirements, latency concerns, and independence from third-party providers to consider.
In this article, we’ll examine this approach in more detail. We’ll also share recommendations on handling IoT data security and compliance, and WebbyLab’s alternatives to on-premise solutions (Propuskator and more).
Propuskator by WebbyLab, a cloud-based ACS
Highlights:
There’s a variety of IoT deployment models available to businesses nowadays:
Each option serves different enterprise needs and performs better for specific IoT business solutions. Let’s see how it all works in the cloud vs on-premise comparison table below.
| Deployment Type | Typical Use Cases | Pros | Cons | Implementation Complexity | Migration Flexibility |
| On-Premise | Building access control systems, SCADA systems, local BMS, solutions with data residency requirements | Full control over data and infrastructure, regulatory compliance, independence from public clouds | High CAPEX, requires a dedicated IT/DevOps team, complex scaling | High | Moving to the cloud typically requires major refactoring |
| Public Cloud (SaaS, multi-tenant) | Smart home/consumer IoT, analytics dashboards, fleet management for small businesses, mobile apps for connected devices | Low entry barrier, rapid scaling, access to AI/ML and data services | Customer data in a shared environment, vendor lock-in, high OPEX at scale | Low/Medium | Complex to move to on-premise or private cloud without rebuilding the architecture |
| Private Managed Cloud (single-tenant per client) | Dedicated instance for enterprise client, custom IoT panels for a factory or facility, separate server for white-label SaaS | Data isolation, easier to meet enterprise requirements, portable containerized architecture | High OPEX (server per client), additional costs for CI/CD and monitoring support | Medium | Easy to migrate between different clouds or to on-premise thanks to Docker/K8s |
| Hybrid Cloud | Local sensor signal processing, cloud analytics, video surveillance with local archive and cloud search, predictive maintenance | Balance between security and scalability, traffic optimization, cloud AI utilization | Complex architecture, expensive DevOps, integration risks | High | Flexible migration with moving toward either cloud or local solutions |
| Edge Deployment | Machine vision on production line, local analytics in transport (e.g., bus monitoring), IoT in agriculture without internet | Low latency, offline operation, traffic cost savings | Limited device resources, difficulty scaling complex computations | Medium | Easy to integrate with cloud/fog as the next step |
| Fog Computing | Distributed EV stations, solar farms, smart-grid systems with dozens of nodes | Reduced latency, cloud load optimization, local data aggregation | High infrastructure complexity and node management costs | High | Can serve as an intermediate step toward cloud or hybrid |
| Multi-Cloud | Cross-regional services (e.g., IoT platform for a global sensor network), SLA-critical services that cannot depend on a single provider | Fault tolerance, cost optimization through different pricing models, no lock-in | Very complex management, need for multi-cloud specialists | Very high | Easy to change balance between clouds, but on-premise migration is difficult |
| Community / Federated Cloud | Smart-city platforms, agricultural cooperatives with a unified IoT database, academic research with sensor data sharing | Shared costs, transparency, standardized data exchange | Difficult to align security and management policies, decision-making inertia | Medium | Can transform into a private or public cloud with management reorganization |
So, architecture determines deployment options. It’s best to consider this as early as you start coding.
Cloud IoT deployment offers clear business advantages. To name a few:
A real-world example of using cloud IoT to the maximum is an A/C drain line cleaner developed by our team. It was deployed on GCP using cloud-native building blocks, as well as Google’s own building blocks and services.
An example of Cloud deployment on GCP
When it comes to IoT data collection and visualization, both options work the same way. Yet, On-premise IoT deployment solves problems that the cloud can’t handle. So, there are several reasons why enterprises go local:
What starts as a cloud vs on-premise security consideration has other benefits for some domains.
Being aware of the advantages and nuances, our team created 2Smart Standalone. The software was developed to be deployed specifically on-premises.
2Smart Standalone logic chart
2Smart Standalone platform by WebbyLab
Ready to discuss your project? IoT consulting services are at your disposal
Yes. It is a hybrid and multi-cloud IoT, as well as a private cloud. It all depends on your case. Private cloud IoT gives you dedicated infrastructure without public cloud risks. Hybrid IoT deployment keeps critical workloads on-premise while moving analytics to the cloud. Multi-cloud IoT spreads risk across providers: if AWS goes down, Azure keeps running.
With cloud infrastructure, a company can focus on business logic and customer value, rather than reinventing the wheel. Our projects, such as MyBox (EV charging management) and Intellect (smart lighting), prove this.
Intellect smart LED lamps built on 2Smart Cloud
Our team can help you solve the on-premise vs cloud IoT dilemma. We have extensive experience with enterprise IoT infrastructure, proven by real case studies. Here are a few examples of the deployment options we can suggest:
Private cloud delivers IoT data sovereignty with scalable flexibility
Learn moreRegardless of what we agree upon, fully secure IoT networks and complete IoT regulatory compliance come in a package.
MyBox, EV charging stations
Cloud vs on-premise deployment
For some companies, on-premise IoT deployment is the only option that makes sense. That’s the case with mission-critical IoT networks and IIoT development services, for example. Yet, it isn’t right for everyone. Here’s a checklist to decide what works for you:
If you answered “yes” to most questions, on-prem IoT for manufacturing likely offers better opportunities for you.
Choose the right IoT deployment strategy and build an IoT device with WebbyLab
Read the guideSo, which one wins in on-premise vs cloud IoT? Just like with connectivity technologies for IoT, its architecture, and other nuances, everything depends on your business priorities and constraints.
Written by:
Head of IoT at Webbylab
With a robust academic background in Telecommunication Systems Engineering, I apply my knowledge to lead innovations in the IoT domain. Starting as the first team member in the newly formed IoT department at WebbyLab, I've spearheaded its growth, fostering the expansion into embedded and hardware development alongside our core software projects. My dedication lies in pushing the boundaries of IoT technology, fostering a culture of innovation and excellence that profoundly impacts our clients' operational success.
Yes. Hybrid deployment lets you process sensitive data locally while sending anonymized insights to cloud analytics services. Many manufacturers do this.
It depends on your architecture. Cloud-native systems built with AWS Lambda or Azure Functions need major rewrites. Containerized applications migrate much more easily.
You’ll get complete control. Only you will decide where data goes, who accesses it, and how long it’s stored. No third-party terms of service will apply to any part of the operations.
We build audit trails into the system from day one. Documentation, access logs, and compliance reports come standard with our on-premise solutions.
Yes, it’s possible, but only if planned from the start. We design systems with deployment flexibility in mind, using containers and infrastructure abstraction.
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