IoT Platforms vs Open Source: choosing the right way to develop an IoT product

IoT Application Enablement Platforms (AEPs) are no longer a new market. System integrators and engineering companies often consider them when choosing a way to implement a customer-tailored solution for their large enterprise customers.

But what if you are a device manufacturer (OEM/ODM) or an independent software vendor (ISV) developing a brand new open market product rather than a “one-off” solution?

The playground is totally different here. In this case you have your own unique knowledge of the IoT market and, likely, some proven or at least credible way to monetize your expertise. Instead of integrator’s (honestly) classic “commission and forget” paradigm you likely have a long-term product strategy or even a well-defined roadmap for delivering product versions, features and perks to your potential customers.

Moreover, your R&D team is likely strong enough to navigate through a long and tricky channel of classic frontend/backend development, testing and DevSecOps. 

However, choosing the right technical architecture and software platform/stack for your product is the key decision made during any IT product development. The question here isn’t even “What am I optimizing at a 3-5 years horizon?” – it’s about truly long term planning since the life cycle of a successful product is 10+ years. It’s extremely expensive to shift to another stack after the development got to the MVP phase and changing it later almost always means developing a whole new product from scratch.

The idea of this long-read is making you challenge your way of thinking and decision making while choosing your IoT product’s base technology. It’s raising several provocative questions that might or might not affect your choice. 

I will only compare long-term technical strategies of products based on IoT platforms and products based on “classic” or “pure” development stacks, which nowadays is mostly about “traditional high-code development based on open-source libraries and frameworks”.

I intentionally skip comparing these two stacks to using IaaS toolkits, such as Big 3 platforms from Microsoft/Amazon/Google. Both of the ways described and compared here may or may not rely on using public cloud infrastructure and consuming cloud services, such as virtual machines and hosted database instances. Running your product in a public cloud greatly affects your architecture and R&D structure, but it’s not directly affecting the “IoT platform vs open-source” balance.

Technologies Side-by-Side

First of all, let’s clarify what we mean by “IoT AEP-based architecture” and “Classic open-source-based architecture”. Here is the comparison table:

Comparison Criteria

With years of experience, we know a lot about how software teams choose development technologies, stacks and instruments. Trying to structure the comparison using this subjective understanding I’ve resulted to the following assessment points:

• Generic long-term benefits
• Time-to-PoC, time-to-MVP, and time-to-market
• Performance and scalability
• Vendor lock-in vs self-lock-in
• End user UI/UX engineering efforts
• Administrative UI engineering efforts
• Application logic engineering efforts
• Multi-tenancy and productization
• Deployment, scaling, and operations
• Security, governance, and compliance
• Economics: CAPEX vs OPEX
• Support: community-only vs community+vendor
• Monetization model freedom
• Outsourced development availability

Generic Long-term Benefits

Do you know any regular modern software that can run without an operating system, on a bare metal server? What about large-scale enterprise systems implemented in C or even C++?

Operating systems and programming languages based on virtual machines (such as Java and .NET) were created to simplify and speed up software development. Since their inception, their benefits became so strong that any older software had to be re-engineered to leverage them.

It’s highly likely that with time low-code IoT platforms will become an inevitable part of vertical IoT product architecture. Serious successful products require continuous development efforts to keep them market leaders, and using a “classic” stack might end up in non-competitively high R&D costs on a long-term horizon, e.g. in 10+ years.

Time-to-PoC, Time-to-MVP, and Time-To-Market

The key idea of an application enablement platform is shortening derived product time-to-market. While classic development might reach parity with some lower-tier platforms at scale, e.g. in high load scenarios, the absolute majority of platforms shine in prototyping. 

Proof-of-concept (PoC) time using a classic stack may be measured in days or weeks required to write and test code, prepare the infrastructure, and get a demo rollout. A properly selected low code IoT platform allows designing a prototype in mere hours or just a few days.

Minimal Viable Product (MVP) assumes you can start selling your product. Without a platform, your MVP will be ready in several weeks, and advanced products will require several months of development time. 

With a platform, you will most likely shorten this period to several days for simple typical products (such as monitoring dashboards). Platform-based systems that perform on device control and deep data analysis normally go to first production in a few weeks.

Performance and Scalability

Many IT professionals still consider low code platforms, including IoT platforms, to be low performance non-scalable “quick prototyping tools”. In the domains where the low code concept was born, such as CMS or CRM, this is no longer the case – most CTOs and solution architects know it’s safe to build the corporate site or an e-commerce solution atop of a well-known mature low code platform.

But what about the IoT world, with machines being by far the most “productive” source of data in the world, incomparable to humans? Machine data streams assume millions or even billions of daily data samples, while human-initiated transactions are often numbered in thousands even in rather large systems.

Does this mean an IoT platform can’t host a highly loaded product that can’t scale endlessly? It depends. 

When considering a platform, make sure to ask the vendor how exactly it would scale to a potentially endless number of devices, e.g. over a million concurrent connections. There should be a clear, consistent and documented way to build horizontally scalable architecture – or you might get stuck one day.

Another good question to ask is the cost incurred in processing a certain number of devices or data samples. Some platforms answer this question in their price list by charting these metrics. This means predictable but often serious growth of expenses during the scaling process – make sure to plan your budget. Other platforms don’t make you pay for every device and data sample – while this’s a benefit on its own, make sure you clearly understand what kind of subscription or on-premise infrastructure resources you’ll need to host a highly loaded app.

With a classic development stack you can certainly build a highly efficient and scalable app – it’s just a matter of funding an R&D team that includes senior architects and backend developers. There will be no platform vendor taking care of scalability issues – but at the same time you’re not restricted by a certain reference architecture. 

Overall, performance and scalability differs in two development models, with each version having its pros and cons. Tie game here.

Vendor Lock-in vs Self-lock-in

Vendor lock-in, which is a heavy dependency to the IoT platform vendor, is likely causing the largest concern when it comes to selecting a foundation for an important and well-invested product with serious strategy and long-term plans.

This is fair enough – while a product based on multiple open-source libraries and implemented as a large volume of company-owned code has no “single point of failure”, any IoT platform is clearly such a single critical component.

You should scrutinize a number of constraints, both technical and non-technical, when considering a platform within any serious product initiative.

First of all, you should be able to access the full “source code” of your product in any structured format. Despite most platforms assume low code, no code, or even AI agent-based development, the actual intellectual property rights (IPRs) that your development team creates during the the development process should be represented in text format, for example such as XML or JSON “configurations”. Besides being version control friendly, those text formats will allow you to have full control over migration to another platform or classic development stack in case of any critical situation. This won’t work with screenshots of your workflows or dashboards – the only real “code” that guarantees consistency is a machine readable set of configuration files that constitute your intellectual property. There should be nothing “non-exportable” – make sure to consult the vendor and double check.

However, for a 10 years old product with a serious revenue and customer base migration to another platform or stack would be a real stress or even a business continuity risk. But what if the platform vendor decides to discontinue the product or cease commercial support?

Such major cases must be clearly covered by your partner agreement, ensuring full legal support. For example, an acceptable solution for the above cases would be transfer of the platform’s source code to you, allowing you to further use it as an “open source” platform.

This, surely, doesn’t fully solve the problem as you’re getting the sources but the platform won’t further evolve on its own, using the vendor’s team. However, you get to a situation similar to a fully custom product developed by your team in-house – you have the full set of your product’s source code, and any IoT platform is just obviously based on the “commercial product friendly” open-source libraries and legally purchased commercial libraries whose evolution will continue.

The most complicated case to cover is financial insolvency and bankruptcy of the vendor. In this case you might not have anyone to provide your the platform’s source code and internal documentation – as the platform’s team might no longer exist!

The solution here requires deeper legal and financial expenses, so it’s normally implemented once you’re really got some serious dependency on the vendor. However, this risk mitigation opportunity should be an initial part of your partnership contract – otherwise you might get stuck with this in the future.

The key to protecting your IPRs is a software escrow – a mature and trusted third-party company that receives platform’s source code from the vendor and stores them securely according to a trilateral agreement. The escrow holder can even verify the completeness of the sources upon every update – this is quite a regular option. Escrow agreement’s release conditions should stipulate that the sources should be transferred to you in case of the vendor’s bankruptcy. This case brings you to a situation described earlier – you’re troubled but never stuck with full sources.

End User UI/UX Engineering Efforts

While platforms were designed to shorten the time-to-market, the second part of almost any IoT platform’s brand promise is development cost cut. This is enabled by low-to-no-code or AI-based development.

The biggest benefit of a platform-based development cost reduction is definitely laying in the frontend and UX implementation process. Being fully visual, it allows IoT industry experts to become “citizen developers”, effectively allowing them to create your product’s user interface.

Visual UI development process gives platform-based development a huge advantage, but make sure to check the selected platform’s capabilities. A mature platform must not limit your creativity – any templates prepared by your design/UX team should not result in “not doable” verdict from the implementation team. The worst case scenario usually assumes you can resort to implementation of a custom component using one of the JavaScript frameworks – and the platform should have proper frontend SDK for such a case.

The above concerns the web-based user interface. As for the mobile apps, if they are a part of your product, the development efforts are in clear parity since you will be using classic development in both cases and the apps will also communicate with the backend using the same regular API-based approach.

Administrative UI Engineering Efforts

When inexperienced teams are assessing SaaS application development costs, they are often delving into the application’s customer-facing UIs, forgetting to plan and estimate the administrative part of the product interface which is used to manage users, tenants, roles, plans, and any other app-specific business objects. This backend should also provide graphical real-time analytics on the product usage, self-monitoring checks, access to global app-level settings, integrations config, and much more.

Since the administrative UI is normally designed for internal users, the requirements to its UX and visuals are not so high. And this is where the IoT platforms give you a huge cost cut and development performance boost!

With a platform, you design most business objects of your app using the platform’s internal tools. The platform, therefore, provides all management interfaces out-of-the box, allowing your administrators and support teams to act immediately, without actually developing most of the administrative UI. They can configure alerts, verify and update email/SMS gateway settings, change plans, send special offers – and all this with zero or near-zero effort from the development team.

With a classic stack, you have to develop your administrative UI from scratch – spending your time and resources, often draining them of the customer-facing UI improvement budget.

Application Logic Engineering Efforts

Similarly to the frontend part, IoT platforms’ value proposition assumes the backend development costs are also lower.

You may object that the classic teams are all into vibe-coding now – and this means low code won’t bring any additional benefits. However, the vibe coding works really great if you have a very compact app, often with a single developer – such as a chat bot. Speaking about complex applications with a large code base (equivalent to millions of classic code lines), there’s a big difference in how your application logic is visually represented. When your core algorithms are visual you can quickly join new team members and they won’t spend too much time “loading” the code base and app’s high level design in their heads. At the same time, the IoT platforms have made equal progress in using AI agents to prepare the “code” – the only difference is that the output is visual and easily readable, greatly simplifying evolution and maintenance of applications with a large code base.

Multi-tenancy and Productization

Multi-tenancy is the point where an internal IoT solution either becomes a real market product – or starts to break under operational complexity. Building a single solution for one customer is relatively easy. Turning it into something that can be sold, deployed, upgraded, and supported across many customers is a fundamentally different challenge. This is where the gap between an IoT platform and a set of open-source IoT-related libraries and tools becomes obvious.

In open-source and IaaS-based approaches, multi-tenancy is usually added after the fact. Teams duplicate environments, databases, and deployments per customer, which works briefly but scales poorly. Over time this leads to fragmented upgrades, customer-specific forks, and growing fear of touching production systems. The result is a self-lock-in – where complexity becomes the main constraint on growth.

True productization requires multi-tenancy to be designed from day one. Shared runtime, a unified application and data model, and strict logical isolation between tenants allow teams to evolve products centrally while keeping customer-specific configuration under control. This makes predictable upgrades, secure operations, and reuse of business logic possible.

This architectural difference has direct economic consequences. Native multi-tenancy reduces marginal cost per customer, enables subscription pricing, and makes repeatable delivery realistic. Without it, most IoT “products” remain disguised projects – and scaling them only amplifies their weaknesses instead of their value.

Deployment, Scaling, and Operations

Deployment is where many IoT initiatives first collide with reality. Early prototypes often run in a single environment, manually deployed, tightly coupled to specific infrastructure choices. This works for demos, but it does not survive real customers, uptime expectations, or security audits. Once multiple environments, regions, and customers are involved, deployment stops being a technical task and becomes an operational discipline – one that must be designed into the platform, not improvised by each product team.

Scaling in IoT is rarely just about handling more messages per second. It is about scaling heterogeneity: more device types, more protocols, more data models, more tenants, and more operational scenarios. Open-source stacks and cloud toolkits tend to push this burden onto the product team, which must design, test, and operate its own Kubernetes topologies, data pipelines, and failover strategies. Over time, operational effort grows faster than product value, and engineering teams become infrastructure managers instead of product builders.

A platform-centric approach changes this balance. Deployment models, horizontal scaling, tenant isolation, monitoring, upgrades, and backup strategies are standardized and repeatable across environments – cloud or on-premises. This allows teams to focus on application logic and customer value rather than reinventing operational machinery. In practice, the ability to deploy consistently, scale predictably, and operate safely is what separates an IoT product that works in theory from one that can survive years of production use.

Security, Governance, and Compliance

Security in IoT is not a single feature, but a continuous process that spans devices, data, users, APIs, and operations. In real-world deployments, security requirements quickly expand beyond basic authentication and encryption to include role-based access control, auditability, tenant isolation, credential rotation, and compliance with internal or regulatory policies. When these concerns are addressed at the application level, they tend to be implemented inconsistently, varying from project to project and eroding trust over time.

This is one of the least discussed but most consequential differences between IoT platforms and do-it-yourself stacks. In a platform-based approach, the core runtime, frameworks, and dependencies are continuously maintained and updated by the platform vendor to address underlying library vulnerabilities, protocol changes, and new IT security trends. In a custom-built or open-source stack, that responsibility shifts entirely to the product team. Every security patch becomes a manual exercise: tracking CVEs, updating individual libraries, regression-testing the system, and redeploying safely. With just one of a few owner products, this creates silent risk over time, as outdated components accumulate simply because updating them is costly and disruptive.

Governance and compliance amplify this gap even further. Centralized user management, policy enforcement, audit logs, and controlled upgrade paths allow organizations to demonstrate compliance without turning each deployment into a bespoke exercise. Instead of relying on tribal knowledge and ad-hoc processes, teams gain repeatable, inspectable controls that scale with the product. In practice, security failures in IoT are rarely caused by bad intentions – they are caused by architectures that place too much long-term responsibility on individual projects instead of embedding security and governance into the platform itself.

Economics: CAPEX vs OPEX

The economic discussion around IoT platforms is often reduced to subscription cost, which obscures the real trade-offs. Classic, code-centric development typically starts with lower visible CAPEX: open-source components, “basic” cloud services such as virtual machines, and in-house engineering appear cheaper at the outset. Platforms, by contrast, introduce an explicit subscription cost early on. The fair comparison, however, is not between license fees and “free” software, but between where and when costs are paid over the full lifecycle of an IoT product.

In classic development, CAPEX quietly accumulates inside engineering and operations. Teams invest heavily in building foundational capabilities – security, device management, data modeling, multi-tenancy, deployment automation – that are not product differentiators but must still be designed, implemented, and maintained. As the solution grows, these sunk costs turn into long-term OPEX: ongoing refactoring, infrastructure tuning, security updates, and operational firefighting. What looked inexpensive at the start becomes structurally expensive to scale, because every new customer and feature increases both engineering and operational load.

Platforms shift this balance deliberately. More cost is visible and operational from day one, but less is hidden in bespoke engineering. Core platform capabilities are shared across customers and continuously improved, reducing duplicated effort and lowering marginal cost over time. For ISVs and OEMs aiming to build repeatable products rather than one-off solutions, this trade-off is often decisive: platforms convert unpredictable engineering CAPEX into more predictable and controllable OPEX, making long-term economics, pricing models, and growth planning far more sustainable.

Support: Community-only vs Community+Vendor

Support models are often framed as a choice between “open community” and “closed vendor”, but the reality is more nuanced. Classic development benefits from large open-source communities, extensive documentation, and public forums where most technical questions have already been asked and answered. This breadth of knowledge can be invaluable, especially during early development. At the same time, responsibility for diagnosing issues, validating fixes, and deciding whether a solution is production-safe always remains with the product team. Community help accelerates learning, but it does not replace ownership.

Vendor-supported platforms operate under a different but equally valid trade-off. While their public communities may be smaller, they combine shared knowledge with accountable support and predictable resolution paths. Issues affecting production systems, security, or upgrades are handled by teams who understand the platform’s internals and roadmap. For product teams, the real distinction is not community size, but risk distribution: community-driven stacks maximize flexibility and peer knowledge, while vendor-backed platforms reduce operational uncertainty by sharing long-term responsibility for stability, fixes, and evolution.

Monetization Model Freedom

Monetization is rarely a first-order concern during early IoT development, but it becomes critical the moment a solution turns into a product. Classic development offers maximum theoretical freedom: teams can design any pricing model, bundle features arbitrarily, and integrate custom billing logic as they see fit. The hidden cost is that monetization mechanics – tenant metering, feature entitlements, usage limits, trials, and upgrades – must be built and maintained alongside the product itself. Over time, pricing changes become engineering projects, not business decisions.

Platforms constrain monetization in more visible ways, but often enable it in practice. Built-in multi-tenancy, usage tracking, and role-based access make subscription tiers, per-tenant pricing, and feature gating far easier to implement consistently. Instead of inventing billing infrastructure, teams can focus on packaging value and experimenting with go-to-market strategies. The real freedom is not unlimited technical flexibility, but the ability to change how a product is sold without destabilizing how it is built and operated.

The bottom line is that the platform of your choice should be designed to enable any monetization model without restraining implementation of relevant functionality. At the same time it should technically simplify monetization of your product.

Outsourced Development Availability

Availability of external developers is an often overlooked but very practical factor when choosing an IoT technology stack. Classic, code-centric stacks clearly win on this dimension. Mainstream programming languages, popular cloud services, and widely used open-source frameworks give access to a vast global talent pool. Finding contractors or outsourcing teams is relatively easy, competitive, and fast, which reduces short-term hiring risk and makes it simpler to scale teams up or down as needed.

Platforms take a more specialized path. While mature platforms do have experienced integrators, certified partners, and even platform-specific developers available for hire, the pool is inevitably smaller. This is the trade-off for higher-level abstractions and stronger architectural consistency. In practice, platform-based teams often rely more on long-term partners or internal expertise, while classic stacks benefit from sheer market depth. The choice is less about availability versus absence, and more about whether you value immediate access to generic skills or a smaller pool of specialists who understand the platform deeply and can deliver faster within its constraints.

Grand Finals

There is no universal winner in the “IoT platforms vs open source” debate – but there is a clear pattern. Platforms consistently win where products are expected to scale, evolve, and be sold for years: speed, productization, operations, security, and governance. Classic development wins where maximum control, ecosystem breadth, and talent availability dominate the decision. The real mistake is not choosing one side – it is choosing without a long-term product lens.

If you are building an internal “lightweight” IoT solution or a small number of bespoke customer deployments, classic development is often the most rational choice. If you are building a mission-critical industrial IoT solution or commercial IoT product, especially with multi-tenancy, subscriptions, upgrades, and a roadmap measured in years, platforms quietly remove entire classes of risk and cost that only become visible too late.

The summary below is not a verdict – it is a mirror. Use it to challenge your assumptions before your architecture hardens and your options narrow.

Dimension	Winner
Generic long-term benefits	IoT platforms
Time-to-PoC / MVP / Market	IoT platforms
Performance & scalability	Parity
Vendor lock-in vs self-lock-in	Open source
End-user UI/UX engineering	IoT platforms
Administrative UI engineering	IoT platforms
Application logic engineering	IoT platforms
Multi-tenancy & productization	IoT platforms
Deployment, scaling & operations	IoT platforms
Security, governance & compliance	IoT platforms
Economics (CAPEX vs OPEX)	Parity
Support (community vs vendor)	Parity
Monetization model freedom	IoT platforms
Outsourced development availability	Open source

Final takeaway:
Open source optimizes for freedom today.
IoT platforms optimize for survival tomorrow.