Why Building IoT In-House Might Fail: The Hidden Costs Machinery OEMs Don't See When They Build vs. Buy

Published by Guy Weitzman

Every year, OEMs invest millions in building proprietary IoT monitoring platforms for their equipment, and the majority of those projects either stall, go over budget, or deliver a product that can't compete with purpose-built alternatives. The engineering teams behind them aren't the problem. They're typically excellent. The problem is that building an industrial IoT platform is a fundamentally different discipline than building industrial equipment, and the gap between those two disciplines is where budgets, timelines, and competitive advantage go to disappear.

I've had this conversation with dozens of OEM engineering leaders over the past several years. It usually starts the same way: "We're thinking about building our own IoT monitoring into our equipment. How hard can it be?"

I understand the impulse. You already design complex mechanical and electrical systems. You know your equipment better than anyone. Adding sensors and connectivity feels like a natural extension of what you already do. And the business case is compelling: equipment monitoring creates recurring revenue, strengthens customer relationships, and differentiates your product line.

But after watching multiple OEMs go down this path, I can tell you: the ones who succeed almost never build it themselves. Not because they lack engineering talent. Because the costs that matter most are the ones that don't show up in the initial project plan.

How Much Does It Actually Cost to Build an IoT Platform In-House?

When OEM teams scope an IoT build, they typically focus on what's visible: a sensor, a microcontroller, a connectivity module, a cloud dashboard. These are tangible engineering deliverables. Your team can spec them, prototype them, and build them.

What sits below the waterline is what kills these projects.

The visible costs (hardware development, firmware, a basic cloud platform) represent maybe 20-30% of the total investment required to deliver a production-grade IoT monitoring solution. The rest is ongoing: firmware maintenance, cellular carrier management, security patching, cloud infrastructure scaling, device lifecycle management, and the organizational overhead of supporting a technology platform that's fundamentally different from your core product.

I'm not speculating here. This pattern plays out consistently because the technical domains involved in industrial IoT are genuinely distinct from the domains most OEMs operate in, even highly sophisticated ones.

Why Is Embedded Firmware So Much Harder Than OEMs Expect?

Let's start with something that seems straightforward: embedded firmware.

Your team builds a sensor node. It reads vibration and temperature, connects via Bluetooth or cellular, and transmits data. The prototype works great on the bench. Maybe it even works well in initial field trials.

Then reality sets in.

Edge computing, the practice of processing sensor data on the device itself rather than streaming everything to the cloud, is where the real engineering complexity lives. Writing algorithms that reliably distinguish a failing bearing from normal operational variation, and doing it on a resource-constrained microcontroller with limited memory and processing power, is a specialized discipline. It's not the same as writing application software or even traditional embedded code.

You need to handle threshold-based alerting with configurable parameters that can be updated remotely. You need FFT (Fast Fourier Transform) processing on-device if you're doing vibration analysis, which means optimizing DSP algorithms for low-power processors. You need to manage power states intelligently, keeping the sensor in deep sleep to preserve battery life while still capturing transient events that might indicate equipment failure.

Then there's the firmware update problem. Every device you ship needs to be remotely updatable. Securely. Without bricking units in the field. Over potentially unreliable cellular connections. With rollback capability if something goes wrong. This isn't a feature you bolt on. It's an architecture you design from the ground up, and getting it wrong means sending technicians to physically touch every device you've deployed.

Most OEM teams estimate firmware development at 6-12 months. In practice, reaching production-quality firmware with reliable OTA (over-the-air) updates, robust power management, and accurate edge analytics takes 18-36 months. And that's before the first customer deployment reveals environmental conditions your lab testing didn't anticipate.

What Makes IoT Connectivity Management So Complex for OEMs?

The second hidden cost is connectivity management, and it's one that consistently surprises engineering teams who haven't built cellular IoT products before.

Choosing a cellular module and getting it to transmit data is the easy part. Managing a fleet of cellular connections in production is an entirely different discipline.

You'll need carrier relationships, and not just one. Different facilities, different geographies, and different RF environments mean you need multi-carrier SIM management to ensure reliable connectivity. That means negotiating carrier agreements, managing SIM provisioning and activation, handling billing across carriers, and building systems to automatically switch carriers when one provides inadequate coverage at a particular site.

Then there's the question of network architecture. Most OEMs default to putting sensors on the customer's WiFi network, which creates an immediate dependency on customer IT infrastructure. Every deployment becomes an IT project. Every facility requires network surveys, firewall configurations, VLAN setup, and ongoing coordination with IT teams who have their own priorities and security requirements.

This is precisely why we built Atomation's system to operate on its own cellular connectivity with a battery-powered gateway. It wasn't because cellular is technically superior to WiFi in every dimension. It's because eliminating the dependency on customer network infrastructure removes the single biggest deployment bottleneck in industrial IoT. We learned this the hard way by watching how deployments stall when they require IT involvement.

An OEM building in-house will face this same discovery, typically after they've already committed to a WiFi-dependent architecture.

Why Do OEMs Underestimate the Cloud Platform Investment?

Let's say your team gets through hardware and firmware. Now you need a cloud platform.

The initial build seems manageable: ingest sensor data, store it in a time-series database, build a dashboard, send alert notifications. A competent full-stack team can prototype this in a few months.

But a production IoT platform is not a web application with sensor data. It's a real-time data pipeline that needs to handle intermittent connectivity, out-of-order messages, duplicate transmissions, device authentication, and data integrity validation, all at the same time.

You need device management: provisioning, configuration, grouping, firmware version tracking, health monitoring for the monitoring system itself. You need multi-tenancy if you're selling to multiple customers, which means data isolation, role-based access control, and per-customer configuration. You need an API, not as an afterthought, but as a core architectural component, because your customers will want to integrate monitoring data with their CMMS, ERP, and analytics systems.

At Atomation, our REST API and the integration patterns it supports represent years of iterative development based on real enterprise integration requirements. We've built interactive API documentation, standardized authentication flows, and comprehensive data access endpoints because our customers told us, repeatedly, that trapped data is nearly as useless as no data at all. An OEM building from scratch will face every one of these same requirements, but they'll discover them sequentially, each one requiring architectural changes to a platform that wasn't designed to accommodate them.

And here's the cost that truly catches OEMs off guard: cloud infrastructure isn't a one-time expense. It's an ongoing operational commitment. Server costs, database scaling, security monitoring, uptime management, compliance certifications. These don't go away after launch. They grow as your installed base grows. You're not just building a product; you're committing to operating a technology service, permanently.

How Does IoT Security Change the Build vs. Buy Equation?

Industrial IoT security deserves its own discussion because it's the hidden cost with the highest potential consequences.

When your sensor connects to a customer's equipment, you've created an attack surface. If your device communicates over the customer's network, you've extended that attack surface into their infrastructure. The security implications are substantial, and they extend well beyond encrypting data in transit.

You need secure boot processes on your hardware. You need encrypted firmware updates with certificate validation. You need device attestation, the ability to verify that the device connecting to your cloud is actually your device and not a spoofed endpoint. You need network segmentation strategies, key management infrastructure, and incident response procedures.

One of the architectural advantages we recognized early at Atomation is that our cellular-based, infrastructure-independent approach provides inherent network isolation. Our sensors don't touch the customer's network, which means they can't serve as an entry point into the customer's systems. This wasn't primarily a security decision. It was a deployment simplicity decision. But the security benefit is significant. It eliminates an entire category of attack vectors that WiFi-connected sensors introduce.

An OEM building on WiFi will need to invest heavily in security architecture to mitigate these risks, and their customers' IT security teams will scrutinize every aspect of the implementation before approving deployment. This review process alone can add months to each customer engagement.

What Is the Real Opportunity Cost of Building IoT In-House?

Beyond the direct engineering costs, there's a strategic cost that rarely appears in the project justification: opportunity cost.

Every engineer working on your IoT platform is an engineer not working on your core product. Every product management cycle spent on firmware updates and cloud infrastructure is a cycle not spent on the mechanical and electrical innovation that differentiates your equipment in the market.

OEMs win by building the best equipment in their category. The companies that try to simultaneously become IoT platform companies typically end up with a mediocre platform bolted onto a product line that's lost its development momentum.

The math usually works out the same way. Building in-house costs 3-5x the initial estimate, takes 2-3x longer than planned, and delivers a platform that's perpetually behind dedicated IoT providers in capability, reliability, and security. Meanwhile, a white-label or embedded partnership with a purpose-built platform gets you to market faster, with a more mature product, at lower total cost.

What Do Successful OEMs Do Instead of Building In-House?

The OEMs I see executing successfully on IoT-enabled equipment share a common approach: they focus on what they know best (their equipment, their customers, their domain expertise) and partner with dedicated IoT platform providers for the technology layer.

This isn't about admitting defeat. It's about recognizing that industrial IoT is its own discipline, with its own hard-won expertise, and that the fastest path to a great product is combining strengths rather than rebuilding from scratch.

A well-designed partnership gives the OEM full brand ownership. The monitoring appears as their product, their interface, their value proposition. The customer never needs to know or care about the underlying technology platform. But behind the scenes, the OEM gets battle-tested firmware, reliable connectivity management, a scalable cloud platform, enterprise-grade APIs, and a security architecture that's been refined through years of production deployments.

The alternative is two years of development, millions in investment, and a V1 platform that still lacks the maturity of existing solutions. That's a path I've watched too many engineering teams walk. The ones who arrive at the other end successfully are the exception, not the rule.

The Real Question

The question for OEMs isn't whether they can build an IoT platform. Talented engineering teams can build almost anything given enough time and resources.

The question is whether they should.

When the true cost is understood (not just the development budget, but the firmware maintenance, the connectivity management, the cloud operations, the security architecture, and the opportunity cost of diverted engineering resources) the answer almost always points toward partnership rather than building.

Your customers want smart, connected equipment. They don't care who built the sensor firmware. They care that the monitoring works, the alerts are accurate, and the data integrates with their systems. Getting them there faster, more reliably, and at lower cost is what smart OEMs do. And that usually means letting IoT specialists handle the IoT, so you can focus on what makes your equipment the best in its class.