As our clients manage more connected devices than ever before – sensors, trackers, and more spread across cities, factories, or remote locations – DMC is often asked: How do I track and manage these assets in a centralized way?
Cloud platforms have emerged as a powerful foundation for tracking IoT assets in real time. By combining IoT connectivity with cloud-based data processing and analytics, businesses can monitor device location, health, and performance throughout the entire asset lifecycle. This approach not only improves operational efficiency but also enhances security, reliability, and decision-making in increasingly connected environments. Here, DMC discusses various approaches for asset tracking in the field.
Options for Tracking Assets in the Cloud
A variety of tools exist to support an organizational strategy for cloud-based asset management. Since this is a broad solution category, with requirements differing from use case to use case, the best structure for a particular organization can vary. Different platforms present tradeoffs in how they model assets, where they run, and how much responsibility they place on engineering teams.
Open-Source and Self-Managed IoT Platforms
Open-source platforms sit at the foundation layer of IoT. They provide the essential capabilities—device connectivity, telemetry ingestion, rules engines, and dashboards—without prescribing how your asset tracking solution should look or behave.
These tools appeal to teams that want full control over their data, infrastructure, and deployment model. If your assets don’t fit neatly into predefined schemas or your workflows are non-standard, these platforms can provide the building blocks to model assets exactly the way your business requires. They’re also attractive for organizations that want to avoid vendor lock-in or support hybrid and multi-cloud deployments.
The upside of this flexibility is transparency and extensibility. You can inspect the architecture, customize nearly every layer, and keep licensing costs relatively low at small to medium scale. The trade-off, however, is ownership: scaling, securing, and operating the platform requires in-house expertise or professional support.
At their core, open-source platforms act as a foundation on which to build your solution, not a finished service you simply turn on.
Examples: Thingsboard, EdgeX Foundry, KubeEdge, and fully custom implementations.
Cloud Provider IoT Platforms
Large cloud providers offer IoT tooling that supports a different approach, with different trade-offs. Rather than focusing on the asset-focused elements of a solution, the core IoT offerings from these large providers tend to focus on doing a few things extremely well: secure device connectivity, massive-scale messaging, and deep integration with cloud services.
Asset tracking on these platforms emerges by composing multiple services together—IoT hubs, databases, analytics tools, visualization layers, and automation pipelines. This model works particularly well for large-scale deployments with unpredictable growth, especially when the organization is already heavily invested in a specific cloud ecosystem.
The strengths here are hard to ignore: proven global scalability, mature security and identity management, and seamless access to cloud-native analytics, AI, and automation services. The trade-offs are mostly architectural. Asset management is not turnkey, pricing models can be complex, and teams must make careful design decisions across many interconnected services.
In short, building your solution with these tools and infrastructure can provide a lot of power and configurability, but the usefulness of the asset tracking solution is defined by how thoughtfully you assemble the stack on top of it.
Examples: AWS IOT and Azure IoT Hub.
Edge-First and Hybrid IoT Platforms
It is also worth noting that there are both open-source and large cloud-provider-based options that focus on the edge elements of these solutions. These platforms shift asset tracking closer to where assets actually operate—factories, vehicles, and remote sites. Rather than relying on sending everything to the cloud, these systems enable local processing, decision-making, and resilience in environments with low-latency or intermittent connectivity.
This model is especially valuable for safety-critical systems, bandwidth-constrained scenarios, or assets that must continue operating when disconnected. By reducing cloud dependency, edge platforms align more closely with real-world operating conditions.
The trade-off is complexity. Edge deployments require device management strategies, careful orchestration, and are often paired with a cloud platform rather than used standalone.
Edge-first platforms ultimately redefine asset tracking as distributed by nature, not purely cloud-centric.
Examples: AWS Greengrass, Azure IoT Edge, and EdgeX Foundry.
Industrial and Enterprise Asset Platforms
Companies focused on manufacturing, industrial, or enterprise applications offer platforms for managing assets that are tailored towards the idea that assets are not just devices emitting data, but long-lived physical entities with maintenance histories, operational constraints, and regulatory requirements.
These platforms are common in manufacturing, utilities, energy, and transportation, where assets are expensive, critical, and expected to operate for decades. They excel at rich asset modeling and digital twins, and they often include built-in workflows for maintenance, reliability, and operations. Tight integration with ERP, EAM, and OT systems makes them especially powerful in asset-heavy enterprises.
That depth comes at a cost. Implementations tend to be expensive and time-consuming; the solutions are frequently limited in their application, with minimal or limited flexibility for experimental or non-industrial use cases.
Here, asset tracking is framed as part of operational activities, rather than merely a data collection exercise.
Examples: Siemens MindSphere, IBM Maximo, and ThingWorx.
Managed IoT and Asset Tracking
Finally, a wide range of companies offer SaaS or PaaS products in the asset management space. For the most part, these products focus on one thing above all else: reducing complexity. They aim to deliver end-to-end asset tracking solutions with minimal setup, prebuilt dashboards, and opinionated workflows that cover the most common use cases.
These tools are well-suited for teams that want faster time-to-value, small to mid-scale deployments, or lack the resources to manage complex cloud architectures. The experience is typically streamlined, pricing is clearer, and operational overhead is much lower than with self-managed platforms.
The pain points with these products show up as deployments grow or requirements become more specialized. Customization options can be limited, advanced analytics may be constrained, and vendor lock-in becomes a real consideration.
These platforms prioritize speed and reduced complexity over architectural freedom, which can be limiting depending on the application.
Custom Development Support for IoT Asset Tracking
There’s no single “best” IoT asset tracking platform—only platforms that align more closely with your organization’s scale, requirements, and data handling priorities. Additionally, regardless of the platform selected, some configuration and customization are required to make sure the solution works for your use case. In these cases, support from a team experienced in implementation can make all the difference in delivering a complete, useful tool for your business.
Infrastructure and System Setup
Centralizing asset tracking requires setting up the necessary cloud infrastructure for managing telemetry from devices, running analytics, and providing user access to data. Working with a team familiar with the tooling and components required helps you to get up and running sooner, with a solution that balances robustness and cost considerations for your specific use case.
Dashboard Development or Configuration
Much of the value of cloud-based asset management comes from the ability to monitor device status and to run analytics on incoming telemetry. Visualization is an important component of making this data accessible to users, and working with a team familiar with a wide variety of available options ensures you get a result that matches your needs for user accessibility, brand look and feel, and ability to postprocess the data. This might include UI/UX support, customization of existing tooling, integration with other applications, BI dashboard development, or custom web portal development.
Edge Device Customization, Connectivity, and Firmware
Whether you are looking to expand your devices’ connectivity or update the capabilities to better support your centralized tracking and management, working with a proven team can ensure the effort is completed to a high technical standard without sacrificing cost and schedule efficiency. This could include hardware updates or a new design to improve device connectivity to the cloud, updating data reporting portions of device firmware to best take advantage of the configurability and visibility afforded by centralized asset management, or implementing Over-the-Air (OTA) updates for your device.
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