As the Internet of Things (IoT) matures, the ecosystem has become increasingly complex, and so has the terminology. One of the most misunderstood aspects of the IoT stack is the role and function of IoT platforms. The term “IoT platform” is often used as a catch-all, but not all platforms serve the same purpose in practice.
To clarify, we can categorize IoT platforms into five distinct types, each serving a specific layer in the IoT value chain. These are:
1. IoT Connectivity Management Platforms (CMPs)
Primary Role:
Manage and orchestrate the network connectivity of IoT devices, especially in cellular or LPWAN environments.
Key Features:
- SIM lifecycle management
- Data plan monitoring and control
- Roaming and multi-network support
- Real-time network diagnostics
- Integration with telecom operators
Best Suited For:
Telecom providers, MVNOs, and large-scale deployments that involve thousands of devices across geographies using mobile or LPWAN technologies.
Example Use Cases:
- Smart metering via NB-IoT
- Connected vehicles with roaming SIMs
- Global asset tracking with eSIMs
Note: CMPs do not handle data visualization, device logic, or application development—they focus strictly on connectivity infrastructure.
2. IoT Device Management Platforms
Primary Role:
Enable secure IoT device provisioning, configuration, monitoring, and firmware management.
Key Features:
- Remote device onboarding
- Over-the-Air (OTA) firmware updates
- Device health monitoring
- Authentication and security management
- Lifecycle tracking
Best Suited For:
OEMs, device makers, and solution providers who need to maintain large fleets of heterogeneous devices over time.
Example Use Cases:
- Managing sensors in industrial IoT networks
- Updating firmware in smart home devices
- Health checks in smart city deployments
Note: Device management platforms focus on the “things” in IoT, not the data they generate or the applications they feed into.
3. IoT Application Enablement Platforms (AEPs)
Primary Role:
Serve as a middleware layer that enables rapid development of IoT applications by providing tools to collect, process, and visualize data.
Key Features:
- Device data ingestion and normalization
- Dashboard creation and data visualization
- Rule engines and workflow automation
- Integration APIs for third-party systems
- Multi-tenancy and user management
Best Suited For:
System integrators, software developers, and enterprises that want to build end-to-end solutions quickly without building their backend infrastructure.
Example Use Cases:
- Cold chain monitoring with real-time alerts
- Smart energy usage dashboards
- Environmental sensing in agriculture
Note: AEPs are the most flexible and business-centric platforms, often forming the foundation for solution accelerators and vertical-specific applications.
4. IoT Data Platforms
Primary Role:
Focus on data storage, processing, and analytics, acting as the central repository for massive volumes of time-series sensor data.
Key Features:
- Scalable, cloud-based data lakes
- Time-series database optimization
- Data filtering and aggregation
- Export to BI or ML tools
- Long-term data retention policies
Best Suited For:
Organizations that generate large volumes of IoT data must perform historical or real-time analysis.
Example Use Cases:
- Predictive maintenance in manufacturing
- Climate trend analysis in agriculture
- Energy usage reporting for compliance
Note: These platforms are strong in data handling but often lack application logic or device interaction capabilities.
5. IoT Analytics Platforms
Primary Role:
Apply advanced analytics and machine learning on IoT data to extract actionable insights and predictions.
Key Features:
- Anomaly detection
- Predictive analytics
- Real-time data streams analysis
- Data visualization with actionable KPIs
- Integration with AI/ML pipelines
Best Suited For:
Data scientists, analytics teams, and business analysts focused on optimizing operations or building intelligent IoT applications.
Example Use Cases:
- Predicting equipment failure
- Behavioral analytics in smart buildings
- Dynamic traffic prediction in smart cities
Note: Analytics platforms usually assume data has already been collected, cleaned, and stored—thus, they operate on or alongside data platforms.
Key Takeaway:
Each type of IoT platform addresses a different stage in the IoT lifecycle—from connecting devices to managing them, building applications, storing data, and analyzing them.
| Platform Type | Focus Area | Primary Users | Example Use Case |
|---|---|---|---|
| Connectivity Management | Network and SIMs | Telecoms, MVNOs | Global SIM tracking |
| Device Management | Device lifecycle | OEMs, Hardware Vendors | Firmware updates for smart meters |
| Application Enablement | App development | Integrators, Developers | Smart city dashboards |
| Data Platform | Data storage | Enterprises, Data Engineers | Compliance & audit trails |
| Analytics Platform | AI & Insights | Analysts, Data Scientists | Predictive maintenance in factories |
Understanding these categories allows organizations to select the right combination of platforms based on their business goals and technical needs—rather than relying on one-size-fits-all solutions.
Here’s a table showing examples of companies that represent each of the 5 IoT platform categories, based on the classification from the VelosIOT article:
| IoT Platform Category | Primary Focus | Example Companies |
|---|---|---|
| 1. IoT Connectivity Management Platforms | SIM management, cellular/LPWAN connectivity | – VelosIOT (formerly JT IoT) – Cisco Jasper – 1NCE – EMnify |
| 2. IoT Device Management Platforms | Provisioning, OTA updates, device security | – Microsoft Azure IoT Hub – Bosch IoT Suite – ARM Pelion – Amazon AWS IoT Device Management |
| 3. IoT Application Enablement Platforms (AEPs) | Build, deploy, and manage IoT applications | – FAVORIOT – Losant – ThingWorx (PTC) – Blynk – Ubidots |
| 4. IoT Data Platforms | Store, process, and integrate large data sets | – Google Cloud IoT Core (deprecated but illustrative) – AWS IoT Analytics – InfluxDB – Databricks (for IoT analytics) |
| 5. IoT Analytics Platforms | Analyze IoT data using AI/ML | – IBM Watson IoT – SAP Leonardo IoT – ThingSpeak (MathWorks) – Hitachi Lumada |
Notes:
- Some platforms span multiple categories, but each has a core strength that defines its primary classification.
- FAVORIOT fits best as an AEP, offering the backend infrastructure for developers to build and manage IoT applications.
- Companies like Azure or AWS may appear in multiple categories due to their broader ecosystems.
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