The ThingsBoard Cloud is a fully managed, scalable and fault-tolerant platform for your IoT applications. ThingsBoard Cloud is for everyone who would like to use ThingsBoard Professional Edition but don’t want to host their own instance of the platform.
Platform supports all Professional Edition features.
Save time on maintenance of the platform or configuration of the features.
The cost of the cluster infrastructure is shared between the users of the platform.
Open-source IoT platform for device management, data collection, processing and visualization. Read More.
Define data processing rules and trigger reactions using powerful rule engine components.
Construct your ThingsBoard cluster and get maximum scalability and fault-tolerance with new microservices architecture.
Advanced IoT Platform distribution based on latest stable open-source version with value added features. Read More.
Multi-tenant configurable white-labeling.
Custom entity groups (devices, assets, etc.) with customizable actions and configurable columns.
Advanced management of user roles and permissions. Manage hierarchy of customers.
Export any dashboard widget data to CSV or XLS format.
Out of the box integrations with popular IoT platforms and connectivity providers.
It allows bringing data analysis and management to the edge, where the data is created. At the same time ThingsBoard Edge seamlessly synchronizing with the ThingsBoard cloud (ThingsBoard Cloud, ThingsBoard Demo, ThingsBoard PE or ThingsBoard CE) according to your business needs. Read More.
Process and store data from edge (local) devices without connection to the cloud. Push updates to the cloud once connection restored.
Filter data from edge (local) devices on the ThingsBoard Edge service and push to cloud only subset of the data for further processing or storage.
The ThingsBoard Trendz is an Analytics Platform that converts the IoT dataset into insights and simplifies the decision-making process. Read More.
Plan and optimize operations with insights into future events and system behavior.
Detect anomalies with automated tools based on built-in machine learning algorithms. Prioritise them and focus on real problems with anomaly scoring.