Many IoT architectures are designed around a cloud provider’s built-in device platform. That pattern exists in several clouds, but not in Google Cloud.
Google once offered a managed service called Cloud IoT Core, but the product was discontinued in 2023. Since then, Google has taken a different approach. Instead of providing its own IoT platform layer, it focuses on data processing, analytics, and AI services while relying on specialized partners to handle device connectivity and operational applications.
This makes Google Cloud a natural environment for platforms such as Iotellect.
A Data-first Cloud
Google Cloud is widely used for large-scale data processing. Services such as Pub/Sub, BigQuery, and Vertex AI are designed to ingest, process, and analyze massive data streams.
What the platform does not attempt to provide is the full operational layer required for IoT systems. Device connectivity, asset modeling, dashboards, automation, and user interaction are expected to come from external platforms.
Iotellect fills this gap by providing the application layer that sits between devices and the cloud’s analytical services.
The Role of Iotellect
In a typical architecture, devices communicate with Iotellect, which handles protocol support, data normalization, real-time processing, and application logic. Once data becomes structured operational information, it can be forwarded to Google Cloud services for long-term storage or advanced analysis.
This separation works well because it allows each system to focus on what it does best. Iotellect manages devices and operational workflows. Google Cloud processes and analyzes data at scale.
Another option is using Iotellect for full-stack app implementation, using only basic GCP services such as virtual machines.
Deploying Iotellect on Google Cloud
Iotellect runs on standard Google Cloud infrastructure and can be deployed in several ways depending on the environment. It may run on Compute Engine virtual machines or on Google Kubernetes Engine for containerized deployments. Storage services and networking components are used in the same way they would be for any other enterprise application.
Because the platform itself is cloud-agnostic, Google Cloud remains an infrastructure choice rather than a dependency. The same architecture can be replicated in other environments if needed.
Working with Google Cloud Services
Once operational data is available inside Iotellect, it can easily be streamed or exported to Google Cloud services. Pub/Sub can be used to move telemetry streams, BigQuery can store large historical datasets, and Vertex AI can apply machine learning models to detect anomalies or predict equipment behavior.
In this model the IoT platform prepares structured operational data, while the cloud platform performs large-scale analysis and machine learning.
Why Better Together
Google Cloud was built as a data and AI platform. Iotellect was designed as an IoT application platform. When used together they create a clean architecture where operational logic and data processing are separated but tightly connected.
Devices, users, and automation workflows live in Iotellect. Analytics, large datasets, and machine learning models live in Google Cloud. The result is a flexible architecture that takes advantage of Google’s strengths without requiring a proprietary IoT hub.
In this environment, Google Cloud becomes the analytical engine behind IoT systems, while Iotellect provides the operational layer that turns connected devices into working applications.