Picking a cloud platform for your IoT project feels like it should be straightforward. All three major providers offer IoT services. All three handle device connectivity, data ingestion, and analytics. On paper, they look almost identical.
In practice, they are built for very different starting points, and choosing the wrong one early on creates migration headaches that compound for years.
The right choice depends less on feature checklists and more on where your team already stands and where your product is heading.
Here is how the three actually differ when you look past the marketing pages.
AWS IoT: The Broadest Ecosystem
AWS has the widest selection of IoT-specific services. AWS IoT Core handles device connectivity over MQTT and HTTPS. IoT Greengrass pushes compute to the edge. IoT SiteWise is purpose-built for industrial data. IoT TwinMaker gives you digital twin capabilities.
Where AWS shines are flexibility. You can stitch together exactly the pipeline you need Kinesis for streaming, Lambda for serverless processing, Timestream for time-series storage, and S3 for raw archival. The building blocks are all there.
Spinning up a basic IoT thing takes one CLI command:
aws iot create-thing --thing-name "sensor-01"
The tradeoff? Complexity. AWS gives you options, not opinions. If your team does not have strong cloud architecture experience, the number of services and configuration choices can slow you down rather than speed you up.
Best fit: Teams with existing AWS infrastructure, complex multi-service architectures, or industrial IoT use cases needing SiteWise and Greengrass.
Azure IoT: The Enterprise Path
Azure IoT Hub is Microsoft's core offering device management, message routing, and tight integration with the rest of the Azure ecosystem. IoT Edge handles edge deployments. Digital twins provide modeling capabilities. And Microsoft Defender for IoT covers security monitoring across your device fleet and network layer.
Creating an IoT Hub is just as quick:
az iot hub create --name my-iot-hub --resource-group my-rg --sku S1
What makes Azure different is the enterprise integration story. If your organization already runs Microsoft 365, Active Directory, Power BI, and Dynamics, Azure IoT slots into that world without friction. Data flows straight from IoT Hub into Azure Stream Analytics, Cosmos DB, or Power BI dashboards your business team already knows how to use.
Best fit: Enterprises already invested in the Microsoft ecosystem, teams that need tight integration with business intelligence tools, and organizations where IT governance favours a single-vendor stack.
Google Cloud IoT: The Data and AI Play
Google shut down its standalone IoT Core service in August 2023, announced a year earlier in August 2022, which caught a lot of teams off guard. But that does not mean Google Cloud is out of the IoT conversation. The approach shifted.
Google now positions Pub/Sub as the ingestion layer, Cloud Functions or Cloud Run for processing, BigQuery for analytics, and Vertex AI for machine learning on device data. Partner solutions like ClearBlade fill the device management gap that IoT Core left behind.
Where Google Cloud genuinely leads data analytics and ML. If your IoT product value proposition depends on what you do with the data, BigQuery and Vertex AI are hard to beat.
Best fit: Teams building data-heavy or ML-driven IoT products, organizations already using BigQuery or TensorFlow, and projects where analytics is the core differentiator rather than device management.
Quick Comparison
So How Do You Actually Choose?
Forget feature comparison tables beyond the basics. Ask these three questions instead:
Where does your team live? If your infrastructure runs on AWS, starting an IoT project on Azure creates unnecessary friction. Platform familiarity saves months.
What matters more, device management or data intelligence? AWS and Azure lead on device lifecycle management. Google Cloud leads on what happens after the data lands.
How much do you want to assemble yourself? AWS gives you the most pieces. Azure gives you the most guided path. Google gives you the strongest analytics foundation but expects you to bring your own device layer.
There is no universal answer. There is only the answer that fits your team, your existing stack, and the product you are building.
Promeraki works with companies navigating exactly this decision from platform selection through to production-grade IoT architectures. If you are weighing your options, it is worth a conversation.
Which cloud platform are you running your IoT workloads on, and what would you change if you were starting fresh today?
