This comparison starts with a fact that changes its shape: Google Cloud IoT Core was retired on August 16, 2023, and Google has not introduced a first-party replacement. Google Cloud remains a serious option for IoT workloads, but today it means building on general-purpose primitives rather than adopting an IoT product. That makes the comparison less “platform vs. platform” and more “build vs. adopt.”
Google Cloud’s current IoT guidance, documented in its Architecture Center, describes three patterns: run a standalone MQTT broker (EMQX, HiveMQ, or Mosquitto) on Compute Engine or GKE and bridge it to Pub/Sub; use a partner IoT platform, with ClearBlade IoT Core available on the Google Cloud Marketplace as an API-compatible successor to the retired service; or connect devices directly to Pub/Sub over HTTPS/gRPC for simple ingestion. Downstream, the data stack is a real strength: Pub/Sub into Dataflow, BigQuery, and Vertex AI is one of the strongest analytics pipelines available anywhere.
TagoIO is a full-stack IoT platform: device connectivity over MQTT and HTTPS with 500+ pre-built connectors, LoRaWAN through network server integrations, time-series storage, dashboards, serverless Analysis scripts in Node.js, Deno, or Python, Actions for rules and notifications, and TagoRUN for white-label end-user portals. It is an adopted product rather than an assembled architecture.
The device layer
On Google Cloud, the device-facing layer is now yours to choose and operate. Pub/Sub has no MQTT endpoint, so MQTT devices need a broker you run or a partner platform you license. Device identity, registry, provisioning, OTA updates, and state management all come from that partner layer or your own build. Teams that already operate Kubernetes and want control over the broker can do this well; teams that expected an IoT Core-style managed front door now assemble one.
TagoIO’s device layer is part of the product: MQTT and HTTPS endpoints, device tokens, payload parsers, a device emulator, QR-code provisioning, and the Live Inspector for watching device traffic in real time. LoRaWAN, Sigfox, and satellite devices connect through maintained integrations with network providers, with network usage free on all plans.
Dashboards, applications, and users
Google Cloud’s answer to visualization is Looker, Looker Studio, or Grafana over BigQuery, which serves internal analytics well. Customer-facing applications, user management, and any multi-tenant portal are custom software projects.
TagoIO includes drag-and-drop dashboards, Blueprint dashboards that scale one layout across fleets, and TagoRUN, which turns a project into a branded portal with your domain, user signup, access policies, and an optional mobile app under your name. For system integrators delivering applications to clients, this is the difference between configuring and contracting a frontend team.
Analytics and custom logic
If your project’s center of gravity is large-scale data analytics or machine learning, Google Cloud’s Dataflow, BigQuery, and Vertex AI stack is hard to argue with, and it is a common reason teams choose GCP regardless of how devices connect.
TagoIO handles the analytics most IoT applications need, aggregation, alerting logic, scheduled reports, integrations, and forecasts and predictions from telemetry, through Analysis scripts and Actions inside the platform, and its API supports exporting data onward when a heavier pipeline is justified. The two are not mutually exclusive: TagoIO can sit as the device and application layer feeding curated data into BigQuery.
Pricing model
Google Cloud bills each primitive by usage: Pub/Sub by data volume, Dataflow by compute, BigQuery by storage and queries, plus the separate cost of the MQTT broker infrastructure or partner platform license. Cost tracks architecture, and estimating it requires designing the architecture first.
TagoIO prices by plan plus service usage: free tier with 5 devices, Starter at $49/month, Scale at $199/month, and dedicated TagoDeploy instances from $850/month, with usage measured in data transactions, storage, Analysis minutes, notifications, and end users.
The bottom line
Google Cloud makes sense when the organization is GCP-centric, device connectivity is simple or already solved, and the value of the project lives in downstream analytics and ML on BigQuery and Vertex AI.
TagoIO fits when you want a maintained device layer and a finished application layer without operating brokers or building portals, which is the gap Google’s own architecture guidance now fills with partners. Some teams use both: TagoIO for devices, dashboards, and end users, Google Cloud for warehouse-scale analytics behind it.