Qubitro vs TagoIO
A full-stack IoT platform with a substantial custom-code and application layer on top of device management.
TagoIO and Qubitro overlap heavily: device management, time-series storage, dashboards, alerting and white-label end-user portals. Either will get a fleet of sensors reporting into a branded application.
They differ in centre of gravity. TagoIO leans toward building an application — its Analysis runtime lets you write and run substantial server-side logic, and its dashboard and user-management layer is designed for shipping a product to end users. Qubitro leans toward time-to-first-dashboard: pick a device template, get a working decoder and dashboard immediately, then add logic where you need it.
One difference worth stating plainly: TagoIO ships a generally available MCP server for AI assistants. Qubitro's is in early access. If connecting an LLM to your IoT data is a requirement today rather than a plan, that is a real point in TagoIO's favour.
What TagoIO does well
- Mature Analysis runtime for substantial server-side logic
- TagoRUN end-user portals with branding, signup and access policies
- Broad protocol coverage
- A generally available MCP server for AI assistant access
Side by side
| TagoIO | Qubitro | |
|---|---|---|
| Time to first dashboard | Build the dashboard | Template ships decoder and starter dashboard |
| Custom logic | Analysis scripts (Node.js / Python) | JavaScript functions and JSONata expressions |
| End-user portals | TagoRUN, branded with access policies | White-label portal with custom domain per tenant |
| Device templates | Available | 291 templates with decoder and starter dashboard |
| AI / agent access | MCP server generally available | MCP server in early access; REST API available today |
| Pricing model | Usage- and resource-based | Per device, from $2/device/month, free tier available |
Choose TagoIO if
- You are building a full application, not just monitoring, and want a deep scripting runtime
- You need an MCP server in production today rather than in early access
- Your team is comfortable writing and maintaining server-side analysis code
Choose Qubitro if
- You want a working dashboard and decoder the moment a supported device connects
- Per-device pricing is easier to quote to your customer than usage-based billing
- White-label with a custom domain per customer is central to how you sell
- You would rather write small transforms than maintain an application codebase
Common questions
- Does Qubitro have an equivalent to TagoIO Analysis?
- Qubitro Functions run custom JavaScript against incoming data, and JSONata expressions handle reshaping without code. They cover decoding, transformation, enrichment and rule logic. TagoIO Analysis is the broader runtime of the two — if you need to build substantial application logic on the platform, that difference matters.
- Can I connect an AI assistant to Qubitro today?
- Through the REST API, yes. The Qubitro MCP server, which would let an assistant query devices and data conversationally, is in early access rather than generally available — access is arranged with the Qubitro team. TagoIO's MCP server is generally available today.
- Which is cheaper?
- It depends on shape rather than scale. Qubitro prices per device from $2 per device per month, which makes a quote to an end customer straightforward. TagoIO prices around usage and resources, which can work out lower for fleets that report infrequently and higher for chatty ones. Model your own data rate before assuming either way.
The fastest way to judge this is to connect one device. The free tier needs no card, and if your hardware is in the template library you will have a dashboard in minutes.