MODE
IoT analytics platforms
- Features
- Ease of use
- Ease of management
- Quality of support
- Affordability
- Market presence
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What is MODE
MODE is a cloud-based analytics platform that combines SQL querying, Python/R notebooks, and interactive reporting in a single workspace. It is used by data analysts and data teams to explore data, build repeatable analyses, and share dashboards and reports with business stakeholders. The product emphasizes collaborative analytics workflows (queries, notebooks, and reports) and governance features such as permissions and scheduled refreshes. MODE is generally applied to business and product analytics; IoT analytics use cases typically depend on how organizations model and ingest device/telemetry data into connected data warehouses.
Unified SQL and notebook workflow
MODE supports SQL-based exploration alongside Python/R notebooks, enabling teams to move from querying to analysis without switching tools. This helps analysts operationalize statistical analysis and data transformations tied directly to source queries. The combined workflow is useful when telemetry data is already landed in a warehouse and needs iterative analysis and visualization.
Collaborative reporting and sharing
MODE provides shared reports and dashboards designed for distribution to non-technical stakeholders. Teams can standardize analyses through reusable reports and controlled access, reducing ad-hoc spreadsheet sharing. This collaboration model fits organizations that need a governed way to publish analytics outputs across functions.
Warehouse-centric connectivity
MODE is designed to connect to common analytical databases and cloud data warehouses and run queries where the data resides. This approach can reduce the need to replicate data into a separate analytics engine for many reporting scenarios. For IoT programs that already centralize device data in a warehouse, MODE can sit on top as the analysis and reporting layer.
Not an IoT ingestion layer
MODE does not primarily function as a device connectivity, message ingestion, or stream-processing platform. IoT telemetry typically requires separate components for device management, protocol handling, and real-time pipelines before data reaches MODE. As a result, end-to-end IoT analytics architectures usually require additional products and engineering effort.
Limited real-time operations focus
The product is oriented toward analytical querying and reporting rather than operational monitoring and alerting. Use cases that require low-latency observability, event correlation, or automated incident workflows may be better served by tools purpose-built for operational telemetry. MODE is more suitable for exploratory and business-facing analytics once data is persisted.
Requires strong data modeling
Effective use depends on having well-modeled, queryable datasets and reliable refresh pipelines. If IoT data is high-volume, semi-structured, or poorly curated, teams may face performance and usability constraints until data engineering work is completed. This can shift effort from analysts to platform and data engineering teams.
Plan & Pricing
| Plan | Price | Key features & notes |
|---|---|---|
| Studio | Free | SQL, Python & R; 10 MB/query; Private database connections; Up to 3 users; Sign up for free (app.mode.com). |
| Pro | Contact sales | All Studio features plus: 250 GB/month data compute (5 GB/query); Schedules; Permissioning; Email & Slack sharing; API access; Standard support; Minimum 3 users; "Schedule a demo" on site. |
| Enterprise | Contact sales | All features in Pro plus: Custom Data Compute; Visualize tens of millions of rows; Advanced identity management; Advanced SSO; Admin-level API access; Premium support; Minimum 50 users; "Schedule a demo" on site. |
Seller details
Mode Analytics, Inc.
San Francisco, CA, USA
2013
Private
https://mode.com/
https://x.com/modeanalytics
https://www.linkedin.com/company/mode-analytics/
