# Blog - Tech Insights | TagoIO

> Insights, tutorials, and news from the TagoIO team.

Insights, tutorials, and news from the TagoIO team.

- [Analytics Models for IoT: From Dashboards to Predictions You Can Act On](https://tago.io/blog/analytics-models-for-iot.md): How analytics models turn IoT sensor data into forecasts, anomaly scores, and time-to-threshold estimates: the four-step pipeline, which model family answers which question, why a fitted model beats an LLM at scoring telemetry, and what the research says about the payoff.
- [What a Good IoT Dashboard for Facility Management Actually Looks Like](https://tago.io/blog/iot-dashboard-facility-management.md): What a good facility management IoT dashboard actually contains: the four views that matter, why layout follows the building, and why the best facility UIs now lead with forecasts and flagged anomalies instead of raw charts.
- [Open Source vs Managed IoT Platforms: What the Difference Really Costs](https://tago.io/blog/open-source-vs-managed-iot-platforms.md): Open source IoT platforms trade license fees for operations: hosting, patching, scaling, and building the analytics layer yourself. What each model really includes, what each really costs, and how to choose honestly.
- [LoRaWAN Network Server vs IoT Application Platform: What Each Actually Does](https://tago.io/blog/lorawan-network-server-vs-application-platform.md): A LoRaWAN network server moves packets; an IoT application platform turns them into dashboards, alerts, forecasts, and applications. What each layer does, why you need both, and how they connect.
- [What Is Predictive Analytics for IoT, and How Do You Get Started?](https://tago.io/blog/what-is-predictive-analytics-for-iot.md): A plain guide to predictive analytics for IoT: what it means, where it pays off, the honest requirements, and a first project you can ship on TagoIO.
- [Anomaly Detection vs Forecasting: Which Does Your IoT Project Need?](https://tago.io/blog/anomaly-detection-vs-forecasting-iot.md): Anomaly detection and forecasting solve different IoT problems. How to tell them apart, when you need each, and how they work together on TagoIO.
- [How Much Historical Data Do You Need to Forecast IoT Metrics?](https://tago.io/blog/how-much-historical-data-forecast-iot.md): How much historical IoT data a forecast really needs: the rule tied to your seasonal cycle, why more is not always better, and how to start before you have years of it.
- [Which Forecasting Model Should You Use for IoT Time-Series Data?](https://tago.io/blog/which-forecasting-model-iot-time-series.md): ARIMA, Prophet, and LSTM compared for IoT time-series forecasting: how each works, when it fits, and how to run the winner on TagoIO.
- [How to Forecast IoT Sensor Data: A Practical Guide](https://tago.io/blog/how-to-forecast-iot-sensor-data.md): A practical progression for forecasting IoT sensor data, from moving averages to seasonal models to machine learning, and how to run it on TagoIO.
- [How to Detect Anomalies in IoT Sensor Data Using AI](https://tago.io/blog/detect-iot-anomalies-with-ai.md): The four shapes a sensor anomaly takes, spikes, drift, silence, and correlated failures, why fixed thresholds miss three of them, and how to build detection on TagoIO with statistical baselines, TagoAI, Analysis, and Actions.
- [Which IoT Platforms Have the Best APIs for Custom Integrations?](https://tago.io/blog/best-iot-platform-api-custom-integrations.md): Five testable properties that decide whether a custom integration ships: coverage, token scoping, rate limit transparency, SDK quality, and two-way webhooks. Where the well-known platforms stand, and the proof to run before you commit.
- [How Satellite IoT Connectivity Works (and When It Makes Sense)](https://tago.io/blog/how-satellite-iot-connectivity-works-and-when-it-makes-sense.md): How satellite IoT connectivity works, the cost and latency trade-offs of LEO versus GEO, and the narrow set of cases where it beats LoRaWAN or cellular.

[llms.txt](https://tago.io/llms.txt)