What is AWS IoT SiteWise? Features, integrations and alternatives
AWS IoT SiteWise is a managed Amazon Web Services service for collecting, modelling and querying data from industrial equipment. It uses asset models and hierarchies to organise time series data and offers an edge gateway to bring in data from on-site systems.
Also known as: SiteWise, IoT SiteWise, Amazon IoT SiteWise
SiteWise ingests measurements from industrial equipment, stores them as asset properties and lets you define asset models that represent machines, lines and plants. Models can include computed properties such as transforms and metrics, so common calculations like averages or aggregates are defined once and reused across assets.
Typical users are industrial engineering and data teams already working in AWS, system integrators building cloud based monitoring, and companies that want to land plant data in a central cloud store. Because it is a managed service with APIs, it suits teams that prefer building on cloud components rather than installing a full IIoT application suite.
In a manufacturing stack it handles industrial time series organisation and access, and works alongside other AWS services for storage, analytics and AI. It does not replace MES, ERP or a plant historian. Edge components can run on site to collect and process data, which matters when connectivity to the cloud is limited.
Modules and capabilities
Asset models and assets
Define reusable models with properties and hierarchies, then create assets from them to represent equipment.
Properties, transforms and metrics
Measurements, derived calculations and time-windowed aggregates defined on the asset model.
SiteWise Edge
Edge software that collects data from industrial sources, for example through OPC UA, and can process data locally before sending to the cloud.
Data ingestion APIs
APIs and connections for writing measurements and reading latest, historical and aggregated values.
Alarms and notifications
Capabilities to define conditions on asset data and trigger notifications through AWS services.
Integration with AWS services
Connects with services for storage, streaming, analytics, visualisation and machine learning.
Integrations and data access
OPC UA and industrial protocols
SiteWise Edge and common gateways collect data from OPC UA servers, including servers such as Kepware.
AWS IoT services
Works with AWS IoT Core and Greengrass for device connectivity and edge workloads.
Analytics and BI
Data can flow to AWS storage and analytics services, and to BI tools through connectors and queries.
SiteWise APIs
APIs and SDKs write and read asset property values, such as batch put and get operations.
ERP and MES
Integrated indirectly through custom pipelines or middleware, sending summarised events or KPIs rather than raw streams.
Strengths
- Managed service that avoids running your own time series infrastructure
- Reusable asset models give consistency across many machines and sites
- Tight integration with other AWS analytics and AI services
- Edge option supports local collection and intermittent connectivity
- API driven, so it fits automated deployments
Limitations
- Cloud centric, which may conflict with air-gapped or strict residency needs
- Requires cloud engineering skills to assemble a complete solution
- Not a full IIoT application suite, so operator apps may need extra build effort
- Cost depends on usage patterns and needs forecasting for high frequency data
- Product features and visualisation options have evolved, so check current documentation
Where AI helps AWS IoT SiteWise users
- - Anomaly detection and forecasting on equipment time series using AWS machine learning services
- - Natural language access to asset data, such as asking which machines drifted from baseline
- - Generating asset model definitions from equipment lists and documentation
- - Combining sensor data with maintenance records for failure analysis
- - Summarising alarm history and likely causes for maintenance planners
Evaluating AWS IoT SiteWise? Check these
- Confirm your security and data residency policy permits cloud storage of plant data
- Design the asset model hierarchy and naming standard before ingesting data
- Estimate data volumes and sampling rates to forecast ongoing cost
- Decide how plant data reaches AWS, for example SiteWise Edge or an existing gateway
- Plan who builds dashboards and apps, since the service is a building block
- Test failure behaviour when the plant loses connectivity
Common alternatives: PTC ThingWorx, Siemens Insights Hub, Litmus Edge, Azure IoT based solutions, Ignition (Inductive Automation), Databricks or Snowflake with a separate ingestion layer.
Frequently asked questions
What is AWS IoT SiteWise used for?
It collects, models and serves industrial equipment data in AWS. Teams define asset models, ingest time series from machines, compute metrics, and query values through APIs for dashboards, analytics and machine learning. It is a building block rather than a complete operations application.
Does SiteWise work with OPC UA?
Yes. SiteWise Edge and many gateway patterns collect data from OPC UA servers, including connectivity servers such as Kepware. This lets plants keep their existing connectivity layer and forward selected tags to the cloud asset models.
Can AWS IoT SiteWise run on premises?
The core service runs in AWS regions. SiteWise Edge can run on customer hardware at the plant to collect and process data locally, but it is designed to work with the cloud service. Fully air-gapped designs generally need a different platform.
Is SiteWise a replacement for a historian or MES?
Not directly. It stores and models industrial time series, which overlaps with a historian in some uses, but it does not provide MES functions such as work order execution or genealogy. Many plants use it alongside existing historians and MES for cloud analytics.
How do I connect SiteWise to ERP data?
Usually through a custom pipeline or middleware. Production counts, downtime or energy KPIs derived from SiteWise can be posted to MES or ERP via APIs, and ERP master data like items and work centers can enrich analytics in a data lake. Design the mapping and ownership deliberately.
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