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Comparing IoT Metrics Tools for Utilities

Utilities face a major challenge: while IoT devices generate over 73.1 zettabytes of data annually, only 2–4% of smart meter data is used. This underutilization leads to missed opportunities for cost savings, outage prevention, and service improvement. IoT metrics tools can bridge this gap by enabling real-time monitoring, predictive maintenance, and actionable insights.

This article evaluates five leading IoT platforms for utilities - MetricFire, AWS IoT Device Management, IBM Watson IoT, Datadog, and Microsoft Azure IoT Central - based on key features like real-time processing, scalability, predictive analytics, security, and integration capabilities.

Quick Comparison

Platform	Strengths	Challenges	Pricing
MetricFire	Hosted Graphite & Grafana, simple pricing, real-time monitoring	Limited advanced query features	Starts at $16/month
AWS IoT Device Management	Advanced analytics, supports legacy protocols, scalable for large fleets	Requires technical expertise, costs can increase with scale	Pay-as-you-go
IBM Watson IoT	AI-driven predictive maintenance, scalable for millions of devices	High setup costs, complex deployment	Lite plan free for up to 500 devices
Datadog	AI-driven anomaly detection, strong integrations, long-term data storage	Expensive for small utilities, requires advanced technical skills	Usage-based SaaS pricing
Microsoft Azure IoT Central	Pre-built templates for utilities, integrates with Azure AI tools	Learning curve for non-Microsoft users, pricing flexibility limited	Tiered pricing starting at $0.08/device/month for up to 400 messages

Each platform has unique strengths tailored to different needs. MetricFire is ideal for utilities seeking cost-effective monitoring with open-source tools, especially when comparing in-house vs. MetricFire solutions. AWS and Azure excel in large-scale operations and advanced analytics. IBM Watson IoT offers powerful predictive maintenance features, while Datadog provides comprehensive observability across multi-cloud environments.

Choosing the right tool depends on your utility's size, technical expertise, and integration requirements. Start your free trial with MetricFire to explore its capabilities.

1. MetricFire

Real-Time Monitoring

MetricFire offers a hosted monitoring platform built on Graphite and Grafana, two open-source tools tailored for handling time-series data. For utility companies managing IoT devices, this means you can monitor metrics from smart meters, transformers, and grid sensors without needing to build custom infrastructure. The platform integrates with the Telegraf agent's SNMP input plugin, which collects performance data from networking devices like switches and routers, then forwards it to MetricFire for centralized monitoring. This setup ensures you can track device health across your entire infrastructure in real time, laying the groundwork for effective alerting.

Alerting Features

MetricFire uses "Events", a storage format designed to capture irregular occurrences with detailed metadata. This is especially helpful for tracking incidents like voltage spikes or equipment malfunctions. Alerts are sent through PagerDuty, Slack, email, or webhooks whenever pre-set thresholds are breached. Additionally, MetricFire's Hosted Graphite improves reliability by replacing standard whisper storage with a cluster-native system that keeps multiple redundant copies of your data. This ensures alerts remain dependable, even during hardware failures.

Scalability

MetricFire's platform easily scales to handle increases in data volume. For example, Coveo expanded its metric count by more than tenfold without extra effort. Maxime Audet, Cloud-Ops Team Lead at Coveo, shared:

"We now have over ten times the amount of metrics we started with... scaling to support this increase has been hassle-free, requiring no additional work on our side."

Jim Davies, Head of DevOps at MoneySuperMarket.com, also highlighted MetricFire's scalability:

"As MetricFire scales effortlessly, we can push and store more metrics than we really need today but might need tomorrow. This increases our depth of understanding of the systems that we run and heads off any future problems."

Pricing

MetricFire uses a simple pricing model based on unique time-series metric names - one metric equals one metric. For utilities managing 250,000 custom metrics, this predictable pricing avoids the steep per-metric charges often seen with other platforms. Business-ready Hosted Graphite and Grafana services start at $16/month, offering unlimited users, integrations, and dashboard sharing at no extra cost. Itai Yaffe, Big Data Developer at Nielsen, praised this transparency:

"There's complete transparency with everything MetricFire do which means we can accurately predict what we'll be spending and comfortably keep within our budget."

You can try MetricFire for free at Hosted Graphite or schedule a demo at MetricFire to see how it can streamline your infrastructure monitoring.

2. AWS IoT Device Management

Real-Time Monitoring

AWS IoT Device Management includes Fleet Hub, a web application that transforms utility-standard protocols like OPC-UA, DNP3, and Modbus into MQTT telemetry data. This enables utilities to visualize critical device health metrics such as connection status, firmware versions, and battery levels. With AWS IoT Events, the platform identifies equipment states and triggers automated responses to operational anomalies using the best tools for monitoring IoT devices - like detecting gearbox vibrations in wind turbines or temperature spikes in solar panels. For instance, Wärtsilä leverages AWS IoT to predict engine failures with over 90% accuracy. These real-time insights are bolstered by automated alert systems, ensuring swift action when issues arise.

Alerting Features

The platform's alerting capabilities are designed to act quickly on real-time data. Through integration with Amazon SNS, AWS IoT Device Management can send automated SMS or email alerts when equipment metrics exceed predefined thresholds. Fleet Indexing further enhances monitoring by enabling utilities to query specific groups of devices - like all temperature sensors in a facility - and aggregate data such as average readings or firmware versions. For troubleshooting, Secure Tunneling allows remote access to devices on isolated networks or behind firewalls without requiring changes to inbound firewall settings.

Scalability

Managing large fleets of smart devices becomes simpler with AWS IoT Device Management's scalable features. The platform supports bulk registration, allowing utilities to onboard millions of devices simultaneously by uploading templates with device identities and X.509 certificates. Dynamic Thing Groups automatically organize devices based on attributes like location or firmware version, streamlining the management of massive networks. This scalability is vital for utilities like Essent, which handles data for 2.5 million customers, processes 200,000 messages every 10 seconds, and has reduced costs by 80% as their platform scaled. To put it in perspective, a single smart meter set to collect data every 15 minutes generates 96 readings per day.

Utilities-Specific Integrations

AWS offers tailored solutions for utilities, such as Meter Data Analytics (MDA) 2.0, which integrates data from Head End Systems and Meter Data Management Systems into an S3 data lake. The AWS IoT Greengrass Modbus-RTU Adapter bridges legacy water and gas meters using traditional industrial protocols to connect them to the cloud. Companies like Net2Grid use AWS's Meter Data Analytics to cut operating costs by 400–500% while achieving 98% accuracy in predicting next-day energy consumption. These integrations also support advanced functions like circuit balancing, energy theft detection, and demand forecasting by combining meter data with GIS and weather data.

Pricing

AWS IoT Device Management follows a pay-as-you-go model, where costs depend on usage. Expenses are incurred for remote actions (like Jobs and Commands), bulk registrations, fleet indexing queries, and secure tunneling sessions. Additional services, such as Amazon SQS and QuickSight, operate on a pay-per-session basis. Serverless offerings like Lambda, Athena, and Kinesis also charge based on usage. This flexible pricing model enables utilities to scale their operations while maintaining control over costs.

Optimizing Utilities with IoT-Enabled Smart Meters

3. IBM Watson IoT

IBM Watson IoT stands out for its ability to provide fast, secure communication and control over distributed assets, making it a reliable option for utilities requiring real-time processing.

Real-Time Monitoring

The platform operates on an event-driven architecture, where devices send "Events" that are processed immediately. It uses MQTT and TLS protocols to ensure secure, low-latency communication between field devices and the cloud. Sensors connect through specialized Gateway devices, allowing seamless integration. Impressively, IBM Watson IoT can handle up to 5,000 MQTT messages per second for each organization, ensuring smooth device-to-cloud communication. Users can subscribe to data streams via the IoT tool within the Maximo Application Suite, allowing near real-time access to raw device data. Additionally, the Device Management Protocol supports "Managed Devices", enabling remote tasks like firmware updates, location adjustments, and factory resets - essential for maintaining distributed utility systems.

Alerting Features

The platform includes a Rule Engine that supports up to 100 rules per organization, each capable of triggering 10 automated actions. This allows utilities to respond instantly when metrics exceed set thresholds. Diagnostic logs are stored for 7 days, aiding in troubleshooting connectivity problems with remote sensors and meters. For monitoring device health, the system retains 500 KB of device logs and stores device information in the "Last Event Cache" for 45 days.

Scalability

IBM Watson IoT is designed to accommodate large-scale operations. The Lite plan supports up to 500,000 devices connected simultaneously, making it ideal for managing extensive smart meter networks or sensor deployments. With its multi-tenant architecture, each deployment is isolated using unique six-character Organization IDs. Devices can be grouped into resource clusters of up to 300 devices in Lite plans, and the platform supports up to 500,000 API keys for non-shared applications, ensuring flexibility for large integrations. These features make IBM Watson IoT an excellent choice for modern utility management.

Utilities-Specific Integrations

IBM Watson IoT integrates seamlessly with utility management systems, enhancing grid operations. It connects Distributed Energy Resources (DERs) - like solar panels, batteries, and electric vehicles - to balance energy supply and demand while ensuring grid stability. Its digital meters and smart sensors assist with planning infrastructure upgrades and responding faster to outages or severe weather. Using AI-driven tools, the platform predicts equipment failures, detects water leaks, and improves grid efficiency. Additionally, integrations with platforms like ThingsBoard provide access to specialized solution templates for SCADA Energy Management, Water Metering, and Smart Irrigation.

Pricing

The Lite Plan offers free access for up to 500 devices, with 200 MB of monthly data transfer and 10 API calls per second. Device-to-cloud messages are stored for 24 hours, while cloud-to-device messages persist for 48 hours. For larger deployments, messaging limits can be adjusted.

4. Datadog

Datadog stands out among IoT metrics platforms with its SaaS model and built-in AI tools, making it a go-to choice for handling utility IoT telemetry streams. Recognized as a Leader in the 2025 Gartner® Magic Quadrant™ for Observability Platforms, it excels at managing distributed, complex infrastructure.

Real-Time Monitoring

Datadog's lightweight IoT Agent is designed for resource-constrained devices, including those running on ARM processors and operating systems like Linux, Windows, and Android. This agent streams metrics and logs continuously, even over low-bandwidth networks, making it ideal for remote utility setups. Each data point is tagged with identifiers like region or device type, simplifying fleet-wide analysis and troubleshooting.

The Watchdog AI engine brings machine learning into the mix, detecting anomalies - like unexpected latency in a batch of smart meters - in real time. Operators can view device health alongside critical KPIs such as energy usage and grid load on a single dashboard. Datadog integrates seamlessly with managed IoT hubs like Azure IoT Hub, AWS IoT, and Google Cloud IoT, ensuring smooth data flow and monitoring.

Alerting Features

Datadog takes a smart approach to alerting, minimizing noise from transient issues by triggering alerts only during sustained or widespread failures. As explained, "IoT fleet operators can build alerts that trigger only on sustained or widespread device failures, so that responders are not overwhelmed by meaningless alerts for transient issues".

Alerts can be routed through multiple channels, including email, PagerDuty, Slack, and ServiceNow, with bidirectional syncing for ticket updates. The platform supports advanced alerting capabilities, like combining multiple trigger conditions and muting alerts during scheduled maintenance. Additionally, Datadog retains metrics for 15 months at a 15-second granularity, providing long-term data visibility.

Scalability

Designed to handle tens of thousands of devices, Datadog ensures quick setup with minimal local configuration. Its Flex Logs feature allows organizations to query high-cardinality IoT data for up to 15 months without requiring external storage. The platform also supports over 1,000 integrations with pre-built dashboards and monitors, making it highly adaptable.

Utilities-Specific Integrations

Datadog includes SNMP integration for monitoring physical infrastructure like American Power Conversion UPS systems, tracking details such as battery health and backup status. Host Maps provide a visual overview of regional grid health and device distribution across large-scale installations. Its integration with Azure IoT Hub centralizes telemetry from managed cloud IoT environments, further enhancing its utility-focused capabilities.

Pricing

Datadog follows a usage-based SaaS pricing model, offering a 14-day free trial for its entire suite. The Metrics without Limits™ tool helps manage telemetry costs by separating ingestion and indexing expenses, a valuable feature for high-volume utility IoT data. Similarly, the Logging without Limits™ model allows all logs to be ingested, charging only for indexing and retaining essential data.

5. Microsoft Azure IoT Central

Microsoft Azure IoT Central simplifies IoT deployments for utilities with its application platform as a service (aPaaS) offering. This solution removes the need to manage complex infrastructure. It includes ready-made templates tailored for energy companies, such as Smart Meter Analytics and Solar Panel Monitoring. These templates come preloaded with device models, dashboards, and command sets, making it easier for utilities to get started.

Real-Time Monitoring

Azure IoT Central uses "warm path" storage to provide near real-time monitoring of meter data. For those looking to monitor IoT devices using Telegraf and Mosquitto, alternative open-source stacks offer similar granular control. The platform includes built-in visualizations that track energy usage, power levels, and voltage trends across utility networks. With the Data Explorer tool, operators can review historical data and identify patterns, such as performance trends in wind turbines from a particular region or meters from a specific manufacturer. It also allows for remote operations like reconnecting meters, updating firmware, or rebooting devices - all without needing to send technicians into the field.

As Charles Alshuler, Director of Sales Operations at Clean Energy, explained:

"Our teams in the field can predictably perform processes and repairs, capturing the data via IoT".

Alerting Features

The platform supports condition-based rules to trigger automatic workflows when telemetry data crosses set thresholds, such as detecting abnormal voltage levels or sudden spikes in power usage. These alerts can initiate various actions, like emailing maintenance teams, calling external webhooks, or launching automated business processes. This proactive approach helps utilities address potential equipment issues before they lead to larger problems, such as outages. These features integrate seamlessly with the platform’s scalable design.

Scalability

Azure IoT Central is designed to handle large-scale utility networks. It supports millions of connected devices and automatically creates new IoT hub instances for every 10,000 devices to ensure consistent performance. The platform offers a 99.9% connectivity SLA and includes built-in high availability and disaster recovery features. Microsoft also invests over $1 billion annually in cybersecurity research to secure its cloud infrastructure. While data is retained for 30 days by default, users can export it to Azure Data Lake Storage or Azure Synapse for long-term storage and compliance purposes.

Utilities-Specific Integrations

Azure IoT Central extends its utility-focused capabilities with features like the Device Bridge, which consolidates devices from other IoT platforms into a single dashboard. It also integrates with Azure Maps, providing geographic insights by displaying device locations and regional grid conditions on map-based visuals. For deeper analysis, telemetry can be streamed to Azure Data Explorer for AI-driven forecasting or to batch processing systems for tasks like automated billing.

Pricing

The platform uses a tiered, per-device pricing model. The first two devices are free, while subsequent tiers are priced as follows:

• Tier 0: $0.08/device/month for up to 400 messages
• Tier 1: $0.40/device/month for up to 5,000 messages
• Tier 2: $0.70/device/month for up to 30,000 messages

Each message is limited to 4 KB, with larger payloads counted as multiple messages. There are no upfront costs or termination fees, and charges are prorated based on when devices are added during the billing cycle.

Pros and Cons

IoT metrics tools offer a mix of strengths and challenges for utility companies, and selecting the right one hinges on factors like company size, technical expertise, budget, and compatibility with existing systems. The table below highlights the key advantages and drawbacks of popular tools, helping utilities evaluate options for managing IoT data efficiently and at scale.

Tool	Pros	Cons
MetricFire	Reliable with hosted Graphite and Grafana; quick setup; excellent for telemetry visualization with Grafana; open-source backing ensures data portability; simple pricing starting at $19/month	Prometheus graphing and dashboards can feel limited for advanced queries; requires setup for more complex configurations
AWS IoT Device Management	Enterprise-level performance; advanced analytics; seamless integration with AWS services like Lambda and S3	Demands significant technical skills; costs can escalate with scale; ties users to the AWS ecosystem
IBM Watson IoT	Advanced AI and machine learning capabilities; proven scalability for millions of endpoints; strong analytics for predictive maintenance	High implementation expenses; complex setup requiring specialized skills; can be difficult to deploy
Datadog	Holistic monitoring across infrastructure and applications; excellent real-time telemetry processing; wide range of third-party integrations	Expensive, particularly for smaller utilities; requires advanced technical know-how for effective use
Microsoft Azure IoT Central	Integrates seamlessly with Microsoft AI and ML tools; highly scalable; offers templates tailored to energy companies; strong security features	Overly intricate for smaller deployments; steep learning curve for non-Microsoft users; pricing may lack flexibility

For utilities operating legacy systems from the 1960s–70s, integration capabilities are a top priority. These tools must support older protocols like Modbus and Profinet, which were not originally designed with cloud connectivity in mind. Additionally, platforms need to manage the immense data flow from millions of smart meters generating frequent interval data.

Conclusion

The evaluations above highlight how different platforms cater to specific IoT monitoring needs. Choosing the right tool comes down to aligning it with your utility's requirements, resources, and infrastructure.

For utilities looking for open-source flexibility combined with efficient data visualization, MetricFire offers a hosted solution with pricing starting at $16/month. It's ideal for teams needing high-performance telemetry monitoring without the hassle of managing their own infrastructure.

On the other hand, utilities managing large-scale smart meter networks might find AWS IoT Device Management or Microsoft Azure IoT Central more suitable. AWS is a natural fit for those already leveraging Amazon's ecosystem, while Azure seamlessly integrates with Microsoft's AI and machine learning tools. Both options support millions of endpoints and provide advanced analytics but require expert oversight and can lead to higher costs over time.

For utilities focused on predictive maintenance, IBM Watson IoT stands out, boasting a 70% reduction in equipment failures and a 45% decrease in downtime. However, its steep implementation costs and complexity make it better suited for larger organizations with dedicated technical teams. Meanwhile, Datadog excels in full-stack observability across multi-cloud environments. While its premium pricing may be a hurdle for smaller operations, it remains a strong choice for those needing comprehensive monitoring.

Integrating with legacy systems is also a critical factor. With IoT's projected value reaching trillions by 2030, selecting a tool that meets current demands while scaling for future growth is vital.

Interested in exploring your options? Start a free trial today (https://www.hostedgraphite.com/accounts/signup/) or book a demo (https://www.metricfire.com/demo/) to discuss your monitoring needs with the MetricFire team.

FAQs

How can I estimate the number of metrics my utility will generate?

The amount of metrics you deal with can vary widely based on factors like how many devices you're monitoring, how often data is collected, and the level of detail required. Take smart meters, for instance - if thousands of them report data every few minutes, you could end up with millions of data points in just one day. Tools such as MetricFire can simplify this process. They help you focus on the most important metrics, establish thresholds, and tailor everything to your operational needs, making the overwhelming volume of data far easier to handle.

What’s the fastest way to start monitoring smart meters with SNMP and Telegraf?

To get started with monitoring smart meters using SNMP and Telegraf, follow these steps:

• Install Telegraf: Begin by installing Telegraf on the system that manages your smart meters. This will serve as the data collection agent.
• Configure the SNMP input plugin: Open Telegraf’s configuration file and set up the SNMP input plugin. You'll need to include details like the device IP, community strings, and relevant OIDs (Object Identifiers) for the metrics you want to monitor.
• Set up the output: Configure where the collected data should be sent. This could be your preferred monitoring platform or database.
• Start Telegraf and verify: Launch Telegraf and check your monitoring dashboards to ensure the data is being collected and displayed correctly.

This straightforward setup lets you keep an eye on your smart meters with minimal hassle!

How should we set alert thresholds to avoid noisy alerts?

To cut down on noisy alerts, focus on setting thresholds thoughtfully. Techniques such as dynamic thresholds, baseline comparisons, and anomaly detection can help minimize false positives, ensuring alerts are meaningful and actionable. On top of that, crafting alert rules with appropriate durations and transformations can further filter out unnecessary notifications. This approach not only enhances the system's reliability but also reduces the distractions caused by excessive noise.