Customers / Data Centers
Designed for what's at stake
Rack densities are hitting 100 to 250kW. Global data center power consumption is projected at 1,050 TWh by 2026. Cooling already accounts for 40% or more of facility energy. Your monitoring infrastructure needs to match the pace of AI-era compute, not trail behind it. Mango delivers the unified telemetry foundation that high-density operations actually demand.
The Industry Right Now
The landscape is shifting
Data centers are being fundamentally reshaped by AI infrastructure demands. The facilities that win will be the ones that see what is happening across every system in real time.
The IEA projects global data center electricity consumption to reach 1,050 TWh by 2026, more than doubling from 2022 levels. AI training and inference workloads are the primary driver, and every additional watt needs to be tracked, allocated, and optimized in real time.
AI racks have pushed average density from 7kW to 40kW, with cutting-edge deployments already at 130kW and projections reaching 250kW per rack. Traditional air cooling cannot keep up. Liquid cooling, rear-door heat exchangers, and immersion systems are becoming standard, and monitoring must evolve with them.
The global AI data center market is projected to quadruple from roughly $236B in 2025 to $933B by 2030. That growth creates massive operational complexity. More facilities, more heterogeneous equipment, more interdependent systems. Unified monitoring is no longer optional.
Capabilities
What Mango does for data center operations
When your BMS, DCIM, EPMS, and OEM systems each operate in isolation, blind spots multiply with every new rack, every new cooling loop, every new facility. Mango brings them together into one telemetry layer built for the density and complexity of modern infrastructure.
Portfolio-Wide Unified View
See every facility, every rack, and every cooling unit in a single interface. Mango aggregates telemetry across BMS, DCIM, EPMS, and OEM systems so your teams stop switching between five dashboards and start operating from one source of truth.
AI-Era Density Monitoring
GPU clusters at 100kW+ per rack generate thermal profiles that shift faster than any human can track. Mango monitors CDU supply and return temperatures, coolant flow rates, differential pressures, and leak detection sensors in real time. Whether you are running rear-door heat exchangers, direct-to-chip loops, or full immersion cooling, every data point feeds into a single view so your teams catch thermal excursions before they become GPU throttling events.
Intelligent Alarming
Alarm fatigue is a relevance problem, not a volume problem. Mango supports contextual, layered alarm logic that suppresses noise and escalates what matters. Teams using this approach have cut alert volume by 41% while catching failures faster than before.
IT/OT Convergence
Your network team monitors switches and servers via SNMP. Your facilities team monitors chillers and PDUs via BACnet and Modbus. When a cooling failure threatens a compute cluster, those two worlds need to talk instantly. Mango speaks both IT and OT protocols natively, correlating network health with mechanical systems in shared dashboards so your NOC and facilities engineers respond together, not in sequence.
Edge to Cloud Deployment
Run on-premise, at the edge with MangoGT handling 200M+ data points on embedded hardware, in Docker containers, or in the cloud. Mango deploys where your infrastructure lives, not where a vendor's licensing model says it should.
PUE Optimization at High Densities
At 40kW+ per rack, every tenth of a PUE point translates to millions in annual energy cost. Mango calculates PUE, WUE, and carbon intensity from live telemetry, breaking efficiency down by zone, row, and cooling system. Track how mixed air-cooled and liquid-cooled environments affect your overall efficiency, and produce audit-ready data for SEC climate disclosure rules, EU Energy Efficiency Directive reporting, ISO 50001 energy management certification, and investor ESG frameworks like GRESB.
Why Teams Switch
Traditional DCIM was not built for this era
Most DCIM platforms were designed for a world of 5 to 10kW racks with predictable cooling loads. That world is gone. AI infrastructure has introduced density, thermal complexity, and data velocity that legacy tools simply cannot handle. Power capacity planning that used to work at 7kW per rack collapses when a single GPU cluster draws 100kW and the facility needs to decide in real time which circuits, PDUs, and cooling loops can absorb the load. Digital twins are maturing beyond visualization into real-time operational models. New metrics like PCE (Power Compute Effectiveness) are emerging alongside PUE because efficiency needs to be measured per useful compute cycle, not just per kilowatt.
Meanwhile, every vendor wants to own your data layer. Proprietary lock-in makes it impossible to feed clean, unified data into the AI-driven optimization tools you are building toward. You need an open platform that connects everything, belongs to you, and does not hold your operational data hostage.
Five dashboards, five logins, five versions of the truth
One interface, every system, one operational picture
Alarm fatigue burying real failures in thousands of alerts per shift
Contextual alarm logic that cuts noise 41% and escalates what matters
ESG reporting requires weeks of manual data extraction and reconciliation
PUE, WUE, and carbon metrics are live, auditable, always current
Vendor lock-in traps your data and blocks AI-driven optimization
Open, protocol-agnostic platform that you own and control
Liquid cooling CDUs and GPU thermals monitored in separate OEM portals
Flow rates, coolant temps, and GPU junction temps in one correlated view
Measured Impact
Results that compound
Based on aggregated data from Radix IoT customer deployments, 2023-2025
FAQ
Frequently asked questions
How does Mango differ from a traditional DCIM platform?
Mango is protocol-agnostic and open by design, unlike traditional DCIM platforms that lock you into a single vendor ecosystem. It connects to your BMS, EPMS, OEM controllers, and IT monitoring tools simultaneously, normalizing data from all of them into one telemetry layer. Rather than replacing your existing systems, it unifies them so you get a single operational picture instead of five disconnected dashboards.
Can Mango handle the monitoring demands of AI and high-density compute environments?
Yes. Mango was built for high-velocity, high-volume telemetry. AI racks pushing 100kW+ with liquid cooling and rapidly shifting thermal profiles generate far more data points per second than legacy monitoring was designed to handle. Mango ingests and processes that data in real time without dropping points, giving your teams the visibility they need to keep high-density infrastructure running safely and efficiently.
How does Mango support ESG reporting and sustainability goals?
Mango calculates PUE, WUE, and carbon intensity metrics directly from your live operational telemetry, making them always current and audit-ready. There is no separate data pipeline, no CSV exports, and no manual reconciliation step. Because the numbers come from the same system your engineers rely on every day, you can pull efficiency data for regulators or investors in seconds, not weeks.
We operate multiple sites with different equipment vendors. Can Mango handle that?
Yes, multi-vendor environments are exactly what Mango was designed for. It normalizes data from any vendor or protocol into a consistent data model, so your teams get uniform visibility regardless of what equipment is installed at each location. Every site can run different HVAC vendors, different PDUs, and different BMS controllers while cross-site comparisons, portfolio-level dashboards, and centralized reporting all work seamlessly.
What protocols does Mango support for data center environments?
Mango supports 40+ protocols out of the box. The most critical for data center operations include BACnet (for BMS and HVAC), Modbus TCP/RTU (for power metering and PDUs), SNMP (for IT infrastructure and network gear), OPC-UA (for industrial and OT systems), SQL and HTTP/REST (for database and API integrations), and MQTT (for IoT sensors and edge devices). It also supports custom and virtual data sources, so if your facility has proprietary controllers or specialized equipment, Mango can connect to them.
How does Mango handle the transition to liquid cooling and higher density racks?
Mango natively monitors all liquid cooling data points, including coolant flow rates, differential pressures, supply and return temperatures, and leak detection sensors, treating them as first-class telemetry. Because Mango is sensor-agnostic (not just protocol-agnostic), it handles any data type from any source. As your facility transitions from air-cooled to direct-to-chip or immersion cooling, you simply add the sensors, connect them, and build dashboards and alarms around the new data. No platform swap required.
Industry Brochure
Data Center Solutions Overview
PDF, 2.4 MB
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