The NADIKI project releases the first open API specification for collecting environmental impact data from data centers for AI workloads. The interface standardizes the reporting of energy, water, and emissions data from building systems, racks, and physical servers in an AI data center to a central registrar, making these metrics available for AI applications.
The NADIKI API is available as an open-source specification on GitHub. Feedback and contributions from the community are welcome—the specification thrives on inputs from data center operators, developers, and researchers.
How much energy does an AI query actually consume? What is the CO₂ footprint of a single inference operation? Until now, a standardized interface to pass data on operations—from power supply to cooling—from the building infrastructure of a data center to the AI application has been lacking. The NADIKI API now offers a specification to fill this gap and enable data exchange.
The specification developed under the BMUKN-funded research project NADIKI defines an open interface through which three infrastructure levels—data center, rack, and server—can report their static properties and dynamic measurements to a central registrar. From the transmitted data, the necessary calculations are later conducted in the registrar's implementation to provide seven environmental impact indicators as Prometheus-compatible metrics.
Seven Environmental Impact Indicators
The API specifies how AI applications are provided with the following environmental indicators:
Recovered Energy (kWh): Reclaimed thermal energy, such as from waste heat usage.
Primary Energy Consumption (kWh): Total energy consumption of the data center.
Renewable Energy (kWh): Share of renewable energies in total consumption.
Non-Renewable Energy (kWh): Share of fossil energy sources.
Greenhouse Gas Potential (CO₂-eq): Operational and embedded emissions.
Freshwater Consumption (m³): Water consumption of the facility.
Abiotic Depletion Potential (kg Sb-eq): Mineral resource consumption due to hardware manufacturing.
Three Infrastructure Levels
The specification covers the entire physical chain of a data center. It specifies which static properties and dynamic measurements each entity in the chain must transmit:
Data Center: Over 40 data points—including energy efficiency (PUE), grid electricity emission factors, cooling systems, water and thermal measurements, renewable self-production, and embedded emissions of the building infrastructure.
Rack: 12 data points—PDU energy consumption, cooling capacity, temperature sensors, redundancy configuration, and lifecycle data.
Server: 17 data points—hardware inventory (CPUs, GPUs, FPGAs, memory), energy consumption via RAPL and IPMI, and cooling type configuration (air, liquid, or immersion).
Data Center Facility
Name | Version | Description | Unit | Type | Used in Indicator | Default/Assumption | Source |
|---|---|---|---|---|---|---|---|
Data Center ID | 1 | A unique identifier for the data center | UID | Static | FACILITY-[COUNTRY_CODE]-ID | ||
Grid Emission Factor | 1 | The emission factor of the Grid surrounding the facility (pass-thru via Electricity Map) | CO2-eq (kg) | Dynamic | Primary energy use | 0,406 kg CO2-eq/kWh | NL 2021 Data from EU |
Backup Emission Factor | 1 | The emission factor of the backup power generation system (diesel generators) | CO2-eq (kg) | Dynamic | Primary energy use | ||
Electricity Source | 1 | A semi-boolean field that tells what electricity source is currently active | Grid or Backup | Dynamic | Grid | ||
PUE 1 | 1 | Power usage effectiveness measured from the output of the UPS systems for IT load | Ratio | Static | Primary energy use | 1.46 | Uptime Average for EU |
PUE 2 | 1 | Power usage effectiveness measured from the output of the PDU systems for IT load | Ratio | Static | Primary energy use | 1.46 | Uptime Average for EU |
Pump Heat Produced | 1 | Amount of energy produced by a heat pump connected to the data center cooling system | kWh | Dynamic | Re-use energy | 0 | |
Heat Pump Power Consumption | 1 | Amount of electricity used by the heat pump to generate heat | kWh | Dynamic | Primary energy use | 0 | Zabbix |
Office Energy Use | 1 | Amount of electricity used by the office | kWh | Dynamic | Primary energy use | 0 | Zabbix |
DC Water | 1 | Amount of fresh water used by the DC, for example in chillers for adiabatic cooling | m3 | Dynamic | Fresh Water Use | 0 | Water meters OR invoice data |
Office Water | 2 | Amount of fresh water used by the Office space associated with the facility | m3 | Dynamic | Fresh Water Use | 0 | Water meters OR invoice data |
Embedded GHG Emissions Facility | 1 | The amount of embodied carbon emissions from the construction of the data center | CO2-eq | Static | Climate Change Potential | ||
Life Time Facility | 1 | The expected lifetime of the facility | Years | Static | Climate Change Potential | 15 | |
Embedded GHG Emissions Assets | 1 | The sum of all GHG emissions embodied in the assets from manufacturing and transport | CO2-eq | Static | Climate Change Potential | ||
Life Time Assets | 1 | The expected, average lifetime of all assets in the facility | Years | Static | Climate Change Potential | 10 | |
Amount of cooling fluids | 1 | The amount of cooling fluids used in the cooling systems, grouped by type | kg or m3 | Static | Climate Change Potential | ||
Types of cooling fluids | 1 | The type for each cooling fluid | Identifier | Static | Climate Change Potential | ||
GHG Emission Factor per Cooling Fluid Type | 1 | The list of GHG Emission Factor values for each type of cooling fluid | GWP Factor | Static | Climate Change Potential | ||
Grid CO2 Intensity Factor | 1 | The amount of renewable energy available in the grid zone of the data center facility | CO2-eq (grams) per kWh | Dynamic | Climate Change Potential | Electricity Map | |
GHG Emission Factor for Generator Fuel | 1 | The GHG emission factor of the fuel type used in backup generators | GWP Factor | Static | Climate Change Potential | ||
Maintenance Hours of Generator Runtime | 2 | Hours per year that the generators are assumed to run and burn fuel | Hours | Static | Climate Change Potential | maintenance manual | |
Total Generator Electricity Production | 1 | The total energy output of the generators running | kWh | Dynamic | Primary energy use, Climate Change Potential | Zabbix | |
Total Average Load Factor of Running Generators | 1 | Average load factor of all generators running | % | Dynamic | Primary energy use, Climate Change Potential | Zabbix | |
Fuel Efficiency of Generators at Load | 1 | Static curve of load factor vs. output efficiency or fuel use | Efficiency % at Load % | Static | Primary energy use, Climate Change Potential | ||
Energy Content of Generator Fuel | 1 | A static value on the energy content in a liter of fuel | kWh per Liter | Static | Primary energy use, Climate Change Potential | ||
Total Grid Transformers Energy | 1 | Sum of energy output of all transformers in the data center facility | kWh | Dynamic | Primary energy use | Zabbix | |
Total On-Site Renewable Power Generation | 1 | Sum of energy produced of all on-site renewable energy sources | kWh | Dynamic | Primary energy use (renewable) | Zabbix | |
IT DC Power Usage (Level 1) | 1 | Sum of all power usage as measured by the total output of the UPS systems | kWh | Dynamic | Primary energy use | Zabbix | |
IT DC Power Usage (Level 2) | 1 | Sum of all power usage as measured by the total output of the PDUs in each rack | kWh | Dynamic | Primary energy use | Zabbix | |
Renewable Energy Certificates | 2 | Either % or kWh covered by annually matched green energy certificates | kWh or % | Dynamic or Static | Primary energy use (renewable and non-renewable) | 0 | Scholt API |
Location of the data center | 1 | Geo coordinates of the data center | Lat/Lng | Static | Grid Emission Factor | NL | |
Installed electrical capacity | 1 | Installed/rated power capacity of the data center facility | kWh | Static | Verification | ||
Number of grid power feeds | 1 | How many physical power feeds are connected to the facility? | Number | Static | Verification | 3 | |
Design PUE | 1 | What was the PUE that the facility was designed for? | Factor | Static | Verification | 1.4 | |
Facility Tier Level | 1 | The certified/rated tier level of the data center facility | Number (1-4) | Static | Verification | 3 | |
Number of floors of white space | 1 | How many floors are used for white space? | Number | Static | Verification | 1 | |
Total facility space | 1 | What is the total space of building? | m2 | Static | Verification | ||
Total whitespace in the facility | 1 | How much space is used for whitespace/to host IT equipment? | m2 | Static | Verification |
Rack
Metric Name | Type | Unit | Default Value | Default Source | Measurement | Required | Description |
|---|---|---|---|---|---|---|---|
Data Center ID | UID | - | - | - | - | Yes | |
Cage ID | UID | - | null | - | - | No | |
Total available power | number | kW | 5 | Assumption | Static | No | |
Total available cooling capacity | number | kW | 5 | Assumption | Static | No | |
Number of PDUs | number | - | 2 | Assumption | Static | No | |
Power redundancy | number | - | 2 | Assumption | Static | No | No of power feeds used for redundancy |
Product passport | object | - | missing | - | Static | No | LCA |
PDU energy consumption | array | kWh | Dynamic | Yes | An array with a power reading for each PDU | ||
Inlet temperature sensor | number | C | Dynamic | No | Inlet cooling temperature (water or air) | ||
Outlet temperature sensor | number | C | Dynamic | No | Outlet cooling temperature (water or air) |
Server
Metric Name | Type | Unit | Default Value | Default Source | Measurement | Required | Description |
|---|---|---|---|---|---|---|---|
Data Center ID | UID | - | - | - | Yes | ||
Rack ID | UID | - | - | - | Yes | ||
Rated power | number | kW | - | - | Static | No | |
Total CPU sockets | number | - | 2 | Assumption | Static | No | |
Installed CPUs | array | - | - | - | Static | No | An array of objects with CPUs: Vendor, Type |
Number of PSUs | number | - | 2 | Assumption | Static | No | |
Total installed memory | number | GB | - | - | Static | No | |
Number of memory units installed | number | - | - | - | Static | No | e.g. 2 memory sticks |
Storage devices array | array | - | - | - | Static | No | Vendor, Capacity (TB), Type (NVMe, SSD, HDD, Other) |
Total GPUs installed | number | - | 0 | Assumption | Static | No | |
Total FPGA installed | number | - | 0 | Assumption | Static | No | |
Installed FPGAs | array | - | - | - | Static | No | An array of objects with FPGAs: Vendor, Type |
Installed GPUs | array | - | - | - | Static | No | An array of objects with GPUs: Vendor, Type |
Product passport | object | - | Boavizta API | Boavizta | Static | No | LCA |
CPU energy consumption | number | kWh | - | - | Dynamic | No | Total energy consumption of all CPUs measured via RAPL |
Server energy consumption | number | kWh | - | - | Dynamic | Yes | Total energy consumption of the server measured via IPMI |
Cooling type | enum | string | air | Assumption | Static | Yes | One of: direct-to-chip, immersion, back-door-liquid, back-door-fan, air |
Open Default Values and Sources
Each parameter features documented default values with source references—such as the EU average PUE of 1.46 (Uptime Institute) or the Dutch renewable share of 48% (2023). Data centers can begin implementing immediately and gradually improve their data quality without initially having to collect every measurement themselves.
Prometheus-Compatible
The specification defines the path by which infrastructure entities register with a central database. After registration, each entity must obtain a Prometheus-compatible endpoint for metric provision of measurements. This enables seamless integration of operational data collection into existing IT monitoring infrastructure.
The NADIKI API is a result of Work Package 1 of the NADIKI project and was developed in collaboration with the Institute of Architecture of Application Systems (IAAS) and the Institute of Energy Economics and Rational Energy Use (IER) of the University of Stuttgart.