AI Tools for Reducing Cloud Energy Costs

Cloud data centers are consuming more energy than ever, with electricity demand expected to double by 2030. AI tools are stepping in to help businesses save energy, reduce costs, and lower emissions. Here’s how:

• Real-time monitoring to track energy use and detect inefficiencies.

• Dynamic resource allocation to optimize performance.

• Cooling system optimization to lower energy used for cooling.

• Predictive maintenance to prevent equipment failures.

• Carbon tracking tools to measure and manage emissions.

Top AI Tools for Energy Efficiency:

1. WEKA Data Platform: Cuts energy use by up to 90% for data-heavy workloads.

2. Microsoft Azure Sustainability Tools: Reduces energy use by 40% compared to on-premises systems.

3. C3 AI Energy Manager: Lowers energy costs by 4% and emissions by 5%.

4. NVIDIA GPU Manager: Improves GPU efficiency and reduces energy waste.

5. Google Cloud Carbon Tools: Tracks and minimizes carbon emissions with machine learning.

6. AWS Energy Tools: Cuts workload carbon footprints by up to 99%.

7. VMware CloudHealth: Offers real-time insights into energy use and emissions.

8. IBM Envizi Analytics: Simplifies energy tracking and reporting with AI.

9. Oracle Cloud SCM AI: Optimizes supply chain energy use and emissions.

10. Siemens WSCO: Improves cooling efficiency, reducing energy use by up to 25%.

Quick Comparison Table:

AI-driven tools are transforming how cloud operations manage energy, offering both financial and environmental benefits. Dive into the full article to see how these tools can help your business save.

Cloud & Data Center Optimization: Sustainable FinOps and AI for Climate Action

1. WEKA Data Platform

The WEKA Data Platform is designed to deliver exceptional energy efficiency and performance for data-heavy workloads, thanks to its advanced architecture. This makes it a powerful tool for organizations aiming to run more energy-efficient cloud operations.

WEKA’s innovative design enables organizations to achieve notable energy savings. For instance, it can reduce carbon emissions by as much as 260 tons of CO₂e per petabyte stored. This efficiency is rooted in the platform’s ability to maximize computational performance while minimizing energy consumption.

In a real-world example, the University of Surrey reported that experiments ran eight times faster on WEKA compared to local storage, all while consuming less energy per experiment.

The platform’s energy efficiency is further enhanced by its seamless integration with major cloud providers like AWS, Azure, GCP, and Oracle Cloud Infrastructure.

"WEKA is pleased to partner with Iris Energy, uniting their expertise in running 100% renewables-powered data centers optimized for power-dense compute with the WEKA Data Platform's advanced storage and data management software for high-performance AI compute environments."

• Liran Zvibel, CEO of WEKA

This efficiency is achieved through several key strategies:

• Pipeline Optimization: Minimizes idle time in data pipelines.

• Hardware Longevity: Extends the lifespan of infrastructure components.

• Resource Maximization: Enhances cooling and power distribution.

• Space Utilization: Increases performance density to save physical space.

Additionally, its software-defined architecture - featuring 4K granularity and virtual metadata servers - ensures resources like storage capacity and electrical power are used as efficiently as possible.

2. Microsoft Azure Sustainability Tools

Microsoft Azure has developed tools that significantly lower energy consumption while improving the efficiency of cloud deployments. In fact, using Azure for cloud operations can cut energy use by up to 40% compared to traditional on-premises solutions.

These tools rely on a variety of AI-powered features, including:

Azure's data centers are leading the way in resource efficiency, achieving 93% water-use efficiency and targeting 100% renewable energy by 2025. Compared to on-premises setups, workloads on Azure are up to 98% more carbon-efficient and 93% more energy-efficient.

One standout example of Azure's impact is JERA in Japan. Through their Digital Power Plant project, they use Azure OpenAI to manage energy performance and implement predictive maintenance. This has resulted in:

• A 25% boost in remote monitoring capabilities

• A threefold increase in sites offering 24-hour services

• Operational efficiencies exceeding 90% in specific instances

Azure’s commitment goes beyond energy savings - it also benefits local communities. For example, in Denmark, surplus heat from a Microsoft data center is used to heat around 6,000 homes.

Additionally, Azure's Carbon Optimization system provides a comprehensive view of carbon trends, data center efficiency, and resource usage. Companies like RELX utilize tools like the Emissions Impact Dashboard, integrating it into platforms such as CO2.Hub to monitor Scope 1, 2, and 3 emissions. These examples highlight Azure's broad approach to managing energy and emissions.

For businesses looking to minimize cloud energy costs, Azure's AI-driven tools offer an automated way to monitor and adjust operations, ensuring energy efficiency without compromising performance.

3. C3 AI Energy Manager

C3 AI Energy Manager leverages AI-driven forecasting to help businesses lower their cloud energy expenses. The platform has demonstrated the ability to cut energy costs by up to 4% while reducing Scope 1 and 2 emissions by 5%.

Here’s how the platform delivers measurable energy savings:

In February 2025, a North American facility achieved a notable milestone: a 10% reduction in total energy costs by optimizing HVAC systems with C3 AI.

Looking ahead, Ball Corporation aims to reduce emissions by 55% and improve efficiency by 30% by 2030. They rely on real-time analysis to gain actionable insights for optimization.

The platform is designed for flexibility, supporting multi-cloud deployments without requiring code modifications. Companies can roll out the system in just a few days and expand it across multiple locations within six months.

For example, a sugar manufacturer achieved a 1.9% increase in annual yield, translating to over $8 million in added value. Their Lead Data Scientist highlighted the seamless integration of live data:

"One of the successes that we've seen with Gurobi is that we're able to seamlessly pass live data - unified on the C3 AI Platform from across disparate enterprise source systems - and use that data in real time as numerical inputs to optimization models. Then, Gurobi can help us determine the maximal sugar output based on the current sensor data."

Additionally, the platform uses natural language processing to ensure precise emission factor selection. This capability allows businesses to shift from static monitoring to dynamic energy management. C3 AI Energy Manager stands out as a powerful tool for driving efficiency and reducing costs in cloud operations.

4. NVIDIA GPU Manager

The NVIDIA Data Center GPU Manager (DCGM) is a tool designed to enhance efficiency in GPU clusters, ultimately helping reduce energy costs. By offering diagnostics and automated configurations, DCGM ensures cloud infrastructure operates at peak performance.

Here’s a breakdown of its key features and their impacts:

These capabilities translate directly into reduced operational costs by cutting down on energy waste. For instance, PayPal achieved a 10% improvement in real-time fraud detection while slashing server energy use by a factor of eight using NVIDIA GPUs for inference. Similarly, Ansys cut power consumption by four times compared to CPU clusters, achieving the same performance with just six GPUs.

DCGM also excels in monitoring GPU health, automatically taking recovery actions, adjusting power caps based on workloads, and offering a centralized dashboard to pinpoint inefficiencies.

For cloud-based deployments, the energy savings are even more striking. A GPU-accelerated cluster managed with DCGM can save 588 megawatt-hours each month compared to traditional setups, offering a fivefold improvement in energy efficiency. In climate modeling, GPUs running DCGM deliver results 24 times faster while reducing annual energy consumption by 127 gigawatt-hours when compared to CPU-only systems.

"DCGM simplifies GPU administration in the data center, improves resource reliability and uptime, automates administrative tasks, and helps drive overall infrastructure efficiency."

DCGM’s benefits extend across major cloud providers. For example, Beamr achieved 30% more efficient video encoding using NVIDIA L40S GPUs on Oracle Cloud Infrastructure. Meanwhile, Encina accelerated simulations by a staggering 506 times on Microsoft Azure, significantly lowering operational costs.

Compatible with Linux systems on both x86_64 and aarch64 platforms, DCGM offers flexibility and the ability to implement automated, energy-saving measures tailored to specific needs.

5. Google Cloud Carbon Tools

Google Cloud is taking energy management to the next level with its AI-powered carbon management tools, designed to cut both energy costs and emissions. One standout feature, the Carbon Footprint tool, uses machine learning to pinpoint ways to save on energy and reduce carbon output.

Google's data centers now deliver three times the computing power on the same electrical input, making them 1.5 times more energy-efficient than standard enterprise facilities.

Smart AI Tools in Action

• Active Assist's Unattended Project Recommender: This AI tool identifies unused resources, helping organizations eliminate waste. For example, Google found that 600,000 kg of CO₂ emissions could have been avoided - equivalent to planting 10,000 trees.

• Key Insights for Optimization:

  • Regional Optimization: AI suggests cloud regions based on carbon footprint, cost, and latency.

  • Resource Right-sizing: Machine learning detects underutilized virtual machines (VMs) and offers better configuration options.

  • Automated Scaling: Dynamically adjusts computing resources to match real-time demand.

These tools illustrate how AI can streamline energy usage while slashing operational costs in cloud environments.

Real-World Impact

Consider Thoughtworks: by following Cloud Carbon Footprint recommendations, they achieved a 60% reduction in emissions and cut costs by 46%. This success story highlights how AI-driven tools are reshaping energy efficiency in cloud operations.

"The capability to measure and to understand the environmental footprint of our Public Cloud usages is among the key axes of our sustainable tech roadmap."

• Hervé DUMAS, Sustainability IT Director, L'Oreal

Organizations looking to reduce cloud energy expenses can rely on Google Cloud Carbon Tools for precise CO₂ metrics. Data is presented in metric tons on dashboards and kilograms through data exports. These tools are accessible via the Cloud Console, with charges applying only for BigQuery data exports.

Continuous Improvement with AI

Google Cloud's AI capabilities are constantly evolving to help businesses optimize their infrastructure. For instance, Uber uses the Unattended Project Recommender to identify idle projects across its engineering teams, cutting unnecessary carbon emissions while improving cost efficiency and security. This blend of sustainability and savings makes Google Cloud's carbon tools an essential resource for modern organizations.

6. AWS Energy and Cost Tools

AWS offers AI-powered tools that combine advanced optimization and monitoring features to help organizations save money and reduce their environmental impact. AWS's infrastructure is reported to be 4.1 times more efficient than traditional on-premises setups, potentially cutting workload carbon footprints by up to 99%.

AI-Driven Optimization Features

AWS integrates several AI-enabled features to enhance energy efficiency and reduce costs:

These technologies translate into measurable savings and operational improvements, as evidenced by their application in real-world scenarios.

Real-World Implementation Success

In one case, AWS solutions helped reduce operational costs by $2.5 million within the first year while also cutting carbon emissions significantly. Building on such results, AWS continues to improve transparency and accountability with robust monitoring tools.

For example, Duke Energy collaborated with the AWS Machine Learning Solutions Lab to create an AI-driven system that automates utility pole inspections across 33,000 miles of transmission lines.

Advanced Monitoring and Reporting

The AWS Customer Carbon Footprint Tool (CCFT) offers comprehensive tracking of carbon emissions, including:

• Monitoring Scope 1 and Scope 2 emissions

• Region-specific carbon impact analysis

• Organization-wide emissions tracking

• Detailed data export options

"AWS continues to relentlessly innovate its infrastructure to build the most performant, resilient, secure, and sustainable cloud for customers worldwide."
– Prasad Kalyanaraman, Vice President of Infrastructure Services at AWS

Sustainability Initiatives in Action

In 2023, AWS data centers achieved an impressive global Power Usage Effectiveness (PUE) of 1.15. They’ve also shifted to hydrotreated vegetable oil (HVO) for backup generators, cutting greenhouse gas emissions by up to 90% compared to diesel.

AWS’s data center designs are set to support up to a sixfold increase in rack power density over the next two years. This not only makes workload processing more efficient but also reduces emissions and operational costs.

A compelling example of these efforts is TotalEnergies' adoption of a generative AI system that automates energy transaction data reconciliation. This system enhances accuracy while reducing energy waste, showcasing AWS’s role in energy-efficient cloud operations.

7. VMware CloudHealth

VMware CloudHealth leverages AI to tackle two major challenges in cloud management: reducing costs and minimizing energy consumption. It offers a comprehensive view of energy usage and carbon emissions across various cloud environments, making it a powerful tool for sustainability.

AI-Driven Energy Optimization

CloudHealth's Intelligent Assist provides real-time, AI-powered insights into cloud services, helping users identify cost drivers and sustainability opportunities. Here's a breakdown of its key features:

By combining these capabilities, CloudHealth enables precise carbon tracking while paving the way for more sustainable cloud practices.

Carbon Tracking and Reduction

In partnership with WattTime, CloudHealth offers precise, location-based emissions data to monitor the environmental footprint of cloud operations. Its GreenOps Operational Emissions Dashboard calculates Scope 3 energy consumption and carbon emissions for compute resources on platforms like AWS and Azure.

"VMware is an incredible partner when it comes to exploring innovative emissions reduction opportunities for their customers and bringing them to life. The GreenOps feature for VMware Tanzu CloudHealth is truly just the beginning."
– Gavin McCormick, Founder and Executive Director, WattTime

Performance Metrics and Sustainability

CloudHealth doesn't stop at tracking emissions. It also evaluates server utilization, idle resource time, encryption overhead, and the optimization of compute and storage resources. These metrics empower businesses to make informed decisions that balance performance with environmental considerations.

Integration and Implementation

Tanzu CloudHealth has already supported over 20,000 customers in building and refining their FinOps strategies. The platform boasts an impressive average rating of 8.6 out of 10, with 75% of users recommending it for managing cloud costs and energy efficiency.

"We are excited to expand our partnership with AWS for such an important endeavor as establishing recommendations and best practices for sustainable infrastructure and application development."
– Purnima Padmanabhan, VMware SVP and GM of the Cloud Management Business Unit

With its multi-cloud compatibility and telemetry-driven insights, CloudHealth integrates sustainable practices seamlessly into cloud operations, complementing the AI-driven strategies discussed earlier.

8. IBM Envizi Analytics

IBM Envizi Analytics takes AI-powered cloud energy management to the next level by transforming raw data into meaningful insights that help cut energy costs and improve efficiency.

AI-Driven Energy Management

At the heart of Envizi is an AI engine that simplifies sustainability data management by automating complex calculations. Here’s a closer look at its standout features:

Real-World Implementation

A great example of Envizi’s capabilities is its adoption by Digital Realty in July 2023. The platform now supports over 300 data centers across 27 countries, streamlining the collection of key metrics such as:

• Power usage efficiency (PUE)

• Utility consumption data

• Renewable energy certificates

• Business travel emissions

• Supply chain carbon footprint

Advanced Analytics and Monitoring

Envizi provides detailed insights, offering visibility from high-level portfolio metrics down to individual facility sub-meters. This allows organizations to:

• Spot billing inconsistencies

• Monitor water and energy consumption trends

• Compare performance with industry benchmarks

• Track the progress of sustainability initiatives

These analytics are further enhanced by seamless integrations that simplify sustainability reporting.

"The IBM Envizi ESG Suite helps our clients, like Digital Realty, gain insights from data to fuel smarter, more sustainable decisions every day." - Christina Shim, Vice President, Global Head of Product Management and Strategy, IBM Sustainability Software

Integration Capabilities

Built on a SaaS framework, Envizi integrates effortlessly through APIs, automating data transfers for comprehensive sustainability reporting. For instance, Melbourne Water cut report preparation time from days to mere hours, and Ikano Group now tracks over 15,000 unique data types.

With its AI-powered analytics and seamless integration features, Envizi equips organizations to optimize energy use while ensuring precise sustainability metrics. This makes it an invaluable tool for businesses aiming to balance efficiency with environmental responsibility.

9. Oracle Cloud SCM AI

Oracle Cloud SCM AI is designed to help organizations streamline their supply chains while reducing environmental impact. By leveraging advanced AI tools, the platform focuses on optimizing shipping routes, cargo loads, and emissions tracking. Building on Oracle’s earlier efforts to use AI for energy efficiency in cloud operations, this system extends those benefits to supply chain management.

AI-Powered Energy Optimization

One of the standout features of Oracle Cloud SCM AI is its ability to deliver actionable insights for managing energy use across supply chain operations. Here's a closer look at its key capabilities:

Sustainability Integration

Oracle Cloud SCM AI includes built-in tools to support sustainability goals. For instance, the platform's Sustainability Policy Advisor aligns operations with regulatory standards and framework guidelines. It monitors sustainability metrics, tracks energy usage, ensures compliance with environmental regulations, and optimizes resource use. This integration highlights how AI can deliver both cost savings and environmental benefits.

Real-World Implementation

Mazda Motor Logistics offers a compelling example of Oracle Cloud SCM AI in action. By adopting Oracle Transportation Management in Europe, Mazda improved its distribution of vehicles and components. The system enabled them to select the best carrier, route, and service level, leading to more on-time deliveries.

Looking ahead, Oracle predicts that by 2025, up to 80% of routine supply chain decisions will be automated through AI.

Advanced Features

Oracle continues to enhance the platform's capabilities. The January 2025 update introduced several new features, including:

• Transit time predictions

• A transportation emissions calculator

• Tools for processing trade incentive programs

• Improved order management features

"To successfully navigate an increasingly complex global landscape, supply chain leaders need agile and efficient processes that can help them diversify and strengthen supplier networks, adapt transportation and logistics strategies, and stay ahead of regulatory changes."
– Srini Rajagopal, Vice President of Logistics Product Strategy, Oracle

Data Security and Integration

Oracle places a high priority on data security while offering AI-driven solutions. Steve Miranda, Executive Vice President for Applications Development at Oracle, reassures customers:

"For all AI, we maintain the exact same agreement with customers regarding the security and privacy of their data. We never pass any data to an LLM and never use customer data to train LLMs".

The platform also integrates smoothly with existing cloud systems, thanks to prebuilt connectors and adapters. This ensures that organizations can easily incorporate energy-efficient and sustainability-focused practices into their current operations.

10. Siemens WSCO

Siemens White Space Cooling Optimization (WSCO) is an advanced platform that uses AI to manage cooling systems in data centers. Given that cooling systems can account for up to 30% of a data center's energy consumption, WSCO focuses on making this process far more efficient.

AI-Powered Cooling Intelligence

WSCO combines a dense network of sensors with AI algorithms to fine-tune cooling at the rack level. Here's how it works:

This level of precision not only avoids wasted energy but also ensures consistent protection for sensitive equipment.

Real-World Performance

Siemens WSCO has proven its effectiveness in various large-scale facilities. For instance, at Greenergy Data Centers in Tallinn, Estonia, their 14,500 m² (about 156,000 sq ft) facility achieved impressive results:

• Power Usage Effectiveness (PUE) of 1.2, a highly efficient target

• 25% greater energy efficiency compared to the market average

• Fully automated cooling optimization 24/7

Integration and Implementation

The system's success is further highlighted by its deployment at BMO Financial Group. Key benefits include:

• A 64% reduction in operating cooling units for constant-speed fans, all while maintaining optimal temperatures

• Real-time airflow modeling and automatic adjustments to changing conditions

Eugene Murariu, Managing Director of Global Engineering at BMO Financial Group, noted its impact:

"Siemens and BMO's Critical Environments Group advanced our sustainability objectives while improving our operational effectiveness."

Advanced Features

WSCO's infrastructure-agnostic design makes it easy to integrate with existing building management systems (BMS). Its standout features include wireless and non-intrusive installation, real-time IT load matching, continuous AI learning, automated thermal protection, and detailed monitoring.

At Thésée Data Center in Paris, WSCO showcased these capabilities. A representative shared:

"We opted for the Siemens solution because it ensures a high level of security and reduces energy consumption and maintenance needs. In the long term, this will also extend the life span of the equipment."

Tool Features and Results

Data from leading cloud providers and AI applications shows measurable gains in energy efficiency and reduced operational costs. Industry examples and performance metrics clearly demonstrate how AI technologies are streamlining energy usage in cloud data centers.

Energy Savings and Carbon Impact

Several major providers have implemented AI-driven strategies to achieve significant energy savings. For instance, Google's DeepMind AI has cut cooling costs in data centers by 40% through advanced system management and predictive maintenance. Similarly, Huawei's use of predictive analytics in its Chinese data centers has led to an 8% decrease in overall energy consumption by forecasting demand and fine-tuning power distribution. Meta has also embraced AI, employing models to regulate environmental factors like heat, emissions, and airflow. This approach not only improves energy efficiency but also enhances safety conditions.

Key Performance Indicators

The demand for electricity in global data centers is expected to nearly double, rising from 536 terawatt-hours (TWh) in 2025 to approximately 1,065 TWh by 2030. AI operations alone could account for over 40% of this power usage. This trend aligns with the observed annual energy consumption increase of 10–30% in cloud computing.

"There isn't a huge [capital expenditure] investment you need to make to cut down on energy emissions. You can employ some of these techniques and cut your operating expenses."

These insights are backed by real-world examples, confirming the value of AI-driven optimization across various providers and scenarios.

As Fatih Birol, Executive Director of the International Energy Agency (IEA), aptly stated:

"AI is a tool, potentially an incredibly powerful one, but it is up to us – our societies, governments and companies – how we use it."

Next Steps

To capitalize on the energy and cost efficiencies discussed earlier, consider implementing AI-driven energy optimization using these actionable strategies that can result in measurable savings.

Strategic Implementation

Start by analyzing your current cloud infrastructure to pinpoint areas of energy inefficiency. Focus on selecting more efficient models to cut down on energy use. For example, DistilBERT can serve as a more energy-conscious alternative to BERT, while GPT-3.5/4 Turbo offers better efficiency compared to standard GPT-3/4 models.

Cost-Effective Solutions

Here are some practical strategies that can lead to immediate cost reductions:

These approaches lay the groundwork for deeper technical optimizations.

Optimization Techniques

• Resource Management: Automate scaling and resource allocation based on real-time demand. For instance, Spotify's auto-scaling system dynamically adjusts to usage needs.

• Hardware Efficiency: Limit the power available to data center equipment to reduce energy waste.

• Model Optimization: Use optimized machine learning architectures that can cut computational requirements by 5–10 times.

"I'm not suggesting that dev teams start optimizing their AI applications right now. But I am suggesting they mitigate cost challenges that tend to follow periods of high innovation."

• Erik Peterson, Co-founder and CTO, CloudZero

Future-Proofing

As data centers expand their global energy footprint, take steps to ensure your operations are prepared for the future. A great example is Microsoft's Project Forge, which achieves 80–90% utilization efficiency at scale.

To stay on track, use data-driven reporting for full traceability and accountability. Incorporate best practices for prompt engineering and establish clear metrics to measure reductions in cloud energy costs. These efforts will help you maintain efficiency and sustainability as your operations grow.

FAQs

How can AI tools help lower energy costs in cloud data centers?

AI tools are reshaping how cloud data centers manage energy, making operations smarter and more efficient. By using real-time monitoring and intelligent analysis, these tools identify inefficiencies and make on-the-fly adjustments. For example, they can fine-tune cooling systems or redistribute power, ensuring energy is only used where and when it's necessary.

On top of that, AI can predict energy demands and allocate resources more efficiently. This proactive approach not only cuts down on energy consumption but also helps businesses save money. At the same time, it supports a greener, more responsible way of running cloud computing systems.

How do AI-powered tools help reduce energy use and environmental impact in cloud data centers?

AI-powered tools are making a big difference in cutting energy use and reducing the environmental footprint of cloud data centers. They monitor energy consumption in real time, pinpoint inefficiencies, and adjust resource allocation to meet demand. This smarter energy management helps lower both operational costs and carbon emissions.

On top of that, AI improves how data centers incorporate renewable energy sources, allowing them to run in a more eco-friendly way. This approach not only supports cleaner operations but also helps businesses align with global sustainability efforts while keeping energy costs under control.

What should businesses consider when choosing an AI tool to manage cloud energy costs?

To find the best AI tool for managing cloud energy costs, start by determining your business's specific needs. Are you looking to track energy usage, optimize how resources are allocated, or predict peak demand? Tools that offer real-time analytics and predictive features can pinpoint inefficiencies and guide smarter energy management decisions.

You’ll also want to assess how well the tool can scale alongside your business and whether it integrates smoothly with your current systems. Look for solutions that deliver clear benefits, such as cutting energy consumption, lowering operational expenses, and supporting greener practices. Keeping these factors in mind will help you choose an AI tool that fits your energy management and cost-saving objectives.

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