This article was originally published on The New Stack.
The adoption of AI and large IT infrastructure to support AI development has dramatically increased across the IT industry in a desperate attempt to streamline processes, optimize costs and reduce the time to market.
However, the improper and reckless adoption of emerging technologies that need gigantic data centers only adds to the costs of cloud computing and energy consumption. According to an International Energy Agency (IEA) report, AI development and deployment are expected to drive a surge in energy demand that will only grow.
Surprisingly, this scenario sets the stage for new opportunities as long as a more responsible, sustainable approach is put in place with green software development and engineering.
Sustainable software development involves efforts to reduce the environmental impact of IT infrastructures throughout the software development life cycle. It also encompasses reducing the economic and organizational impact of software, aiming for lower total cost of ownership. This could be achieved with efficient hardware, by insisting on a GreenOps approach and, more generally, by making “green” choices when it comes to the design and development of software applications.
Understanding the Energy Demands of AI
AI is permeating every aspect of our lives. Every organization, small or large, is lured by the promises brought out by AI implementation in products, applications and services.
Yet many people ignore that AI workloads can be particularly energy-intensive, leading to a huge environmental impact. Common pain points include the energy consumption needed to train large AI models, the power requirements of data centers to support solid AI infrastructures, but also e-waste.
Inefficient software development practices can only worsen the situation. All these concerns find evidence in a prediction by Gartner that anticipates the will of global enterprises to prioritize software sustainability alongside other nonfunctional requirements. Moreover, sustainability will likely be a major factor in buying technology, potentially causing a 50% rejection rate in purchasing decisions.
Basically, writing and processing code and software on which the whole AI paradigm relies requires massive energy consumption and produces excessive emissions. If the energy needed to fuel both the software and hardware comes from non-renewable sources and bad practices, the impact on the environment will be undoubtedly negative.
Introducing GreenOps: At the Core of Sustainability
So how can we minimize environmental impact while maintaining high efficiency and quality standards?
GreenOps is a holistic approach that combines business practices and technological innovations to improve cloud efficiency while reducing environmental impact.
It is worth highlighting that GreenOps paves the way for opportunities to enhance one’s own efficiency in terms of operations and market competitiveness. This is particularly true for all those organizations that must address high pressure derived from the sustainability discourse and budget allocation plans.
Essentially, a GreenOps approach could deliver advantages both in terms of sustainability and money. It involves using cloud configurations; choosing resources that are not thirsty for energy and computing power; scaling, scheduling and renewing resources; terminating zombie workloads and shifting to composability.
FinOps: A Complementary Approach to GreenOps
Apart from the environmental discourse, you must cope with hidden cloud costs. Just like GreenOps addresses the carbon footprint of cloud infrastructure, FinOps is a cooperative approach between IT, finance and business to address resource costs’ accountability in cloud environments through a cross-functional, centralized team.
This is possible through mutual conversation and precise cost monitoring, management and predictions. And it demands a substantial change from a cultural perspective.
Working together, GreenOps and FinOps can continuously optimize cloud resources to reduce costs, energy consumption and gas emissions. Ultimately, everybody wins.
Sustainable Software Development and AI: Best Practices
Smart and responsible use of AI can promote sustainability, yet it’s vital to start with a sustainable design approach.
Start with these best practices:
- Cloud migrations and optimizations: Cloud technologies are energy-efficient and scalable, significantly reducing e-waste and carbon emissions.
- GreenOps and FinOps: The combination of GreenOps and FinOps approaches can help monitor both costs and emissions.
- Green coding: Full life cycle management of sustainable software development involves green coding practices including optimizing algorithms, coding with efficient programming languages, but also reducing redundancies and complexities.
- Tools to optimize resources: Some tools are useful to minimize or optimize the use of resources that require loads of memory and processor power. For example, Kube-green is an open source tool that manages Kubernetes cluster resizing to optimize IT infrastructure energy consumption, thus reducing carbon emissions. Kube-green is a Kubernetes controller that uses a custom resource to pause and restart pods. Deployments can be therefore scaled down to zero and cron jobs can be restricted to working hours.
- Software architecture: Choosing software architecture such as serverless computing and other composable approaches help guarantee flexibility, scalability and adaptability, promoting reuse and reducing costs.
- AI smart usage: A responsible AI implementation presents opportunities to achieve sustainability goals. This can involve AI assistants or agents for optimizing processes, automating routine tasks, monitoring energy consumption or error rates and analyzing data trends, among other strategies. Augmented FinOps — or the integration of AI and machine learning (ML) into FinOps to streamline cloud cost management — helps cut cloud waste, reduce costs and carbon, and provide data for greener decisions.
Internal Developer Platforms and AI to Pursue Sustainability
Internal developer platforms (IDPs) provide a means to keep sustainability in mind alongside cost and reliability in software delivery.
An IDP with AI-native capabilities can promote sustainability by distributing energy-intensive tasks across different environments and discovering areas for energy savings.
Specifically, Mia-Platform can facilitate eco-friendly development practices through efficient virtualization, containerization and resource consolidation. It aims to manage the full life cycle of cloud native applications at scale, providing productivity, governance, but also sustainability benefits.
Moreover, AI companions and agents can help with collecting platform resources, supporting daily developer tasks, generating and reusing software assets, building and testing templates and components, monitoring errors and power usage effectiveness to identify areas for optimization.
The Symbiotic Relationship Between AI and Sustainability
Following the path of sustainability doesn’ t mean only saving energy and reducing emissions, but also reducing the total cost of ownership of software, minimizing its economic, organizational and environmental impact.
Some benefits include:
- Return on investment: Resource-aware AI development minimizes computational costs and the environmental impact. AI-powered platforms with automated tools bring lower operational expenses, faster development cycles and enhanced resource utilization, improving ROI while aligning with broader sustainability goals with efficiency and responsibility.
- Enhanced brand reputation and customer trust: Environmentally conscious consumers are increasingly choosing companies that demonstrate sustainable practices, leading to a significant boost in brand image, reputation customer loyalty. Embracing sustainability is not only an ethical commitment but also a powerful discriminator in today’s market.
- Mitigating the environmental risks associated with AI: Sustainable software development practices foster the ethical and scalable growth of AI, while lessening its negative environmental effects, including high energy consumption and e-waste.
Conclusion
A sustainable future requires prioritization of sustainable practices in software development.
Crucial best practices include taking a sustainable approach to the software delivery life cycle end to end, choosing efficient hardware and infrastructure, using green code and architecture, adopting GreenOps and FinOps and smartly implementing AI.
With AI adoption growing at amazing speed, responsible integration has become a necessity, not an option.
It’s vital to avoid being carried away with the hype and choose automation technologies based on specific use cases and tangible outcomes, which contributes to efficient and pragmatic implementation. This reduces wasted effort and resources, but also minimizes the environmental impact.
Use Mia-Platform, an AI-native developer platform, if you need the essential, comprehensive structure and tools to seamlessly manage the full life cycle of software development, addressing sustainability objectives alongside other other goals.
