DevGreenOps—also called DevSusOps—extends traditional DevOps by weaving environmental sustainability into every stage of the software development lifecycle. Jochen Joswig outlined this approach at OOP Conference, presenting a framework built on four core principles: transparency, minimalism, efficiency, and awareness. The goal is straightforward: deliver all the benefits of DevOps while dramatically reducing the environmental impact of software systems.
The promise isn’t just about feeling good—it’s about measurable resource reduction. DevGreenOps aims to cut energy consumption, water usage, and material resources throughout software’s entire lifecycle, from development through operation and eventual decommissioning. For an industry that accounts for growing percentages of global energy consumption as digitalization accelerates, these reductions matter.
Transparency Makes Environmental Impact Visible
The transparency principle centers on making sustainability metrics as visible and actionable as traditional performance metrics. Development teams already monitor response times, error rates, and resource utilization. DevGreenOps adds environmental dimensions to this observability, integrating meaningful sustainability metrics into monitoring systems and communicating them to all stakeholders—developers, operations teams, designers, and end users.
This visibility can take various forms depending on what drives decision-making in your organization. Sustainability reports provide comprehensive overviews, dashboards offer real-time insights, and single key performance indicators highlight specific concerns when they’re most relevant. The key is matching the format to how teams actually make decisions, ensuring sustainability data influences choices rather than getting ignored.
Carbon budgets represent one concrete implementation of transparency. Similar to fiscal budgets that limit financial spending on development or operations, carbon budgets cap the emissions that software projects can generate. This constraint forces teams to make trade-offs explicitly—just as financial budgets require choosing between features based on cost, carbon budgets require choosing based on environmental impact.
Scoreboards serve dual purposes in transparency implementations. First, they can gamify environmental protection by tracking which teams or projects achieve the most significant environmental impact reductions within specific timeframes. This competitive element taps into the same motivations that drive performance optimization contests or hackathons.
Second, scoreboards identify where impacts actually occur—which applications, classes, methods, or machines generate the largest environmental footprints. This granular tracking reveals improvement opportunities that would remain invisible in aggregate metrics. When you know that a specific database query consumes disproportionate energy or a particular microservice generates outsized emissions, you can target optimization efforts effectively.
Minimalism Questions What’s Actually Necessary
The minimalism principle asks a deceptively simple question: “Is this really necessary?” The power comes from asking it early and often throughout the development lifecycle. The earlier you question necessity, the more resources you save by avoiding unnecessary work entirely.
Consider the impact of discovering through paper prototype testing that users don’t actually need an entire planned feature. That feedback eliminates weeks or months of development, testing, deployment, and ongoing maintenance—saving not just environmental resources but also team time and organizational money. The feature that’s never built can’t consume energy, can’t require server capacity, and can’t add complexity to the system.
This scrutiny should extend to every aspect of software projects, which can feel painful when it targets work teams have invested significant effort into building. Eliminating features, services, or infrastructure components that people have poured time and energy into creating requires emotional resilience. But the payoff typically justifies the discomfort.
Unnecessary code makes software harder to maintain, slower to deploy, more expensive to operate, and more complex to monitor. Removing it improves all those dimensions while simultaneously reducing environmental impact. Users often benefit too—simpler software with fewer unnecessary features tends to provide better experiences than bloated alternatives.
The minimalism principle operates at every level of abstraction, from individual lines of code up to fundamental questions about whether entire services or projects should exist. Does this variable need to exist? Does this function need to run? Does this microservice need to be separate? Does this entire application need to be built? Each level offers opportunities for elimination that compound into substantial resource savings.
Efficiency Optimizes What Remains After Minimalism
Once teams have established that software components are genuinely necessary, efficiency optimization makes sense. The efficiency category encompasses both the resource and energy efficiency of products throughout their lifecycles and the efficiency of development processes themselves.
Programming patterns, algorithms, and data structures represent foundational efficiency considerations. A poorly chosen algorithm that scales poorly with data size can consume orders of magnitude more computational resources than a well-chosen alternative. Data structure selection similarly impacts memory usage and processing speed, with cascading effects on energy consumption.
Data formats matter more than many developers realize. JSON’s human readability comes at the cost of larger file sizes compared to binary formats. Uncompressed images consume bandwidth and storage that compressed alternatives avoid. These choices accumulate across millions or billions of operations, turning small inefficiencies into substantial environmental impacts.
Caching represents a powerful efficiency technique when applied thoughtfully. Storing computation results or data that’s expensive to generate or fetch can eliminate repeated work. However, caching requires memory and potentially storage, creating trade-offs that require careful analysis. Not everything benefits from caching, and excessive caching can waste resources rather than saving them.
Dependency elimination offers another efficiency opportunity. Every library or framework a project depends on adds code that must be loaded, maintained, and potentially executed. Removing unnecessary dependencies reduces complexity, speeds startup times, and often improves security alongside efficiency gains.
Programming language selection generates fierce debates in efficiency discussions. Research by Pereira et al. in “Ranking Programming Languages by Energy Efficiency” demonstrates that language choice can create up to 75-fold differences in energy consumption—the same algorithm implemented in C versus Python shows this dramatic variance.
However, language selection rarely exists in isolation. Developer productivity, ecosystem maturity, library availability, team expertise, and maintainability all factor into real-world language choices. The most energy-efficient language might not be the best choice when considering total cost of ownership and development velocity. Context matters enormously.
Awareness Adapts to Environmental Realities
The awareness principle recognizes that even transparent, minimal, and efficient software can reduce its environmental footprint further by responding intelligently to external conditions. This includes both technical awareness—where software adapts based on environmental factors—and team awareness about the broader implications of what they’re building.
Technical awareness enables software to respond to the fluctuating availability of renewable energy. Solar and wind generation varies with weather and time of day, creating situations where renewable energy is abundant at certain times and scarce at others. Software that schedules computation for when renewable energy is plentiful reduces reliance on fossil fuel generation without requiring new infrastructure.
This approach becomes particularly powerful when it reduces curtailment—situations where renewable energy generation exceeds demand, forcing grid operators to waste available clean energy. If computational workloads can shift to consume this otherwise-wasted renewable energy, the environmental benefit is substantial. Data processing, model training, batch jobs, and other deferrable workloads can take advantage of these conditions.
Team awareness extends beyond technical implementation to understanding the broader context of what’s being created. The question of sufficiency lives here—does the software actually improve lives, or does it simply create new forms of consumption? This philosophical dimension challenges teams to consider whether their work contributes to genuine human flourishing or merely adds to digital noise.
The Jevons Paradox presents a critical awareness challenge for efficiency-focused initiatives. This economic principle observes that efficiency gains don’t necessarily translate to resource savings because more efficient systems get used more intensively. When technology becomes more efficient, people and organizations tend to use it more, potentially consuming more resources overall despite the per-unit efficiency improvement.
A concrete example: more fuel-efficient cars might encourage people to drive more miles, potentially consuming more total fuel than less efficient vehicles that limited driving through cost. Similarly, more computationally efficient software might enable features or use cases that weren’t previously feasible, potentially increasing total energy consumption despite improved per-operation efficiency.
This paradox doesn’t invalidate efficiency efforts, but it does highlight the importance of combining efficiency with actual resource limits or usage constraints. Carbon budgets, for instance, prevent the Jevons Paradox by capping total emissions regardless of efficiency improvements. Efficiency gains within the budget allow for more functionality, but total environmental impact remains bounded.
Implementing DevGreenOps in Practice
Adopting DevGreenOps doesn’t require revolutionizing your entire development process immediately. Start with transparency—measure and make visible the environmental impact of your software systems. Without visibility, you can’t identify improvement opportunities or track progress.
Add sustainability considerations to existing decision-making processes rather than creating parallel sustainability-specific processes. When teams evaluate architectural options, feature priorities, or optimization efforts, include environmental impact alongside traditional considerations like cost, performance, and user experience.
Question necessity systematically. Build lightweight prototypes before investing in full implementations. Validate assumptions with users before building features. Review existing systems to identify components that can be eliminated without harming functionality. These practices improve software quality while reducing environmental impact.
Optimize efficiency where it matters most. Profile systems to identify actual resource consumption patterns rather than optimizing based on assumptions. Focus effort on the operations that consume the most resources—optimizing rarely-executed code paths delivers minimal benefit while optimizing hot paths can transform overall efficiency.
Cultivate awareness within teams about both technical opportunities and broader implications. Discuss how systems might adapt to renewable energy availability. Consider whether efficiency improvements might trigger rebound effects through increased usage. Question whether projects contribute to genuine value or simply add to digital clutter.
The DevGreenOps framework provides structure for integrating environmental sustainability into software development without abandoning the practices that make DevOps effective. The four principles—transparency, minimalism, efficiency, and awareness—complement rather than conflict with traditional software quality goals. Better environmental outcomes and better software quality often align more than diverge, making DevGreenOps an approach that benefits both planetary and organizational health.