Community Spotlight

Gaël Duez is a former CTO who works on making IT sustainable. After working in payment services and managing large property platforms across Europe, he began questioning the environmental and social footprint of the technologies he had spent years building. That realization led him to launch the Green IO podcast nearly five years ago, followed by conferences across several continents and a newsletter focused on sustainable technology, responsible AI, and green engineering practices.

For CTOs and development teams that have never measured their environmental impact, Duez recommends starting with AI usage. Token consumption is an imperfect measure, but it offers an accessible proxy for energy use and gives teams something concrete to track. From there, they can begin examining carbon emissions, water consumption, and the raw materials required to support computing infrastructure. The commercial argument is that reducing unnecessary model calls, oversized cloud environments, and idle infrastructure usually lowers operating costs alongside environmental impact.

One of the biggest obstacles is what Duez calls the measurement trap. Engineering teams are accustomed to seeking precise numbers, but environmental accounting remains far more uncertain than financial accounting. Teams often delay action because they cannot produce a perfectly accurate carbon or water figure for senior management. Duez argues that imperfect measurement remains useful when it identifies waste, fosters internal awareness, and establishes a baseline that can improve over time.

He also sees sustainability as more than a cost-control exercise. Giving engineers ways to reduce environmental impact can strengthen employee engagement and employer reputation, particularly among technical workers concerned about climate change, resource scarcity, and water stress. It can also force companies to confront difficult questions about resilience. Heavy dependence on a single cloud provider or frontier AI model may expose critical workflows to price increases, geopolitical restrictions, service changes, or sudden loss of access. A frontier model may be justified for complex development work, but routine tasks such as drafting standard emails could run on smaller or self-hosted models at lower cost and with fewer dependencies.

Duez’s most practical recommendation is to make computing workloads responsive to the electricity grid. Before launching model training, large batches, or other energy-intensive jobs, developers can use services such as Electricity Maps to check the carbon intensity and demand level of the local grid. Non-time-sensitive workloads can then be shifted to another hour, region, or availability zone. The habit reconnects developers with the physical infrastructure behind the cloud and turns sustainable IT into everyday engineering decisions. His broader message is that every organization is late, including those already working on decarbonization. The useful question is not what must be in place before starting, but where the team can start today.

API Feed

Big Story

When organizations first begin building APIs, delivery is often straightforward. A small number of teams own a limited set of services, communication is direct, and decisions can be made quickly. As the organization grows, however, the environment becomes more complex. New products are launched, engineering teams expand, and APIs become a critical part of how applications, data, and business capabilities are delivered across the enterprise.

At this stage, the challenge is no longer simply building APIs but delivering them efficiently across multiple teams, departments, and business units.

As API programs mature, teams often encounter the same obstacles. Different groups adopt different processes, release cycles, and development practices. Teams may build similar capabilities without realizing they already exist elsewhere in the organization. Dependencies increase, reviews take longer, and delivery slows as more stakeholders become involved. What worked well for a small team can become difficult to sustain across dozens of teams operating at enterprise scale.

Many organizations initially respond by introducing additional controls. While governance is important, excessive process can create new bottlenecks. Teams spend more time navigating approvals, coordinating across departments, and managing dependencies than delivering value. As a result, organizations often discover that scaling API delivery is not primarily a technical problem.

This is where platform thinking becomes important.

Rather than requiring every team to solve the same problems independently, organizations can provide shared capabilities that simplify API delivery. Common design standards, reusable templates, automated quality checks, developer portals, CI/CD pipelines, and self-service tooling help teams move faster while maintaining consistency. Instead of reinventing processes for every project, teams can build on a common foundation.

The goal is not to centralize all API development. In fact, the most successful organizations avoid creating a single team that becomes responsible for every API decision. Instead, they create platforms and practices that allow teams to operate independently while remaining aligned with broader organizational standards. Teams retain ownership of their services, but they benefit from shared infrastructure and common ways of working.

Self-service is often a key part of this approach. Developers should be able to discover APIs, access documentation, test integrations, and deploy services without waiting for multiple approvals or manual intervention. Communication also plays an important role. As the number of APIs grows, teams need visibility into what already exists, what is changing, and who is responsible for maintaining critical services. Clear documentation, transparent ownership, and consistent communication practices help reduce duplication and improve collaboration across the organization.

Ultimately, scaling API delivery is about creating an environment where teams can move quickly without creating unnecessary complexity. Organizations that succeed are the ones that make it easier for teams to build, share, and evolve APIs together. As APIs continue to underpin digital products, integrations, and business operations, the ability to deliver them efficiently across large organizations will become an important competitive advantage. The focus is shifting from simply building APIs to creating the systems, platforms, and practices that help teams deliver them at scale.

Resources & Events

apidays Munich brings together API architects, platform engineers, and enterprise technology leaders for two days focused on API strategy, platform operations, and AI-driven enterprise systems. The 2026 program includes sessions on API lifecycle management, developer experience, event-driven architectures, and the operational challenges of managing APIs across distributed environments. Designed for teams scaling enterprise API programs, the event combines technical discussions, practitioner-led case studies, and platform engineering perspectives on how APIs are evolving alongside automation and AI-connected workflows. Details →

apidays India brings together API architects, platform engineers, developers, and enterprise technology leaders for two days focused on the next generation of API-driven systems. The 2026 program explores how APIs are evolving to support AI agents, autonomous workflows, and machine-driven ecosystems, alongside sessions on API monetization, security, governance, platform strategy, and AI-driven automation. Designed for teams building and operating modern digital platforms, the event combines technical deep dives, practitioner-led case studies, and real-world discussions on how APIs are being designed, secured, and managed in an AI-connected world. Details →

You can find a list of all Apidays events here

Apply to speak at Apidays Singapore, NY, London, Paris, and more here

The summit brings together enterprise platform engineering, developer experience, DevOps, reliability, and engineering leaders working across complex and regulated environments. The agenda focuses on how large organizations are building internal developer platforms that scale, treating platforms as products, creating golden paths and self-service workflows, and using automation and AI without weakening reliability, governance, or compliance. Case studies from companies including IKEA, Wise, Adidas, BlackRock, Lloyds Bank, Sanofi, Nationwide, SEB, and Ford Credit will examine platform adoption, developer productivity, technical debt, AI maturity, centralized tooling, and the challenge of demonstrating measurable business value. Details →

This report examines how large language models can be used to strengthen REST API testing. Researchers applied LLM-based test amplification to microservices at one of Belgium's largest logistics companies and found that the approach improved API coverage and uncovered anomalies and edge cases that existing tests had missed. Rather than replacing existing test suites, AI is being used to augment them by generating additional test scenarios, exploring alternative execution paths, and identifying potential weaknesses with limited manual effort. For API teams, the report offers an early look at how AI-assisted testing may become part of the standard API delivery lifecycle. Read →

Insight of the Week

In Forbes, apidays CEO Baptiste Parravicini argues that compliance is shifting from a bolt-on afterthought to an AI system built directly into how platforms run. Old enforcement checks each signal on its own against fixed rules, but a new login, a password change, or a profile edit only reveals an attack when read together. Adaptive methods like reinforcement learning catch these patterns and keep pace with threats that static, label-dependent systems miss. Regulations such as the EU Digital Services Act and AI Act raise the bar further, demanding that decisions be traceable, explainable, and auditable by design rather than retrofitted later. The goal, he says, is bounded autonomy: let AI handle high-volume, fast-moving enforcement while humans keep appeals, escalations, and edge cases. Companies still running static rule engines and fragmented tools will most likely spend the next decade trying to catch up.

For the Commute

In this APIdays New York session, Matthew Voget, VP of Engineering at Ambassador, explores approaches for improving API quality and developer productivity. The talk examines common challenges in API development, including missing API specifications, testing complexity, and slow development feedback loops. Voget discusses how API specifications, workflow testing, mock environments, and AI-assisted tooling can help teams build more reliable APIs while reducing delivery friction. For platform and API teams scaling development across multiple services and teams, the session offers lessons to improve API quality, accelerate delivery, and create more effective developer experiences.

Stay tuned for bold ideas, fresh perspectives, and the next wave of API innovation

-The Apidays Team