It is not an easy time to be a developer. Due to ongoing talent shortages and budgetary constraints, devs are tasked with more responsibilities than ever before. Meanwhile, the applications they work with are growing more and more complex. Devs are also under pressure to get up to speed with new tools, programming languages and technologies like generative AI (GenAI) to keep up in this highly dynamic environment. In lieu of these factors, every DevOps team needs to take a hard look at its processes and tech stack heading into the new year. Supporting developer productivity in this high-pressure environment requires the right combination of technology and strategy.
Here are a few tools and best practices DevOps should consider in a bid to foster productivity, improve developer experience and drive innovation in 2025 and beyond:
AI-powered tools, particularly GenAI tools, can offer substantial productivity gains if leveraged properly. For example, technologies like coding assistants have been shown to increase developer productivity by up to 45%. In addition to helping with code generation, AI-driven tools can aid in application testing, anomaly detection and predictive maintenance, infrastructure optimization and accelerating continuous integration and deployment (CI/CD).
However, DevOps needs to bear in mind that AI-powered tools are not infallible. They make observability tools (more on this next) even more necessary for catching potential errors before they go into production. DevOps will increasingly need AI-powered tools to stay competitive, but there is still a strong need for human oversight. Finally, DevOps teams should be discerning about which -- and how many -- AI tools they introduce into their tech stack. AI only makes sense for certain use cases, and in some situations, introducing AI can create more work for devs (e.g., more code to review). So, DevOps must weigh the costs and benefits carefully.
Observability tools are crucial for enhancing developer productivity, improving application performance and reducing costs for DevOps teams. The deep visibility these tools provide supports productivity by granting developers end-to-end visibility into their applications and infrastructure along with insights into how everything is performing. This helps fine-tune and optimize their systems as needed.
Additionally, observability tools help developers take a proactive approach to performance by rapidly identifying and remediating any application issues, reducing mean time to resolution (MTTR) and supporting overall application performance. By identifying inefficiencies and reducing application downtime, observability tools end up saving DevOps teams both time and money.
Platform engineering is the practice of creating and maintaining internal developer platforms to streamline the software development process. It helps developers automate their workflows, standardize their tools and offer self-service capabilities so that they can spend more time building features and less time managing infrastructure. The benefits of this strategy are plentiful, so it is no surprise that platform engineering adoption is on the rise and expected to be implemented in 80% of engineering firms by 2026.
Now is the time for DevOps teams to solidify their platform engineering strategy to improve the developer experience and boost productivity. As a first step, they should take inventory of the devs' biggest pain points to determine the problems the platform will address -- whether it is related to tool selection and management or infrastructure. From there, they can determine which workflows, tools and infrastructure components the platform will consist of, and assess their current tooling and workflows to determine areas for standardization and improvement. The goal is to build consistent workflows and practices that supercharge productivity and uphold software quality.