INDUSTRY INSIGHTS

Platform Engineering: Rapid Adoption and Impact

Discover the growing importance of platform engineering and the significant adoption levels within IT organizations. Learn how DevOps and platform engineering complement each other. Explore the drivers, objectives, and measures of success, as well as the future challenges and opportunities presented by AI.

The Rise of Platform Engineering: Unleashing Developer Productivity in the Age of DevOps

Platform engineering has quickly gained traction within IT organizations. It’s fast becoming an established practice for DevOps and software development teams, as shown by our survey and other research organizations’ findings.Platform engineering emerged to address the “forgotten developer,” lost with the heightened emphasis on agile, cross-functional teams and DevOps pipelines. With the economic downturn over the past 12 to 18 months, software leaders were forced to do more with less. This shifted focus to making developers more productive, but a clear path to achieve greater productivity was lacking. Some industry pundits confidently declared that DevOps was dead and pointed to platform engineering as its replacement. That couldn’t be farther from the truth. In reality, platform engineering has gained its own focus and set of engineering disciplines that are complementary to DevOps.

Platform Engineering: Transforming DevEx, Speeding Up the SDLC

Because of the complementary nature of platform engineering and DevOps, the emergence of platform engineering is timely. Platform engineering takes a holistic view of developers and the environments they work in and establishes practices around internal developer platforms (IDPs). IDPs are, in turn, defined around improving developer experience (DevEx). The rise in platform engineering directly addressed hits to software dev cycles due to the complexities of IDEs, plugins, toolchains, repositories, environment creation, and incompatibilities.

What platform engineering objectives are most important to your organization?

In fact, virtually all of the platform engineering objectives rated most important relate to DevEx and improving productivity for developers. The three highest-ranked objectives were self service for developers (29%), easy adoption (25%), and meeting developer needs (20%) (Figure 5).

  • As DevOps grew in popularity, platform engineering’s rise to prominence was often attributed to the “We forgot about the developers!” phenomenon. While there are many positives, developer productivity strongly influences the creation of self-service tools and infrastructure, eliminating friction and bottlenecks and creating “developer-friendly” environments.

2024 and Beyond. What’s in store for platform engineering in the next 12 to 18 months?

First, it's important that platform teams equip themselves for ongoing, continuous change. Companies are acquired, new platforms are acquired (often without shedding old ones), and new applications and deployment patterns will continue to emerge.The most recent disruptive technology to arrive is generative artificial intelligence (AI), following closely on the heels of AI and particularly machine learning (ML). AI can and will be applied to improving platform engineering. However, like every other aspect of creating, operating, and securing software and systems, AI/ML and generative AI bring with them challenges. We see three significant ones:

Managing multiple large sets of data and models

The first is managing multiple large sets of data and models, the lifeblood of ML algorithms and generative AI large language models (LLMs). Like specialized expert systems, domain-specific LLMs trained on internal enterprise data will prove particularly important in adopting generative AI, provided data privacy and security are maintained.

Platform engineering must adapt

Second, platform engineering must adapt to new AI workflows and pipelines for data, prompts, and the AI engineers who design, train, and maintain models, vector databases, and large datasets as they grow and evolve. These AI pipelines must support particulars of their workflow patterns and coincide and integrate with interdependent software development pipelines and release processes.

AI/ML and generative AI

Lastly, AI/ML and generative AI often have operating characteristics separate and apart from the cloud and application environments we understand and operate today. AI brings new hardware operating environments, including AI accelerators, GPUs, VPUs, and highly scalable CPUs, and challenging performance and optimization learning curves. Platform engineering will play a crucial role as AI, particularly generative AI, is adopted and mainstreamed in enterprises.

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