# The Glue Problem in Modern AI Development

DevFeed: [The Glue Problem in Modern AI Development](<https://devfeed.tech/articles/the-glue-problem-in-modern-ai-development-19881.md>)

Original publisher: [Read original article](<https://www.digitalocean.com/blog/glue-problem-ai-development>)

Author: James Skelton

Published: 2026-04-02T21:30:07Z

Content type: article

Language: en

Sources: [DigitalOcean](<https://devfeed.tech/sources/digitalocean.md>)

Topics: [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [AI Platform](<https://devfeed.tech/topics/ai-platform.md>), [Development](<https://devfeed.tech/topics/development.md>), [Cloud](<https://devfeed.tech/topics/cloud.md>), [Deployment](<https://devfeed.tech/topics/deployment.md>), [Orchestration](<https://devfeed.tech/topics/orchestration.md>), [Inference](<https://devfeed.tech/topics/inference.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [ai-development](<https://devfeed.tech/tags/ai-development.md>), [ai-ml](<https://devfeed.tech/tags/ai-ml.md>), [cloud](<https://devfeed.tech/tags/cloud.md>), [deployment](<https://devfeed.tech/tags/deployment.md>), [developers](<https://devfeed.tech/tags/developers.md>), [efficiency](<https://devfeed.tech/tags/efficiency.md>), [engineering](<https://devfeed.tech/tags/engineering.md>), [fragmentation](<https://devfeed.tech/tags/fragmentation.md>), [infrastructure](<https://devfeed.tech/tags/infrastructure.md>), [orchestration](<https://devfeed.tech/tags/orchestration.md>), [overhead](<https://devfeed.tech/tags/overhead.md>)

## AI overview

The article examines how fragmented AI infrastructure creates a glue-code problem for developers. It argues that integrated cloud platforms can reduce integration work, operational overhead, and total cost of ownership when deploying and scaling AI applications.

## Source excerpt

AI is now central to modern software development. Teams across industries are turning to AI to solve product and workflow problems in software. But building production systems is still complex. The hardest part of deploying AI isn't the model, it's everything around it. That complexity becomes a glue-code problem when storage, compute, orchestration, networking, authentication, and inference live in separate systems with different operating models. The more seams a workflow crosses, the more developer effort shifts from building product logic to wiring services together. A more integrated platform model reduces that burden. This article examines what it takes to deploy and operate AI applications in today's cloud landscape. Using two examples, we will compare the process in two landscapes: a neocloud combined with a hyperscaler versus a vertically integrated cloud stack. While surface-level costs may look similar, the integrated model offers clear advantages in efficiency by reducing the time developers spend writing glue code and managing the problems that emerge as AI products scale. Key Takeaways The biggest cost in AI systems isn't infrastructure: it's integration. Fragmented, multi-provider stacks force developers to spend time writing and maintaining glue code instead of building product features, turning engineering effort into the real cost center. Raw infrastructure pricing is no longer the differentiator; total cost of ownership is. Even when platform costs are nearly identical, the added complexity of cross-cloud orchestration increases operational overhead, failure points, and staffing requirements at scale. The future of AI platforms is vertical integration, not more tools. Platforms that unify compute, storage, and inference reduce friction, accelerate development, and allow smaller teams to build and scale AI applications more efficiently. The Real Problem Is Fragmentation Consider the modern landscape for AI deployment. AI applications rely on far mo