Cloud platforms are overwhelming for the average developer. And they seem to get more complex year after year, with more products that very few people understand and can use. But now, with AI agents becoming the main interface between developers and computation infrastructure, hyperscalers have a unique chance to make things easier for devs.
For a significant portion of my applications (AI-centric), I would rather pay to access a variety of AI agents — be they generic or specialized — over traditional VMs and bare metal solutions.
The rationale is straightforward: the cost-to-benefit ratio of using a cloud VM just doesn't add up. The common practice of segmenting cloud computational resources into CPU cores, GPUs, and RAM stands as an impractical model for developers.
Unlocking the full potential of a specific allotment of CPU/GPU/RAM demands a degree of specialization and commitment far exceeding what most developers are prepared to invest. This challenge in leveraging maximum value plays a significant role in prompting individuals to revert to local solutions away from cloud computing.
A more appealing approach to retailing cloud-based computational resources to the broad developer community doesn't involve CPUs, GPUs, or RAM — it revolves around providing a dependable trio: CLI, API, and Web Platform. The precise number of CPU cores required for my application to function smoothly is not my primary concern. Above all, my priority is ensuring its operational reliability for end-user access, followed by its capability to recover and reboot in the face of any malfunction. The appropriate CPU core allocation naturally follows from the prioritization of service quality (QoS).
What about situations where CPU usage optimization is imperative? The solution is to simply integrate this requirement with the AI agents tasked with your application's design. This avoids the inefficient expenditure of time sifting through the myriad VM options provided by major cloud service providers.
Here's my vision for an ideal cloud-offer framework for developers:
- Streamlined Onboarding: The process begins with user registration via their GitHub, GitLab, or Atlassian accounts.
- Dedicated Project Spaces: Each project is assigned a comprehensive workspace containing all pertinent materials—documentation, analytical reports, meeting summaries, datasets, and exclusive code not available on external platforms, among others.
- AI-Powered Flexibility: The AI agents are granted complete autonomy to utilize, modify, and generate new files within this designated folder.
This framework aims to break down the complexities traditionally associated with cloud computing, providing an intuitive, efficient, and developer-centric cloud experience.
AI agents will soon make the cloud as easy to use as Dropbox.
Top comments (0)