In the rapidly evolving world of generative AI applications, composable architecture is emerging as a framework for building scalable AI-powered applications. Integrating independent, modular components via APIs enables developers to create customised solutions quickly.
“When we talk about composable architecture, we really mean building larger systems by assembling smaller, independent modules that can be easily swapped in and out,” explained Jaspinder Singh, principal consultant at Fractal, during an interaction with AIM.
Designing for Scale with Composable Architecture
This modular approach enables developers to build applications by combining several smaller modules, providing flexibility, scalability, and enhanced control over the deployment of AI solutions.
“Not every project needs this approach,” Singh pointed out. “If you’re building something small and straightforward, you might be overthinking it if you try to make everything modular. But if you are planning to scale up, if lots of people will use your application, that’s when composable architecture really shines.”
Scalability is one of the primary benefits of composable architectures. Singh emphasised that individual modules can be scaled independently based on demand, optimising resources for generative AI applications.
For example, a data processing module might require more frequent scaling than a front-end user interface. This selective scaling manages costs by avoiding unnecessary resource allocation.
The composable architecture is designed to allow for rapid experimentation and granular control, which are especially useful in the fast-paced world of generative AI. With new AI models appearing frequently, the ability to integrate and test them with minimal disruption to the overall system ensures that applications stay relevant and current.
The composable paradigm also allows a balance between custom development and leveraging off-the-shelf modules. Using modular APIs and established components for routine tasks allows developers to focus on refining specific business logic, reducing time to market and enabling faster iterations.
“Companies can’t afford to spend months building everything from the ground up anymore,” according to Singh. “These modular components let you move quickly and stay competitive, especially in fast-paced tech-powered industries.”
Integrating Foundation Models in Generative AI Systems
Foundation models serve as fundamental building blocks in composable generative AI systems. These models, which serve as a base layer, can be fine-tuned or augmented for specific tasks, providing a versatile starting point within modular applications.
A content creation system exemplifies this flexibility: organizations can integrate GPT-4 for text generation alongside image generation models like Flux-pro, resulting in a seamless workflow. This modular approach enables strategic combinations of best-fitting AI capabilities.
According to Singh, the output from each model can be routed to specialised modules for further processing, such as plagiarism detection, grammar correction, or style enhancement. This results in a robust but flexible workflow in which each component performs its specialised function while maintaining system cohesion.
The architecture excels in adaptability, and organizations can improve or replace individual components as technology evolves, ensuring that their AI systems remain current without requiring complete rebuilds.
Architecting Better Prompt Management
Prompt engineering is critical in generative AI applications, but managing and optimising prompts at scale poses significant challenges. Composable architecture addresses this issue by treating prompt management as a separate module within the overall system.
“We have seen organisations struggle with prompt consistency and version control,” Singh points out. “By incorporating a centralised prompt library into the composable architecture, teams can standardise their approaches while remaining flexible. This is especially useful when combined with experimentation features like A/B testing prompts, models, and data variations.”
The composable architecture enables this structured approach to prompt management, monitoring, and model evaluation by allowing developers to manage each of these activities within their own modules.
Security and Compliance in Composable Generative AI Systems
While composable architectures provide increased flexibility, they also present unique security and compliance challenges. The distributed nature of modular generative AI systems requires data security management, as sensitive data may flow through multiple modules.
Compliance with data protection laws is critical, especially when data needs to move beyond an organisation’s infrastructure. In such cases, only necessary data should be transferred, with all confidential information handled securely in-house.
Moreover, generative AI models may be vulnerable to adversarial attacks, in which malicious inputs attempt to manipulate model behaviour. Singh recommends that input and output vetting should be a regular part of the composable AI pipeline, along with secure communication channels and access control mechanisms. A strong data governance framework, as well as regular security audits, help to ensure the security of application environments.
Composing the Way Forward
The flexibility of composable architectures offers a promising path forward for generative AI applications. As standardised interfaces evolve, Singh highlights that organisations can avoid vendor lock-in and experiment with competing AI solutions to find those best suited to their needs.
The modularity of composable architectures facilitates a low-code or no-code approach, making AI development more accessible and accelerating the adoption of generative AI across industries.
However, implementing composable architectures can be challenging. Integrating multiple modules and transitioning from experimental to production environments presents challenges, especially as AI tools and technologies advance rapidly. Data privacy, intellectual property rights, and model reliability remain key areas of focus, demanding ongoing attention as organisations scale their generative AI applications.
Singh recommends comprehensive monitoring throughout the AI application lifecycle, from ideation to deployment, to ensure that modular generative AI systems operate seamlessly. Observability frameworks and GenAIOps practices can track metrics such as model accuracy, application performance, and cost efficiency. This would provide a comprehensive view of the system’s health and aid in the development of generative AI solutions that are both reliable and effective.
By embracing composable architectures, organisations can position themselves to adapt swiftly to AI’s evolving landscape, benefiting from the enhanced flexibility, scalability, and security that modular systems provide.
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