AI features are rapidly becoming table stakes for applications, but we're discovering that the traditional cloud-first approach isn't always the optimal path forward. Through our journey at Slipbox, we've been exploring a different paradigm: a hybrid architecture that thoughtfully combines on-device and cloud processing. Like many in the field, we initially defaulted to pushing everything to the cloud, but experience taught us that intelligent systems need more nuanced approaches to truly scale sustainably.

What we've discovered is both humbling and exciting. By balancing local compute with cloud resources, we've seen transformative results: 11% Word Error Rate for on-device transcription and 50% cost savings through strategic use of Small Language Models (SLMs). This architectural shift isn't just about technical optimization; it's about reimagining how AI applications can grow sustainably while maintaining their promise to users.

The emergence of more capable SLMs, combined with the rapid evolution of on-device compute capabilities (particularly with chips like Apple Silicon), suggests we're just at the beginning of this transformation. As we continue to explore this space, we're excited to see how the industry evolves and adapts to this new paradigm.

If you're interested in diving deeper into our findings and the technical implications of this architectural approach, you can read our full blog at ✨ https://www.slipbox.ai/blogs/hybrid-agent-architecture