Gentleman Programming

This is how our inference architecture works at Helmcode. Serving inference in production is more than hitting the API you use. 🤷‍♂️ You need routing, fallbacks, observability, cost control, and the right deployment model. Here's a closer look at how we approach it at Helmcode: 1/ API Gateway & Routing Every request enters through an OpenAI-compatible API. We handle authentication, rate limits, and routing to the appropriate model and infrastructure. 2/ Model Serving Different models have different requirements. We run inference workloads across GPU infrastructure, managing model replicas, batching, and concurrency to balance throughput and latency. 3/ Scalability & Reliability Traffic isn't constant. The infrastructure needs to handle demand spikes, distribute requests across available capacity, and recover when something fails. 4/ Observability We monitor latency, token throughput, GPU utilization, errors, and request volume to understand how the system is performing and identify bottlenecks. 5/ Cost & Deployment Infrastructure decisions directly affect inference costs. Shared, dedicated, and on-prem deployments have different trade-offs in utilization, isolation, performance, and economics. All of this needs to work together while keeping the API simple for developers. This is how we make open model inference accessible to businesses at Helmcode.