How a global fintech scaled coding agent traffic with Dedicated Model Inference

This post details how a global fintech scaled their GLM 5.2 coding agent traffic using Together AI's Dedicated Model Inference (DMI). Key contributions include: 1. Self-service endpoint provisioning and scaling, allowing engineering teams to manage capacity without platform team intervention. 2. Programmatic access to metrics via a Metrics API for root cause analysis of issues like prefill backlogs and queuing. 3. Model fluidity demonstrated by seamless transitions from GLM 5.1 to GLM 5.2 and context length adjustments on the same endpoint. 4. The customer's deliberate choice to prioritize concurrency over raw throughput for their peak-load, low-TPS workload by selecting 256K/512K context over 1M. 5. The successful migration of a customer-support NLP workload to the same DMI infrastructure in a new region, showcasing the platform's versatility.