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

A global fintech uses Together’s Dedicated Model Inference platform to run its internal AI coding assistant on GLM 5.2, supporting unpredictable engineering-hours demand that made traditional capacity planning ineffective. Because the workload has relatively low request volume but large prompts and sharp concurrency bursts, the deployment prioritizes concurrency headroom across dozens of B200 GPUs rather than maximum raw throughput. DMI gives the company self-service endpoint provisioning, scaling, configuration, model upgrades, blue/green testing, and programmatic metrics access, reducing dependence on support tickets and central platform-team coordination. During a production queuing issue, metrics identified a pending-prefill backlog rather than insufficient compute capacity, allowing the team to adjust cache-aware routing and worker limits through a live, zero-downtime configuration change. The company migrated from GLM 5.1 to GLM 5.2 on the same account, chose 256K or 512K contexts over a 1M-context option to preserve concurrency, and is now planning a separate regional deployment for a customer-support NLP workload.

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