Dedicated vs shared LLM inference: when reserved capacity makes sense

Shared, token-based LLM inference is generally well suited to prototypes, low-volume features, unpredictable demand, and teams that value elasticity, model choice, and zero idle costs, while dedicated inference reserves GPU capacity for a single customer in exchange for more predictable throughput, isolation from other tenants, controlled reliability under load, and potentially lower costs at sustained high utilization. The decision depends on demand patterns, latency sensitivity, rate-limit pressure, batch deadlines, and whether token spending is stable enough to justify capacity planning. Dedicated services often retain OpenAI-compatible APIs, allowing shared and reserved deployments to operate side by side, although implementation differences in streaming, tool calls, and parameters should be tested. A common scaled approach is hybrid deployment, reserving capacity for forecastable baseline traffic while using shared endpoints for spikes, especially for agent workloads whose repeated, long-context, multi-step requests can create steady demand and amplify tail-latency problems.

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