Agentic workloads are fundamentally different from simple chat completions. A single user request can trigger a multi-step loop where the model reasons, emits tool calls, waits for external results, and appends everything back into the context window for the next iteration. Each turn inflates the prompt length, and on token-based inference platforms, that inflation maps directly to cost. If you are building autonomous agents, the pricing model matters as much as the model weights. Oxlo.ai treats each API call as a single flat-cost request, which changes how you should design, optimize, and scale agentic systems.
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