Why Price-Per-Token Is the Wrong Way to Pick an AI Model Endpoint

As AI agents move from demos to production, choosing the right model endpoint based solely on cost-per-token pricing is proving inadequate for real workloads. Agent traffic differs fundamentally from chat traffic, often arriving in bursts of parallel tool calls rather than steady streams, which can overwhelm endpoints optimised for throughput. Three key factors should guide the decision: traffic shape, data sensitivity, and the team's capacity to manage infrastructure. Free hosted tiers suit steady, non-sensitive, low-maintenance use cases, while self-hosting better serves spiky, latency-sensitive, or privacy-critical workloads. A practical stress-test script that measures success rate, rate-limit events, and latency percentiles at varying concurrency levels is recommended to evaluate any endpoint before committing to production.