AI Engineer · October 31, 2026

Self-hosted agent evaluation: measure concurrency and cost per completed task

video Model Evaluation AI Engineering

Self-hosted agent evaluation: measure concurrency and cost per completed task video thumbnail

Why it matters

Steven Latré describes comparing coding agents across models, harnesses and GPU configurations using SWE-bench Pro. The method measures resolved tasks, latency, concurrency and hardware occupancy together: similar task scores can hide different token use and GPU time. The talk covers one workload and promises a broader public dataset; its break-even percentages are not portable purchasing guidance.

My takeaway: Replay representative tasks with fixed acceptance criteria and record model, harness, inference and hardware settings. Increase concurrent load while measuring completion quality, latency and memory pressure. Compare API spend with full hosting costs, including idle capacity, operations and retries. Rerun the comparison for your workload mix before treating a benchmark result as a capacity plan.

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