Replay production requests against new models before switching. Evolve prompts with genetic optimization. Gate deployments on quality scores. Detect hallucinations before they reach users.
Pick any traced request. Replay it against a different model. Compare cost, latency, and output side by side. Share the comparison.
Feed your best prompts into Breed. It crossovers, mutates, and tournaments them against LLM judges until the winner emerges.
Set quality thresholds. Doubt flags hallucinations. Verdikt blocks bad responses. Crucible scores confidence. Nothing ships without passing.
Lasso replay showed gpt-5.4-mini scored 0.91 quality on summarization tasks vs gpt-5.4 — same quality, 6.6x cheaper. Shared the comparison on X.
See the data →Most LLM observability tools show you what happened after the request completed. Stockyard shows you what is happening inside the request as it flows through the middleware chain. The trace module records timing for every middleware step — cache check, guardrail evaluation, provider selection, response processing — so you can see exactly where latency comes from. The replay module lets you re-run any historical request through the current middleware configuration, which is how you test guardrail changes without deploying to production.
Quality monitoring through the assay module runs automated checks against model responses. Define assertions — response must contain certain keywords, must not exceed a token count, must parse as valid JSON — and the system flags failures. This catches model regression before your users notice. Combined with prompt version control, you can track which prompt version produces which quality metrics over time, turning prompt engineering from guesswork into measurable iteration.
Install Stockyard, send a request, watch it flow through the middleware chain. Everything on this page starts working immediately.