Do we really need a feature store, or can we just query our warehouse in real time?

You can, but you’ll blow your latency SLOs and parity. Warehouses aren’t built for low-latency fan-outs or request-scoped consistency. A feature store gives you point-in-time correctness, an online cache, and a registry so batch and online use the same logic.

Why not compute features on the fly inside the model server?

For simple transforms, fine. But anything involving joins, windows, or late data becomes a reliability and latency nightmare. Centralizing transforms and materialization lets you monitor freshness, enforce TTLs, and decouple compute from inference hot paths.

How do we measure hallucination rate objectively?

For LLM flows, combine retrieval coverage (e.g., % of answers with at least 1 high-score document), schema validation failure rates, and human evals on a stratified sample. Track these as metrics and use them for canary decisions; don’t ship on vibes.

What’s the fastest way to de-risk the rollout?

Start with one high-impact model and 10 features. Implement parity tests and guardrails, run a canary + shadow for a week, and compare business KPIs (conversion, fraud catch rate) plus tech KPIs (P95 fetch latency, freshness, drift). Scale only after you see stability.

Ai-delivery · Oct 2, 2025 · 10 minute read

Your Model Isn’t Wrong—Your Features Are: Building a Feature Store That Doesn’t Drift at 2 a.m.

If your offline and online features don’t match, your “state‑of‑the‑art” model will look drunk in production. Here’s the feature store architecture and guardrails we ship when uptime and accuracy actually matter.

Back to all posts

Your Model Isn’t Wrong—Your Features Are: Building a Feature Store That Doesn’t Drift at 2 a.m.

Key takeaways

Implementation checklist