Do I need a managed feature store to get started?

No. Start with a real registry (even if it’s Feast + Git), enforce point-in-time correctness, wire metrics and tracing, and define SLAs. You can move to Tecton or Databricks Feature Store when you outgrow the basics.

How do I prevent training-serving skew?

Use one feature definition and compute code path for both training and serving. Enforce point-in-time joins for training, TTL for online freshness, and versioned transformations. Test backfills against leakage.

What should I alert on?

Feature retrieval p95 and error rate, feature freshness p95, missing rate by feature, PSI/KL skew vs. training, model latency/error, and validated-output rate for LLMs. Tie alerts to SLOs and automate rollbacks for canaries.

Where do guardrails fit for LLMs with RAG?

Before generation (filter low-relevance docs, rate limit), during generation (schema validation, constrained decoding), and after generation (moderation, re-ask or fallback). Track a validated-output SLO and burn an error budget like any SRE practice.

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

Feature Stores That Don’t Lie: Shipping Consistent Features With Guardrails, Not Excuses

Your model isn’t flaky—your features are. Build an online/offline feature architecture with traceable freshness, drift alerts, and circuit breakers so AI doesn’t torch your SLOs.

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Feature Stores That Don’t Lie: Shipping Consistent Features With Guardrails, Not Excuses

Key takeaways

Implementation checklist