Do I really need both an online and offline store?

If you need low-latency serving and point-in-time correct training, yes. The offline store provides accurate historical snapshots for backfills; the online store gives you millisecond reads. The feature store’s job is to keep definitions and transformations consistent across both.

Feast vs Tecton vs Hopsworks—what should I pick?

Feast is a solid open-source baseline with a straightforward mental model. Tecton and Hopsworks add managed infra, governance, and enterprise features. We’ve shipped all three; the choice usually comes down to team size, compliance requirements, and whether you want to run Redis/Dynamo yourself.

How do I keep LLMs from hallucinating in production?

Ground responses with retrieval (RAG), enforce output schemas and content filters, and measure retrieval coverage. Canary prompt/model changes and gate with policy checks. Feature stores still matter in LLM systems—your retrieval features and user context need the same consistency and freshness controls.

What’s the fastest path to value if we’re already live and messy?

Start by instrumenting: OTel traces, Prometheus metrics for freshness and skew, and a dashboard you trust. Then pull your top 5 features into a registry (Feast) and wire materialization to an online store with TTLs. You’ll see incident rates drop before you migrate everything.

Ai-delivery · Dec 8, 2025 · 10 minute read

Feature Stores That Don’t Drift: Shipping Consistent Features with Real Guardrails and Telemetry

If your model sees one world at training time and another at serving, it’ll hallucinate, thrash, or stall. Here’s the feature store architecture that actually holds up under pressure—plus the instrumentation and safety rails to keep you out of pager hell.

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Feature Stores That Don’t Drift: Shipping Consistent Features with Real Guardrails and Telemetry

Key takeaways

Implementation checklist