What Is a Feature Store?
Infrastructure that computes, stores, and serves the input signals ML models use to make predictions — bridging raw data and real-time decisions.
What the model sees
Batch-refreshed. Frozen since last pipeline run.
What's actually happening
Continuously computed. Fresh at decision time.
The Problem: Feature Drift
Without shared infrastructure, teams compute features ad-hoc — one version in training, another in production, a third in a dashboard. The values diverge. Models degrade silently.
Actual value right now
Training notebook
31
Computed last Tuesday
Production API
38
Cache from 2h ago
Batch dashboard
44
Last night's run
Three systems. One feature. Three different answers. This is drift.
What a Feature Store Does
A feature store centralizes the full lifecycle: define features as named transformations, compute them consistently via batch or streaming pipelines, store historical values for training and current values for serving, and serve them to models at inference time with low-latency APIs.
One definition. One computation path. Every consumer — training jobs, production models, analytics dashboards — reads from the same source. Drift eliminated by design.
But most feature stores stop there. They serve pre-computed values from a cache — values that were fresh when the pipeline ran, but stale by the time the model reads them.
The Freshness Spectrum
For fraud, personalization, and dynamic pricing — the gap between "when the feature was computed" and "when the model reads it" is where value leaks.
Where It Matters Most
decision window
Fraud Detection
Evaluate transaction risk against behavioral features from the last few minutes — not last night's batch.
features per request
Personalization
Recommendations from live session signals, purchase history, and embeddings — updated as the user browses.
market signals
Dynamic Pricing
Prices that track demand, competitor data, and inventory — continuously, not hourly.
QPS in milliseconds
Risk Scoring
Credit and insurance features that reflect the applicant's most recent activity.
What to Evaluate
Freshness
Consistency
Training-Serving Parity
Semantic Operations
Traditional vs. Unified
When workloads demand continuous freshness or semantic reasoning, the architecture needs to collapse compute and serving into one boundary.
Traditional
Architecture
Separate offline + online stores
Freshness
Minutes to hours
Consistency
Eventual
Semantic ops
Bolt-on vector DB
Unified Context Layer
Architecture
Single system for compute and serve
Freshness
Continuous
Consistency
Transactional
Semantic ops
Native embeddings
Go Deeper
Feature Freshness, Explained
Why stale features silently degrade model performance
What Is an Online Feature Store?
Architecture, components, and how to build one
Do You Actually Need a Feature Store?
A decision framework for when a store pays for itself
How to Evaluate a Feature Store in 2026
The criteria that separate tools you'll outgrow from ones that scale
The Ideal Stack for AI Agents
Where feature serving fits in the agent infrastructure layer
Context Lake vs. Data Lake
How unified context layers differ from storage-first architectures
Why AI Agents Spin Their Wheels
What happens when agents act on stale context
What Is a Context Lake?
The infrastructure imperative for real-time AI
See how Tacnode approaches feature serving
Features computed directly from live data, inside a single transactional boundary. No batch pipelines. No sync. No stale snapshots.