Feature Store
Features that reflect what's true now — not what was true when the pipeline last ran.
The problem
Your model isn't wrong. The feature state is. Most feature stores compute in batch, materialize elsewhere, and sync into serving systems. By the time your model reads the feature, it reflects a past state — not decision-time truth.
What breaks in real systems:
- Velocity counters lag — your feature says 2 transactions, there are 5 in flight
- Pipelines update seconds or minutes after state changes
- Offline and online features diverge under load
- Concurrent requests see different feature snapshots
Two parallel evaluations both read utilization = 32%. One approves and increments to 40%. The other still sees 32% and also approves. Fast feature serving on stale state is just well-documented mistakes.
How Tacnode solves it
Tacnode computes and serves features directly on live system state. No warehouse. No reverse ETL. No sync gap.
What this means:
- Features update as data changes — not when pipelines run
- All services read from the same committed snapshot — not independent caches
- Feature read and mutation happen in the same transactional boundary
This is not a batch feature store:
- Live — features reflect current state, not last pipeline execution
- Transactional — concurrent requests see consistent values
- Unified — no offline/online split, no training-serving skew
- Direct — no warehouse copy, no reverse ETL loop
No warehouse. No reverse ETL. Features computed and served from one authoritative state.
Key Capabilities
Live Feature Computation
Features reflect state at decision time, not pipeline time.
Transactional Consistency
Concurrent model evaluations read from the same committed snapshot.
Atomic Updates
Feature read and mutation in the same transactional boundary.
No Sync Gap
No reverse ETL. No warehouse copies. No propagation delay between computation and serving.
How it works
Architecture Highlights
- Features computed and served from identical live state
- No offline/online split — one source of truth
- Training and serving read from the same state model
- Transactional consistency under concurrent load
- No reverse ETL pipelines to build or maintain
When you need this
- Models make decisions during live user interactions
- Features depend on rapidly changing state
- Multiple services share feature state
- Decision latency budgets under 100ms
When you don't
- Batch predictions run hourly or daily
- Static datasets with infrequent updates
- Offline experimentation only
- No shared mutable feature state
Common Patterns
Fraud detection
Velocity features must reflect transactions in flight, not transactions from the last pipeline run.
Personalization
Session intent and inventory must reflect current state, not cached snapshots.
Credit decisioning
Utilization features must update atomically during approval, not after.
Related
Capabilities
- Live feature computation
- Transactional consistency
- Decision-time serving
Integrations
- ML frameworks
- Model serving
- Real-time inference
Documentation
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