AI/ML
Build, run, and scale AI on live data.
Enable end-to-end AI systems that operate directly on real-time and historical data within a unified platform.
AI/ML on a unified, real-time data plane
Most AI systems depend on disconnected pipelines and static datasets, leading to outdated models and delayed decisions. MonkDB enables AI to operate directly on live data.
What MonkDB makes possible for ai/ml
Train and infer on continuously updated datasets
No batch windows, no stale snapshots. Models retrain and infer on the latest state.
Combine structured + unstructured + vector data seamlessly
Hybrid retrieval across modalities in one query.
Deploy models without external data movement
Inference and training happen where the data lives.
Maintain real-time context for accurate predictions
Live state updates flow into model context with no pipeline lag.
Three steps, one continuous loop
Train on continuously updated data
No batch windows. Models retrain on the latest state with no snapshot drift.
Serve inside the engine
Inference runs alongside SQL and vector retrieval. No data movement to a model server.
Close the feedback loop
Live outcomes flow back into the same store. Drift is observed, not surprised.
We collapsed our feature store, vector DB, and serving stack into MonkDB. Models now retrain hourly on production data, not last week’s extract.
- 4×Retraining frequency
- 60%Pipeline systems retired
- <5 msP99 inference latency
More ways to put MonkDB to work, in your domain
Build AI on live data, not yesterday’s snapshot.
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