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Build once, serve everywhere — the single source of truth for every ML feature.

FeatureVault

Centralizes the creation, storage, versioning, and serving of ML features for both online (low-latency) and offline (batch) consumption, eliminating duplicate feature computation across teams. Directly addresses the data inconsistency problem that causes production-training skew in traditional enterprise ML deployments.

Why It Matters

FeatureVault solves one of the most expensive hidden problems in enterprise ML — every team rebuilding the same features differently.
A centralized feature store makes features reusable, consistent, and correct, eliminating the training-serving skew that silently degrades production models.
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Kills training-serving skew

The same feature definitions serve both batch training and low-latency online inference, so models behave in production exactly as they did in training.

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Build once, reuse everywhere

Teams stop duplicating feature engineering — features become shared, versioned assets, cutting compute costs and accelerating every new model.

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Point-in-time correctness for regulated data

Accurate historical feature retrieval — which Vertex AI Feature Store lacks for complex banking transaction data — prevents data leakage and satisfies model-risk auditors.

The Cloudly Advantage

FeatureVault is the data backbone of Cloudly’s MLOps platform story — the piece that makes TrainForge, ExperimentHub, and AutoML Accelerator work on consistent, trusted data.
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Completes the platform narrative

With FeatureVault, Cloudly pitches the full enterprise ML stack — features, training, tracking, governance — as one integrated engagement no point-solution vendor matches.

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High-value banking wedge

Point-in-time correctness for transaction data is a sharp, defensible differentiator against Vertex AI in banking deals — our strongest regulated-industry talking point yet.

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Deep Cluster Services revenue

Redis, Hive, and Spark infrastructure is exactly what our Cluster Services consultancy builds and operates — every deployment is a substantial services engagement.

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Pulls through AWS Redshift work

Offline store integration opens AWS Migration and Monitoring conversations in every account, with BigQuery covering GCP-side deals.

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Enterprise-wide expansion motion

Feature stores serve many teams by design — landing one use case naturally expands the contract as more teams onboard.

The Final Takeaway

Sticky operational revenue — Once production models depend on FeatureVault for online serving, it becomes mission-critical infrastructure — driving long-term managed-service and AIOps monitoring contracts.

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Feast

Core to the FeatureVault technology stack.

Redis (online)

Core to the FeatureVault technology stack.

Apache Hive/Spark (offline)

Core to the FeatureVault technology stack.

AWS Redshift

Core to the FeatureVault technology stack.

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