Feature stores · Chalk AI

Chalk

Real-time feature platform that computes and serves ML features from Python definitions, deployed inside the customer's own cloud.

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Chalk is a feature platform for teams running machine learning and LLM-agent inference in production. Engineers declare features and their dependencies in Python; Chalk compiles those definitions into pipelines that run across streaming, batch and on-demand contexts, using the same source code to produce point-in-time-correct training sets and low-latency online feature values, which is the core problem feature stores exist to solve: keeping training and serving features consistent. It targets low-latency use cases such as fraud detection, underwriting and real-time recommendations, claiming sub-5ms serving for complex feature computations at high query volumes. Unlike the managed feature-store modules built into Databricks, SageMaker or Vertex AI, Chalk is deployed into the customer's own cloud account rather than fully hosted by the vendor, and is sold as a standalone product rather than bundled with a broader ML platform.

At a glance

Vendor Chalk AI
Pricing model Quote only
Free tier —
Deployment Cloud, Self-hosted
Open source No
Best for ML and fraud/risk teams needing low-latency, training-consistent features deployed in their own cloud.

Pricing

No public pricing; Chalk is sold through a sales conversation and deployed into the customer's cloud.

Pricing has not been verified yet — see the vendor's site.

Features

  • Features declared as Python with automatic dependency resolution
  • Same definitions used for training data and online serving
  • Streaming, batch and on-demand feature computation
  • Sub-5ms P99 serving latency for complex queries
  • Deploys inside the customer's own cloud account
  • Built-in observability for feature drift and data quality
  • Native integrations with existing operational databases

Integrations

Profile last reviewed September 21, 2026

Alternatives

Databricks Feature Store Feature registry and serving layer built into Databricks Unity Catalog, using Delta tables as feature tables. Amazon SageMaker Feature Store Fully managed feature repository inside SageMaker that stores, discovers and serves ML features for training and inference. Vertex AI Feature Store Managed feature-serving module inside Vertex AI that serves ML features from BigQuery data for training and low-latency inference.

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Terms to know

Machine learning Building models that learn patterns from data to make predictions or decisions, rather than following hand-coded rules. Data pipeline The automated sequence of steps that moves data from source systems to a destination, transforming it along the way. Data quality The degree to which data is accurate, complete, consistent and fit for the purpose it is being used for. Fraud detection Identifying transactions, claims, or behavior that are likely fraudulent, using rules, statistics, or machine learning.

Related guides

How to choose a feature store The real choice is whether your cloud or warehouse already has one built in, and whether you need sub-millisecond serving or just a shared registry.