Chalk is a real-time feature store and AI data platform built by Chalk AI. It helps data science and ML engineering teams define, compute, and serve features for machine learning models and, increasingly, AI agents. The platform targets teams that need both offline training data and low-latency online inference from a single codebase.
Chalk originally positioned itself as a feature store. Its current messaging describes it as an AI data platform delivering real-time context and compute infrastructure for agents and models. The platform deploys inside the customer’s own cloud rather than a separately hosted managed store.
Developers define feature schemas and transformations in standard Python. Chalk’s execution engine, built in Rust and C++, handles data fetching, temporal window updates, caching, and serving. It performs point-in-time as-of joins to build training datasets without data leakage, and the company states it can serve queries at under 5 milliseconds even at high throughput.
Chalk was founded in 2022 and is headquartered in San Francisco. The company has raised 60 million dollars across a seed and Series A round. Its Series A was a 50 million dollar raise led by Felicis at a 500 million dollar valuation, with participation from Triatomic Capital, General Catalyst, Unusual Ventures, and Xfund.
Pricing
Chalk does not publish pricing tiers, free trial terms, or usage-based rates on its website. There is no public self-service signup flow. Prospective customers must book a demo and receive a custom quote from the sales team, which is typical for infrastructure sold to enterprise fintech and risk teams.
* Disclaimer: Please note that pricing information may not be up to date. For the most accurate and current pricing details, refer to the official website.
Key Features
- ✓
Declarative feature definitions written in standard Python with type hints
- ✓
Low-latency online serving through a Rust and C++ execution engine
- ✓
Point-in-time correct training data via automated as-of joins
- ✓
Native temporal aggregation windows computed incrementally without extra orchestration
- ✓
Unified engine for batch, streaming, and on-demand compute
- ✓
Built-in observability for feature freshness, latency, and data drift
Use Cases
Fraud and Transaction Risk
Teams evaluate payments using velocity checks, device fingerprints, and time-windowed aggregates. Chalk computes these risk features and serves them to fraud models before a transaction is approved.
Credit and Underwriting Decisions
Lenders aggregate bank statements, bureau updates, and live credit scores for loan applications. Chalk generates point-in-time features for backtesting alongside instant live underwriting requests.
Real-Time Personalization
User profiles update continuously from clickstream events, session length, and purchase history. Chalk serves fresh context vectors to recommendation models during active browsing sessions.
Dynamic Pricing and Yield
Ride-sharing, travel, and delivery platforms calculate prices from localized demand and supply signals. Chalk aggregates streaming data across rolling windows to feed pricing models.
Real-Time Context for LLMs
Teams feed live account state and operational data into LLM prompts. Chalk retrieves up-to-date feature values to support retrieval-augmented generation pipelines.
Strengths & Weaknesses
Strengths
Native Python syntax lets teams define production features without a proprietary query language.
The Rust and C++ engine targets sub-5ms query latency at high throughput for real-time inference.
Automated point-in-time joins prevent data leakage when building offline training sets.
Chalk holds SOC 2 Type 2 and ISO/IEC 27001:2022 certifications for enterprise security requirements.
Deployment inside the customer’s own AWS or GCP environment avoids a separate managed data store.
Weaknesses
Pricing tiers and dollar amounts are not published anywhere on the website.
Teams used to SQL-based batch pipelines may face a learning curve adopting Python-first workflows.
There is no public self-service signup, so onboarding requires a sales demo.
Detailed concurrency limits and throughput benchmarks beyond marketing claims are not published.
Who Is This For?
Machine learning engineers and data scientists who want to write production features in Python without a separate serving pipeline.
Fintech and fraud risk teams that need sub-5ms feature serving for instant credit and fraud decisions.
E-commerce and consumer app teams that need real-time user context for recommendations and ranking.
Enterprise data infrastructure teams looking to centralize feature governance and cut duplicate pipeline work.
Frequently Asked Questions
What does Chalk actually do?
Chalk lets teams define machine learning features once in Python and compute them for training, batch scoring, and real-time inference from the same code.
How much does Chalk cost?
Chalk does not publish pricing. Interested teams must contact sales through a demo request for a custom enterprise quote.
Does Chalk require rewriting feature logic for production?
No. Features are defined once in Python, and Chalk’s Rust and C++ engine executes them for low-latency production serving.
How does Chalk prevent data leakage in training data?
Chalk runs point-in-time as-of joins so feature values reflect exactly what was known at the historical moment of each event.
Can Chalk read from both batch warehouses and streaming sources?
Yes. Chalk unifies batch connectors like Snowflake with real-time streaming sources under one feature resolution model.
Where does Chalk deploy?
Chalk deploys inside the customer’s own AWS or GCP environment rather than a separately hosted vendor store.
What security certifications does Chalk hold?
Chalk is certified under SOC 2 Type 2 and ISO/IEC 27001:2022, with data encrypted at rest and in transit.
Who uses Chalk in production?
Chalk’s customers include fintech, fraud prevention, and consumer platforms such as Whatnot, Socure, Turo, and Mission Lane.
How much funding has Chalk raised?
Chalk has raised 60 million dollars total, including a 50 million dollar Series A led by Felicis at a 500 million dollar valuation.
Is there a free trial available?
No free trial or self-service tier is listed publicly. Access starts with a sales demo.
Chalk lists integrations with PostgreSQL, Snowflake, AWS, Google Cloud, Databricks, Jupyter, Datadog, PagerDuty, Slack, and Apache Airflow on its current site. Underlying platform documentation also references support for streaming and cache connectors such as Kafka, Kinesis, Redis, DynamoDB, and Amazon S3.