The data platform for inference

Build, deploy, and iterate faster
with Chalk's feature engine
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MoneyLion Customer Story
Ramp
Melio
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Vital
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Turo

See Chalk in action

Discover how Chalk helps teams build real-time models for mission critical operations.

Machine learning infrastructure is painful

Chalk makes it simple for data teams to focus on building the unique products and models that make their businesses thrive.

Deploy to your own infrastructure.

Use your existing database as your online + offline store. No bespoke storage. Everything in your cloud.

High-volume workloads at ultra-low latency.

Chalk’s Compute engine scales horizontally out-of-the-box and executes most complex queries on a Rust-based runtime for maximum performance. 100,000 QPS in < 5ms? We have you covered.

Power real-time decisions with real-time data.

Make better predictions with fresher data. Don’t pay vendors to pre-fetch data you don’t use. Query data just-in-time for online predictions.
user
chalk
query
get_plaid
Apple Watch Series 3
$299.00
Processing...
from chalk.api import ChalkClient ChalkClient().query({ inputs={ Transfer.user.id: 182831, Transfer.amount: 299.00 }, outputs=[Transfer.user.score], })
get_credit_report
name_match_score
is_income_txn
get_user
get_plaid
get_failed_logins
login_counter
200 OK { name: "J.J. Chusterton", age: 28, accounts: [ "Bank of America", "Chase", "First Republic" ] }

Perfect auditability

Know everything you computed and data replay anything.
Parallel Resolvers
This operator executes your Python code in Chalk's massively parallel low-latency runtime environment.
Execution time
4ms
Self time
<1ms
Result size
10MB
Result
500k rows
Groups
4
Groups size
5MB
Runtime
Rust
Resolvers
Features
Output
FeatureValueValue
01
pkey
1
2
02
recent_tx_amts
[130, 24, 87]
[999, 0, 0]
03
fraud_score
0.26
0.13
04
fraud_id
cle2k1
clle09
05
tx_distribution
norm
unif
06
authorization_code
83823
19231
07
authorized
true
true
08
name_match_score
0.99
1.0

Unify training and serving. Iterate faster.

Experiment in Jupyter, then deploy to production.
Prevent train-serve skew and create new data workflows in milliseconds.

Detect, troubleshoot, and eliminate data issues

Track data use, drift, and quality effortlessly with observability—built right in.
Jupyter Notebook
In []
df = client.offline_query( inputs={User.id: user_ids}, outputs=[ User.name, User.credit_report, User.account.bal, ] )
In []
# xgboost train / predict xgb = XGBClassifier( eval_metric="logloss", use_label_encoder=False )
dashboard.chalk.ai

Integrations

Integrate with the tools you already use and deploy to your own infrastructure.

Blog + News

The Chalkboard

MoneyLion Customer Story

Powering personal finance products with Chalk
May 5, 2025
The Chalkboard

Chalk on the Move: April Events Recap

May 1, 2025
CB Insights

Chalk AI named to CB Insights AI 100 list for 2024.

April 2, 2024
The Information

What Separates the AI Winners and Losers; The General Catalyst-Backed Startup Taking On Databricks

December 12, 2023
Get Started with Code Examples
Unlock the power of real-time data pipelines. Explore all examples 
Withdrawal Model
Fraud & Risk
Decide and enforce withdrawal limits with custom hold times.
Income
Credit
Compute income from Plaid transactions.
Cache Busting
Caching
Bypass the cache with a max-staleness of 0.
Device Data
Predictive Maintenance
Easily listen to streaming data and parse messages with custom logic.

Start building with Chalk

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