Introduction

What is Chalk

An overview of the platform and what you can build with it.

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Core concepts

How Context, Runtime, and Learning fit together.

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Setup guide

Install the Chalk CLI, create a project, and set up credentials.

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Choosing where to run code

Choose between a sandbox, a scaling group, and a function.

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Context Engine

Quick start

Define, deploy, and query your first features.

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Features

Define features for training and inference.

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Resolvers

Write the SQL and Python that compute feature values.

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Data sources

Connect databases, warehouses, and APIs.

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Queries

Fetch feature values online and offline.

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Streaming

Compute features from streaming sources.

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Aggregations

Aggregate data over sliding time windows.

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Branches

Deploy changes to a branch and test them before release.

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Runtime and Learning

Sandboxes

Run code in isolated containers.

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Scaling groups

Deploy autoscaling HTTP services.

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Functions

Define and call remote functions.

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Model registry

Track, version, and run ML models.

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Model gateway

Route LLM traffic to multiple providers through one endpoint.

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Model training

Run training locally or on managed GPU containers.

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Evaluations

Evaluate LLM workloads against historical production data.

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Notebooks

Explore and analyze data with SQL, Python, and Markdown cells.

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Tools and operations

Platform architecture

How the parts of a Chalk deployment fit together.

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Chalk Serverless

Run Chalk on Chalk's infrastructure.

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Chalk SQL

Query everything in a Chalk environment with SQL.

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MCP server

Connect AI agents to your Chalk deployment.

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MCP gateway

Register and govern the external MCP servers your agents use.

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Observability

Monitor pipelines and get alerts when problems arise.

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Security

How Chalk protects your data.

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Billing

How Chalk bills each kind of workload.

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Clone a sample project

Get started with a sample project that demonstrates how to use Chalk.