Chalk

Founded by Marc Freed‑Finnegan & Elliot Marx & Andy Moreland in 2022

Serve live context and run AI models

Pricing model

Demo, Paid

Rating

★ People love it

Time to value

Moderate Setup (1–3 hours)

You can use Chalk to serve fresh data features directly to machine learning models and AI agents during real-time inference. It connects to databases and data warehouses like Postgres, Snowflake, and Stripe without requiring complex pipelines. You can define feature calculations using Python and SQL, execute low-latency queries, and serve computed context in milliseconds. Chalk also provides runtime environments for serving open-source or custom models on GPUs, running code inside sandboxed containers, evaluating model changes, and tracing execution steps before deploying updates to production.

What Chalk does

Calculate windowed metrics over transactional data using PythonDeploy a model inference endpoint with custom GPU scaling configurationsSpin up an isolated sandbox to run arbitrary code or CLI toolsConnect relational databases and cloud warehouses into a single query layerRun evaluation suites against real-world data before shipping model updatesInspect latency and data payloads for inference traces in productionServe computed features and context at inference time with sub-10ms latencyDefine feature pipelines and caching logic using composable Python and SQLQuery across multiple data sources using a federated HTAP engine without ETL pipelinesHost and serve open-source models with auto-scaling GPU computeRun agents and arbitrary code in secure sandboxed container environmentsTest and evaluate model modifications against historical data before deploymentInspect execution steps and trace inference requests across data pipelines