$10M Seed Round to Accelerate ML

We’re proud to announce we’ve raised $10 million in seed funding led by General Catalyst, Unusual Ventures, and Xfund to build the data platform for machine learning and generative AI.

Announcing our $10M seed to accelerate machine learning

$50M Series A to Power AI Inference

Chalk raises $50M Series A led by Felicis Ventures to power real-time AI inference, enabling low-latency, intelligent decisions across fintech, healthcare, and more.

I'm incredibly excited to share that Chalk has raised a $50 million Series A at a $500 million valuation, led by Felicis, with participation from Triatomic Capital and our existing investors General Catalyst, Unusual Ventures, and Xfund. This funding marks a huge milestone and a powerful validation of our vision for real-time AI infrastructure.

Chalk Raises $50M Series A to Power AI Inference

Apartment List uses Chalk’s real-time feature platform to power an individualized apartment search experience.

Apartment List | Chalk Customer Story

Hyperpersonalized apartment recommendations with Chalk

Discover how Chalk’s feature platform enables Apartment List to power dynamic, fast, low-latency recommendations, unify real-time data sources, and deliver a personalized apartment search experience—while reducing engineering overhead and accelerating ML development.

Apartment List Customer Story

CB Insights AI 100

Chalk and Databricks selected as leading Machine Learning / AI Development Platforms in 2024 CB Insights AI 100

Today we're proud to announce CB Insights recognized Chalk alongside Databricks in its 2024 AI 100 as one of the leading ML / AI Development Platforms globally.

Chalk for AI Engineers

Learn why AI systems fail in production and how to fix them. Eliminate stale data, reduce tooling complexity, and build reliable AI systems that work.

Learn how AI engineers use Chalk to eliminate stale context, reduce tooling complexity, and ship AI features that work consistently in production.

Chalk for Data Engineers

Data engineers waste weeks managing ML pipelines. Learn how to escape backfill nightmares and tool sprawl with declarative features on Chalk.

Chalk on TWiST500

Chalk CEO Marc explains why speed transforms AI on This Week in Startups. Learn why speed is a feature and how Chalk delivers fresh data to ML models in milliseconds.

Our CEO Marc Freed-Finnegan joins This Week in Startups (TWiST) to explain why AI needs fresh data fast. Learn how Chalk eliminates the speed vs. freshness tradeoff.

Which LLM Wins at Nolan Trivia?

Using Chalk's prompt eval, we benchmarked top LLMs like GPT-4o, Claude, and Gemini on a custom Nolan trivia dataset—showing how teams can test, trace, and deploy LLM workflows with full observability and real-time performance.

We put Chalk’s native prompt evaluation framework to the test by benchmarking top models on a custom Christopher Nolan trivia dataset—showcasing how teams can evaluate and deploy LLM workflows with production-grade rigor.

Which LLM Wins at Nolan Trivia? Chalk’s Prompt Evaluation in Production

Data Council 2025

Explore key takeaways from Data Council 2025: why real-time is now table stakes, code-first workflows are replacing GUIs, Python is the new standard interface, and modern builders want simplicity. Plus, why community is the true backbone of innovation.

Chalk at Data Council | The Chalkboard

Insights from Data Council 2025 on the shift toward real-time inference, declarative data architectures, Python-first interfaces, and simplified data stacks—plus a look at the community shaping the future of data.

What Data Council 2025 taught us about the future of data

April Events Recap

Chalk's team recaps their April 2025 tour across major AI conferences, sharing what they learnt on real-time ML infrastructure and LLM integration.

Chalk on the Move: April Events Recap | The Chalkboard

Chalk on the Move: April Events Recap

Feature store vs. Feature engine

Learn when to use feature stores vs feature engines for ML. Understand the shift from pre-computed storage to real-time computation for modern ML infrastructure.

Discover the critical differences between feature stores and feature engines. Learn when to use each approach and how feature engines enable real-time computation and rapid experimentation for ML teams.

Feature Store at Work

Machine learning has long been deeply embedded in the field of fraud detection. A deep dive into how feature stores empower ML teams to build fraud models.

Feature Store at Work: A Tutorial on Fraud and Risk | The Chalkboard

A Tutorial on Fraud and Risk

A deep dive into how feature stores empower machine learning teams to detect fraud risk.

Chalk wins at GenAI Demo Day

This month, Chalk and Elliot Marx won Best Technology at The GenAI Collective's Demo Day by showing how Chalk's feature store can be used to unify traditional machine learning features with generative AI for parsing unstructured data.

Chalk wins Best Technology at The GenAI Collective's Demo Day | The Chalkboard

Chalk wins Best Technology at The GenAI Collective's Demo Day

Introducing Chalk NY

Today we’re thrilled to announce the opening of Chalk’s new expansion office in NYC.

Introducing Chalk NY | The Chalkboard

Today we’re thrilled to announce Chalk's expansion with the opening of a New York City office.

Chalk powers Mission Lane’s feature store for credit decisioning

Mission Lane | Chalk Customer Story

Mission Lane uses Chalk to power real-time credit approvals, fraud detection and customer-facing features, all from a single, consistent feature platform. As modeling and decisioning became core to the business, Chalk gave the Mission Lane team the infrastructure to move faster, reduce drift, and scale confidently across systems.

Mission Lane Customer Story

Serve up Some ML

Surprisingly, the majority of performance gains in machine learning are driven by feature engineering improvements. Consequently, the bulk of ML engineering effort tends to be spent iterating on feature pipelines: extracting new features from data, adding new data sources, and improving the implementation of existing features.

ML Serving for Beginners

How to Get Started

ML Training for Professional Engineers

MoneyLion powers real-time financial products with Chalk

Moneylion | Chalk Customer Story

Powering personal finance products with Chalk

MoneyLion uses Chalk’s real-time data platform to unify ML development, accelerate feature delivery, and reduce time-to-production.

MoneyLion Customer Story

Building PartiQL Support at Chalk

A deep dive into how we built support for PartiQL and DynamoDB on Chalk by using DuckDB's SQL parser

Deep Dive: Building PartiQL Support at Chalk | The Chalkboard

Deep Dive: Building PartiQL Support at Chalk

Quarterly Product Update: Spring

A roundup of Spring 2025’s latest product releases and feature updates. Chalk shipped major upgrades for ML in production—from compiling Python to C++ for faster inference, to new tools for managing features and understanding system performance.

Chalk Quarterly Product Update: Spring | The Chalkboard

This quarter’s updates help ML teams move faster in production with better performance, observability, and flexibility.

Quarterly Product Update: Summer

This quarter, we delivered platform-level improvements that extend Chalk’s ability to support complex, production-grade ML systems.

Quarterly Product Update: Summer | The Chalkboard

Quarterly Product Update: Winter

A roundup of the latest product releases, feature updates, and new documentation for the end of the year.

Chalk Quarterly Product Update: Winter | The Chalkboard

New product releases and changelog updates from Chalk for summer 2024.

Chalk Quarterly Product Update: Summer | The Chalkboard

Quarterly Product Update: Fall

A roundup of Fall 2024’s latest product releases, feature updates, and new documentation and tutorials.

Chalk Quarterly Product Update: Fall | The Chalkboard

SciPy 2025 Recap

Chalk's SciPy 2025 recap on how Python became enterprise-ready with composable data stacks, native GPU support, and sub-10ms ML inference.

SciPy 2025 Recap | The Chalkboard

The Chalk team went to SciPy 2025 in Tacoma and came back inspired. Here's our recap of all the advances we saw in Python's data ecosystem.

Chalk Completes SOC 2 Type 1 Report

Chalk is proud to announce the availability of our SOC 2 Type 1 Report.

Chalk is proud to announce the availability of our SOC 2 Type 2 Report.

Chalk completes SOC 2 Type 2 Report

Symbolic Python Interpreter

Chalk’s Symbolic Python Interpreter transforms flexible Python code into high-performance Velox-native expressions—accelerating real-time ML workloads without compromising developer experience

Accelerating Real-Time ML with Symbolic Python Interpreter | The Chalkboard

To eliminate Python’s performance bottlenecks in real-time ML, Chalk built a Symbolic Python Interpreter that converts flexible Python resolver code into optimized Velox-native expressions.

Accelerating Real-Time ML with Symbolic Python Interpreter

You shouldn't see this

This blog post is unpublished

Discover how Chalk’s platform helped Verisoul increase detection accuracy with Chalk’s powerful, efficient, and secure platform.

Verisoul Chalk Customer Story

Eliminating fake accounts with Chalk

Discover how Chalk’s platform helped Verisoul increase detection accuracy, ship updates 10x faster, and reduce engineering overhead—all while keeping critical platforms secure at scale.

Verisoul Customer Story

Verisoul Demo Recap

Learn how Verisoul combines traditional ML features with LLM analysis for fraud detection. Live demo shows building domain-based signals with Chalk's ML platform.

Verisoul Demo Recap | The Chalkboard

Verisoul shows how to catch sophisticated fraudsters by using Chalk to combine ML features with LLM intelligence. See how they rapidly iterated through three different techniques in under an hour.

Discover how Vital predicts hospital wait times with Chalk’s powerful, secure, and reliable feature platform.

Vital Chalk Customer Story

Predicting hospital wait times with Chalk

Discover how migrating to Chalk helped Vital dramatically increase the pace of its product development while giving it confidence its patient data would never leave its own cloud infrastructure.

Vital Customer Story

What is a Feature Store?

A feature store is more than a tool–it is a secret weapon that empowers teams to innovate faster, collaborate better, and deliver production grade machine learning.

Feature Stores: An Architectural Deep Dive | The Chalkboard

An Architectural Deep Dive

An introduction to the feature store: a powerful hub at the heart of machine learning platforms that every production machine learning team needs to innovate faster, collaborate better, and deliver high-performing models.

What is MLOps?

Learn why models get stuck between teams and how Chalk helps data scientists, engineers, and MLOps teams ship ML without rewrites or delays.

What is MLOps? Explore how data scientists, data engineers, AI engineers, and MLOps teams use Chalk to eliminate handoffs, prevent feature rewrites, and ship ML models faster.

What is MLOps? How different teams use Chalk

Chalk powers Whatnot’s real-time recommendation engine, enabling low-latency personalization and scalable ML infrastructure for the largest live shopping marketplace.

Whatnot | Chalk Customer Story

Whatnot uses Chalk to power real-time recommendations across its marketplace, from feed ranking to show discovery.

Whatnot Customer Story

Why We Are Better

This is a custom blog post summary that doesn't actually match the content of the blog post. Mister Peanut and his peanut factory are our biggest customer when it comes to the metric of ...

Why Chalk

One system for building, computing, and serving 

Trusted by leading ML teams
for real-time feature engineering

Used by ML and data platform teams at

Chalk delivers the platform
modern ML teams actually need

Real-time compute

Fresh data from any source

Fast iteration in Python

Unified online + offline serving

Observability and lineage

Deploy in your cloud

One system for building, computing, and serving production-grade features

Connect to any data source

The latest at Chalk

Chalk on TWiST500

Chalk for Data Engineers

Databricks competitor Chalk raises $50 million series A

Chalk named to CB Insights AI 100 list for 2024.

Production ML should be this fast

Trusted by leading ML teams for real-time feature engineering

Used by ML and data platform teams at

Chalk delivers the platform modern ML teams actually need

Real-time compute

Fresh data from any source

Fast iteration in Python

Unified online + offline serving

Observability and lineage

Deploy in your cloud

One system for building, computing, and serving production-grade features

Connect to any data source

The latest at Chalk

Chalk on TWiST500

Chalk for Data Engineers

Databricks competitor Chalk raises $50 million series A

Chalk named to CB Insights AI 100 list for 2024.

Production ML should be this fast

Trusted by leading ML teams
for real-time feature engineering

Chalk delivers the platform
modern ML teams actually need