The AI infrastructure startup ecosystem continues to expand as new companies tackle the challenges of large-scale machine learning. One of the latest is Chalk AI, which recently raised $40 million in Series B funding (over $57 million to date). Chalk is positioning itself not as just another platform, but as both a challenger and, in some cases, a complement to giants like Databricks, particularly in the demanding domain of real-time AI inference.
Background
- Company: Chalk AI
- Founded: 2022
- HQ: San Francisco
- Co-founders: Elliot Marx, Marc Freed-Finnegan, and Andrew Moreland
- Funding: $50M (Series A) at $500M Valuation
- # of Employees: 69 (LinkedIn)
- Product: AI Inference Platform
How Chalk Got Started
Chalk AI was founded by former Google and Meta engineers, including CEO and co-founder Fraser Herring. Their journey began from a shared frustration with the complexities of building and deploying real-time AI systems at scale. While data warehousing and big data processing had made significant strides, getting fresh, relevant data to models at the exact moment of inference remained a persistent challenge.
Many organizations found themselves spending months, or even years, building custom infrastructure to bridge the gap between their offline training environments and the low-latency demands of production models. This “last mile” problem often led to:
- Train-Serve Skew: The data used to train a model differed from the data it saw in production, leading to degraded model performance.
- High Latency: Models couldn’t get the freshest data fast enough, impacting the quality of real-time decisions.
- Developer Overhead: Data scientists and ML engineers spent more time on infrastructure than on building and improving models.
Chalk was born out of the vision to solve these problems by providing a purpose-built platform for real-time feature engineering and serving.
The Problems Chalk is Solving: Real-Time Features for Real-Time Decisions
At its core, Chalk AI is a real-time feature engine and feature store. Its primary goal is to empower organizations to build and deploy machine learning and generative AI applications that demand instant, data-driven decisions. Think fraud detection, personalized recommendations, dynamic pricing, or instant loan approvals.
Here’s how Chalk addresses key challenges:
- Eliminating Train-Serve Skew: Chalk’s standout innovation is its ability to use the exact same Python code for both generating offline training datasets and serving live features to models in production. This fundamental design choice is a game-changer for model reliability and consistency.
- Ultra-Low Latency Inference: Built on a high-performance, Rust-based engine, Chalk compiles Python feature definitions into incredibly fast, parallel pipelines. This ensures that features are computed and served with latencies often measured in single-digit milliseconds, critical for real-time applications.
- Developer Productivity: Data scientists and ML engineers can define features using familiar Python, freeing them from the complexities of managing underlying infrastructure, data pipelines, and distributed compute systems.
- Unified LLM Toolchain: Recognizing the rise of generative AI, Chalk also unifies structured business data with unstructured data. It includes a vector database and integrations for Retrieval Augmented Generation (RAG) workflows, making it easier to ground LLMs with up-to-date, relevant internal data.
- Robust MLOps: Chalk provides essential MLOps capabilities right out of the box, including versioning, branching for safe experimentation, comprehensive monitoring for data quality and pipeline health, and a centralized feature store for discovery and reuse.
Chalk AI vs. Databricks: Where the Battle Lines Are Drawn
Databricks, with its robust Lakehouse Platform, is a powerhouse for data engineering, large-scale analytics, and machine learning training. It offers a comprehensive suite of tools for ETL, data warehousing, data science, and MLOps, all built on Apache Spark.
While both platforms aim to support the entire AI lifecycle, their core strengths and competitive focus differ significantly:
|
Aspect |
Chalk AI’s Competitive Edge |
Databricks’ Core Strength |
|
Primary Focus |
Real-Time AI Inference and ultra-low latency feature serving. |
Large-Scale Data Processing, ETL, and Model Training (Batch-oriented workloads). |
|
Data Freshness |
High performance for large data, but not inherently designed for extremely low-latency online serving. |
Supports various methods for feature engineering, often relying on Spark; feature stores are available but real-time serving is less inherent. |
|
Latency for Serving |
Extremely Low: Optimized for single-digit millisecond feature retrieval. |
Batch-Oriented: Data is often processed in batches; real-time serving requires additional specialized components. |
|
Feature Engineering |
Unified Engine: Python code translated to high-performance Rust for both training & serving. Prevents train-serve skew. |
Supports various methods for feature engineering, often relying on Spark; feature stores are available, but real-time serving is less inherent. |
|
Architecture |
Purpose-built for high-performance, real-time feature serving and inference. |
Lakehouse architecture, designed for comprehensive data management and big data processing. |
The “Inference Shift”: Chalk’s thesis is that a significant portion of AI compute is shifting from model training (where Databricks excels) to real-time model inference. As more businesses embed AI directly into live user experiences, the demand for instant, fresh data will only grow. This is where Chalk aims to be the undisputed leader.
Complements, Not Just Competitors
It’s important to note that Chalk and Databricks are not always mutually exclusive. In many enterprise settings, they can be complementary. An organization might use Databricks for:
- Ingesting and transforming massive datasets.
- Building a comprehensive data lakehouse.
- Training complex machine learning models at scale.
Then, Chalk AI can integrate with this data infrastructure to:
- Access the raw or pre-processed data from Databricks.
- Perform real-time feature engineering on that data.
- Serve those fresh features with ultra-low latency to models making live predictions.
Summary
Chalk AI represents a new generation of AI infrastructure that acknowledges the growing importance of real-time, low-latency data for production models. By focusing on the “last mile” of AI deployment and simplifying the complexities of feature engineering and serving, Chalk is empowering organizations to build more robust, performant, and reliable AI applications. As the demand for instant, intelligent decisions continues to soar, platforms like Chalk AI will undoubtedly play a crucial role in shaping the future of AI.