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Real-time feature serving for production ML

Serve production features at inference time with single-digit millisecond latency. Chalk’s query execution engine runs Python functions, queries databases, and calls APIs in real-time, enabling decisions on the freshest data without brittle ETL or stale caches.

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Trusted by teams building the next generation of AI + ML

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Why teams choose Chalk for real-time serving

from chalk import online

@online
def get_username(email: User.email) -> User.username:
    username = email.split("@")[0]
    if "gmail.com" in email:
        username = username.split("+")[0].replace(".", "")
    return username.lower()

Built for real-time

Chalk is a query execution engine that can run Python functions, query databases, and call APIs at inference time. Compute the freshest data without complex and stale values from reverse ETL.

Python Resolvers

Low latency by design

Chalk parallelizes execution and builds an optimized query plan that avoids redundant computation, enabling sub-5ms end-to-end feature retrieval.

QUERY PLANNER

Deploy securely in your VPC

Deploy Chalk directly inside your AWS, GCP, or Azure cloud account. Keep all data, features, and models within your VPC while operating under your own IAM, networking, and encryption standards.

DEPLOY IN YOUR VPC

Performance at scale

Resolve feature values in single-digit milliseconds under peak load.

Consistent with training

Unify feature definitions across batch, real time, and inference without drift or duplicated pipelines.

Native Python support

Use familiar tools like Pydantic, Numpy, and Pandas. Chalk runs your Python code at scale without DSLs or wrappers.

Declarative pipelines

Define dependencies with Python signatures. Chalk orchestrates resolvers into efficient query plans across online/offline environments.

Iterate faster

Experiment faster. Chalk spins up deployments per branch for safe iteration and review.

Rust-powered runtime

Run Python at native speed. Chalk uses Rust to parallelize fetches, push down ops, and multithread computations.

Chalk has transformed our ML development workflow. We can now build and iterate on ML features faster than ever, with a dramatically better developer experience. Chalk also powers real-time feature transformations for our LLM tools and models — critical for meeting the ultra-high freshness standards we require.

Jay Feng ML Engineer

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Accelerated execution with Python-to-Rust transpilation

# get_username converted into a static expression
lower(
  if_then_else(
    !=(
       strpos(str(email), str(gmail.com)),
      int(0)
    ),
    replace(
      element_at(
        split(
          element_at(split(str(email), str(@)), int(1)),
          str(+)
        ),
        int(1)
      ),
      str(.),
      str()
    ),
    element_at(split(str(email), str(@)), int(1))
  )
)

One language and one system for training and inference. Your Python becomes the source of truth and is automatically optimized for real-time execution. Chalk:

  • Builds highly efficient query plans so you only compute and fetch what you need.
  • Parses and transpiles your Python into static native expressions that run at Rust speed.
  • Centralizes your ML logic in code, removing duplication and eliminating feature rewrites.
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Compute fresh features. Serve them in single-digit milli­seconds. Unify your ML workflow.

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Explore more of Chalk’s data platform

LLM Toolchain

LLM Toolchain

Unify prompt engineering, embeddings, vector search, and inference to ship LLMs faster.

Temporal Aggregations

Temporal Aggregations

Define aggregations once. Reuse them across batch, online, and real-time workloads.

Training Data

Training Data

Generate point-in-time correct training datasets from production feature definitions.

Feature Store

Feature Store

Define features once, and compute them on demand for training, batch scoring, and inference.