Production infra for
LLM inference

A unified interface to serve, observe, and optimize LLMs with structured and unstructured data — with sub-5ms latency, fallback, and real‑time eval.

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Loved by AI and data teams at

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LLM Toolchain Benefits

One platform. One toolchain. All the way to production.

Prompt Engineering

Experiment with prompts on historical data using branches. Chalk tracks outputs, computes metrics, and promotes winning prompts with one command.

Model Inference

Deploy inference pipelines with autoscaling and GPU support. Write pre/post-processing in Python—Chalk handles the rest, including data logging and versioning.

Evaluations

Log and compare model outputs with quality metrics to pick the best prompt, embedding, or model—all versioned automatically in Chalk.

Embedding Functions

Use any embedding model with one line of code. Chalk handles batching, caching, and lets you safely test new models on all your data.

Vector Search

Run nearest-neighbor search directly in your feature pipeline. Use any feature as the query, and generate new features from search results.

Large File Support

Process and embed large files—docs, images, videos—at scale. Chalk handles batching, autoscaling, and execution with a fast Rust backend.

Chalk powers our LLM pipeline, turning complex inputs—HTML, URLs, screenshots—into structured, auditable features. It lets us serve lightweight heuristics up front and rich LLM reasoning deeper in the stack, so we catch threats others miss without compromising speed or precision.

Rahul Madduluri
CTO at Doppel
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Real-time feature serving for LLMs

Connect your LLMs to the freshest data without ETL pipelines

  • Retrieve structured features dynamically at inference time
  • Use Python (not DSLs) to define feature logic
  • Fetch real-time context windows with point-in-time correctness
  • Mix embeddings and features for fully grounded RAG workflows

Prompt engineering & evaluation

Design prompts like you design software

  • Write, version, and reuse prompts with structured parameters
  • Evaluate prompts and models using historical production data
  • Compare model performance on accuracy, latency, and token usage
  • Debug failures with end-to-end traceability and lineage
  • Deploy prompt + model bundles as artifacts with full observability

Want to see how Chalk compiles prompt logic, feature queries, and completions into optimized inference pipelines?