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Daft: High-Performance Data Engine for AI and Multimodal Workloads
Daft is a high-performance data engine for AI and multimodal workloads. Process images, audio, video, and structured data at any scale.
Native multimodal processing: Process images, audio, video, and embeddings alongside structured data in a single framework
Built-in AI operations: Run LLM prompts, generate embeddings, and classify data at scale using OpenAI, Transformers, or custom models
Python-native, Rust-powered: Skip the JVM complexity with Python at its core and Rust under the hood for blazing performance
Seamless scaling: Start local, scale to distributed clusters on Ray, Kubernetes
Universal connectivity: Access data anywhere (S3, GCS, Iceberg, Delta Lake, Hugging Face, Unity Catalog)
Out-of-box reliability: Intelligent memory management and sensible defaults eliminate configuration headaches
Getting Started
Installation
Install Daft with pip install daft. Requires Python 3.10 or higher.
For more advanced installations (e.g. installing from source or with extra dependencies such as Ray and AWS utilities), please see our Installation Guide
Quickstart
Get started in minutes with our Quickstart - load a real-world e-commerce dataset, process product images, and run AI inference at scale.
More Resources
Examples - see Daft in action with use cases across text, images, audio, and more
User Guide - take a deep-dive into each topic within Daft
API Reference - API reference for public classes/functions of Daft
Benchmarks
To see the full benchmarks, detailed setup, and logs, check out our benchmarking page.
Contributing
We ❤️ developers! To start contributing to Daft, please read CONTRIBUTING.md. This document describes the development lifecycle and toolchain for working on Daft. It also details how to add new functionality to the core engine and expose it through a Python API.
Here’s a list of good first issues to get yourself warmed up with Daft. Comment in the issue to pick it up, and feel free to ask any questions!
Telemetry
To help improve Daft, we collect non-identifiable data via Scarf (https://scarf.sh).
To disable this behavior, set the environment variable DO_NOT_TRACK=true.
The data that we collect is:
Non-identifiable: No session IDs or user identifiers are collected
Metadata-only: We do not collect any of our users’ proprietary code or data
For development only: We do not buy or sell any user data
Please see our documentation for more details.
License
Daft has an Apache 2.0 license - please see the LICENSE file.
Release files for daft 0.7.25
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Built distributions (wheels)
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|---|---|---|---|---|
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| daft-0.7.25-cp310-abi3-manylinux_2_24_x86_64.whl | CPython 3.10 | abi3 | Linux glibc 2.24+ x86-64 | Details |
| daft-0.7.25-cp310-abi3-manylinux_2_24_aarch64.whl | CPython 3.10 | abi3 | Linux glibc 2.24+ ARM64 | Details |
| daft-0.7.25-cp310-abi3-macosx_11_0_arm64.whl | CPython 3.10 | abi3 | macOS 11.0+ ARM64 | Details |
| daft-0.7.25-cp310-abi3-macosx_10_12_x86_64.whl | CPython 3.10 | abi3 | macOS 10.12+ x86-64 | Details |
Total release size: 272.9 MB
Release files / daft-0.7.25-cp310-abi3-win_amd64.whl
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Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
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PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Sep 12, 2026.
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Provenance
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