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Decision brief
Daft is a Rust-based high-performance data engine for AI and multimodal workloads that supports processing various types of structured and unstructured data at scale.
Good fit when
- - When you require high performance and efficiency in a multilingual environment, particularly if projects are primarily developed in Rust
- - If your project involves heavy multimedia data handling, including images, audio, video alongside structured data
Avoid when
- - Avoid using Daft for projects where Python dominates the tech stack or development ecosystem
- - When performance requirements are lower and ease of use is prioritized over speed
Observed Jul 17, 2026 · Source: enrich:decision_facts
Verify the decision
Maintenance and security
Full trust report- Maintenance
- Very active (0d since push)
- As of today
- Provenance
- Not a fork · Organization account
- As of today
- Security (OSV)
- No lockfile
- As of 1mo
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
cargo add DaftSimilar tools
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Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
Provides capabilities to process images, audio, video, and structured data at scale designed with ai engineering, big-data, and distributed computing needs in mind.
Capability facts
- CLI
- CLI entrypoint
Source: pyproject.toml:[project.scripts] · Aug 22, 2026
- Languages
- rust, python
Source: github.language+pyproject.toml · Aug 22, 2026
Categories
Compatibility
Sourced claims from the README excerpt - not unsourced marketing copy.
Source: README excerpt (regex_v1, Aug 22, 2026)
* **Python-native, Rust-powered:** Skip the JVM complexity with Python at its core and RusSource link
Tags
README
|Banner|
|CI| |PyPI| |Latest Tag| |Coverage| |Slack|
Website <https://www.daft.ai>_ • Docs <https://docs.daft.ai>_ • Installation <https://docs.daft.ai/en/stable/install/>_ • Daft Quickstart <https://docs.daft.ai/en/stable/quickstart/>_ • Community and Support <https://github.com/Eventual-Inc/Daft/discussions>_
Daft: High-Performance Data Engine for AI and Multimodal Workloads
|TrendShift|
Daft <https://www.daft.ai>_ 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 <https://docs.daft.ai/en/stable/distributed/ray/>,Kubernetes <https://docs.daft.ai/en/stable/distributed/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 <https://docs.daft.ai/en/stable/install/>_
Quickstart ^^^^^^^^^^
Get started in minutes with our Quickstart <https://docs.daft.ai/en/stable/quickstart/>_ - load a real-world e-commerce dataset, process product images, and run AI inference at scale.
More Resources ^^^^^^^^^^^^^^
Examples <https://docs.daft.ai/en/stable/examples/>_ - see Daft in action with use cases across text, images, audio, and moreUser Guide <https://docs.daft.ai/en/stable/>_ - take a deep-dive into each topic within DaftAPI Reference <https://docs.daft.ai/en/stable/api/>_ - API reference for public classes/functions of Daft
Benchmarks
|Benchmark Image|
To see the full benchmarks, detailed setup, and logs, check out our benchmarking page. <https://docs.daft.ai/en/stable/benchmarks>_
Contributing
We ❤️ developers! To start contributing to Daft, please read CONTRIBUTING.md <https://github.com/Eventual-Inc/Daft/blob/main/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 <https://github.com/Eventual-Inc/Daft/issues?q=is%3Aopen+is%3Aissue+label%3A%22good+first+issue%22>_ 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 <https://docs.daft.ai/en/stable/telemetry/>_ for more details.
.. image:: https://static.scarf.sh/a.png?x-pxid=31f8d5ba-7e09-4d75-8895-5252bbf06cf6
Related Projects
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