Daft is a high-performance data engine built for the specific demands of AI workloads. While legacy tools like Apache Spark or Pandas were designed primarily for tabular data, Daft focuses on multimodal data—images, video, and unstructured text—alongside traditional columns. It is developed by Eventual, a company that has raised $30 million to solve the friction of moving AI models from a laptop to a production environment.

Consistency from local to production

The core of the offering is an open-source Python framework. Its primary value proposition is consistency. A developer can write a pipeline on their local machine to process a few dozen rows, and then run that same code across petabytes of data in a distributed cloud environment. This eliminates the rewrite phase that often plagues data science teams transitioning from experimentation to engineering. By providing a unified API, Daft ensures that data processing logic remains identical regardless of the underlying scale.

Multimodal data as a first-class citizen

Unlike general-purpose data frames, Daft includes first-class operators for AI-specific tasks. This includes ingestion, chunking for vector databases, generating embeddings, and performing LLM extraction. Instead of stitching together separate tools for ETL and model inference, Daft treats model calls as native parts of the data pipeline. This is particularly important for tasks like structured output generation, where a user needs to extract specific data points from millions of documents into a schema. The engine is written in Rust, which allows it to handle heavy-duty processing with a performance profile that exceeds pure-Python alternatives.

Competition and funding

The competitive environment for Daft is defined by the tension between established data engineering tools and new, AI-native entrants. Spark is the incumbent for large-scale processing but often feels heavy and lacks native support for modern multimodal types. Pandas is the developer favorite for local work but fails to scale. Daft positions itself as the middle ground: the simplicity of a Pythonic API with the power of a distributed engine.

Eventual, the company behind Daft, secured its funding from a list of high-profile investors including Felicis, CRV, and M12. This backing reflects a bet on the data-for-AI layer of the stack. As companies move beyond simple chat interfaces and toward complex agentic systems, the bottleneck shifts from the model to the data pipeline. Daft aims to be the standard plumbing for these systems.

The project has gained significant traction in the open-source community, surpassing 5,000 stars on GitHub. For organizations requiring managed infrastructure, the company offers Daft Cloud. This service handles the orchestration and scaling of the open-source engine, allowing teams to focus on their model logic rather than cluster management. In a market where many AI startups focus on the application layer, Daft is building the fundamental infrastructure required to handle the massive amounts of data those models consume.