Welcome to the Daft blog
Join us as we explore innovative ways to handle multimodal datasets, optimize performance, and revolutionize your data workflows.
Eventual Raises $30M to Build the Future of Data
Fall 2025 Review: Daft Open Source Updates
Highlights from new AI Functions, updated UDFs, and daft.File upgrades.
Cutting LLM Batch Inference Time in Half: Dynamic Prefix Bucketing at Scale
A new inference backend that maximizes batch inference throughput.
Simplifying Voice AI Analytics with Daft: Transcription, Summaries, and Embeddings at Scale
How Daft simplifies voice AI analytics pipelines for meeting summaries, subtitle translations, and more.
Using PyTorch DataLoaders to Streamline Multimodal Data
Learn how PyTorch's DataLoader streamlines deep learning pipelines by efficiently loading and shuffling data in batches.
Benchmarks for Multimodal AI Workloads
Spark, Ray Data, and Daft
Introducing Flotilla: Simplifying Multimodal Data Processing at Scale
Daft's new distributed engine
Exploring Daft's Local Execution
The Swordfish Engine
After the First Run
Using Daft’s observability tools to uncover performance pitfalls
Making GPUs Zoom (Part 1)
A deep dive into GPU optimizations for production-scale multimodal data processing
End-to-End Distributed PDF Processing Pipeline
OCR, Spatial Analysis & GPU Embeddings with Python
How to Build Scalable, End-to-end Batch Inference Pipelines with Daft
From prompts to parquet: making batch inference simple, fast, and scalable.
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How Essential AI Built Essential-Web v1.0 with Daft
