Side by side

DeepSpeed vs Ludwig

Both stand on deep learning software. Which one you can start free with, which one lets you read the code, and what each costs once you pay.

DeepSpeed

Free plan LinuxMacSelf-hosted

DeepSpeed combines distributed training and inference tools with integrations and monitoring options. It is free, with support and features geared toward large-scale deep learning work.

Visit DeepSpeed

Ludwig combines declarative model setup with broad data support, tuning methods and deployment options. For LLM-only fine-tuning where maximum throughput is the priority, its FAQ notes Unsloth may be faster.

Visit Ludwig

The facts

	DeepSpeed	Ludwig
Free plan	Yes	Yes
Open source	Not published	Not published
Runs on	Linux, Mac, Self-hosted	API, Linux, Mac, Self-hosted, Windows
Lowest paid plan	Free or on request	Free or on request
Plans published	1	1
Maker	Not published	Not published
Website	deepspeed.ai	ludwig.ai
On deep learning software	#8 of 35	#12 of 35

DeepSpeed: what it does well

Supports mixed precision and several forms of parallelism.
ZeRO partitions model states and gradients to reduce memory use.
Inference supports model quantization for transformer-based PyTorch models.
Monitor logs metrics to TensorBoard, WandB or CSV.
Free and Apache-2.0 licensed.

What to know first

Listed platforms are Linux, macOS and self-hosted.
Megatron and inference features are framed around specific model workflows.

Ludwig: what it does well

Validated YAML configuration avoids writing training loops
Supports multimodal and multi-task models
Includes multiple LLM tuning and parameter-efficient methods
Ray-based distributed training supports DDP, FSDP and DeepSpeed
Exports to SafeTensors, ONNX and torch.export

What to know first

Unsloth may be faster for LLM-only tuning when maximum throughput is needed
Python is the listed supported language

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