All Comparisons/Artificial Intelligence
Head-to-Head Intelligence Radar

Together AI vs Hugging Face

Compare open-source AI model inference platforms, hosting pricing, and developer infrastructure.

Together AI logo

Together AI

Artificial Intelligence

Cloud platform for training and running open-source AI models.

Verified Changes: 11Pricing Shifts: 1
Hugging Face logo

Hugging Face

Artificial Intelligence

The AI community building the future of open models and datasets.

Verified Changes: 10Pricing Shifts: 0

Live Verified Changes Timeline

Together AI Latest Updates

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featureSep 11, 2026

Together AI expands fine-tuning service with more models, live metrics, and finer controls

Together AI has integrated new open-weight models and introduced granular training features including Expert LoRA, early stopping, and live experiment tracking. The update also implements pre-flight validation and tokenized dataset previews alongside reduced pricing for specific models.

Service now includes Expert LoRA, live experiment tracking, early stopping, pre-flight validation, tokenized dataset previews, and reduced pricing on selected models.
featureSep 10, 2026

To Infinity and Beyond: ThunderKittens Now on NVIDIA Vera Rubin NVL72!

Together AI has ported the ThunderKittens library to support the NVIDIA Vera Rubin NVL72 architecture. The implementation includes a rebuilt NVFP4 GEMM kernel that achieves over 22 PFLOPS performance.

ThunderKittens supports NVIDIA Vera Rubin NVL72 with a rebuilt NVFP4 GEMM kernel delivering over 22 PFLOPS.
pricingSep 10, 2026

Introducing preemptible compute: the same compute, half the price

Together AI has introduced preemptible compute instances for its GPU clusters. This new offering provides identical GPU capacity at a 50% discount compared to on-demand rates, subject to a five-minute drain window.

Together GPU Clusters now offer preemptible compute instances at 50% of the on-demand rate with a five-minute termination notice.
product launchSep 9, 2026

The Open Source AI Stack

Together AI has released a comprehensive open-source AI stack designed to streamline the deployment and fine-tuning of large language models. The stack integrates optimized inference engines, data processing pipelines, and model training frameworks to reduce latency and infrastructure overhead.

Users have access to a unified, open-source stack for deploying, fine-tuning, and serving open-weights models with optimized performance.
product launchAug 28, 2026

GLM-5.3 vs. GLM-5.3 Flash on DeepSWE: Cost, Coding, and Routing

Together AI has introduced GLM-5.3 Flash as a high-efficiency alternative to the standard GLM-5.3 model. The new model achieves a 17x reduction in cost while maintaining performance within 5.6 points of pass@1 and 2.6 points of pass@4 on the DeepSWE benchmark.

Availability of GLM-5.3 Flash, offering a 17x cost reduction compared to GLM-5.3 with a 5.6 point pass@1 performance delta.

Hugging Face Latest Updates

Full Hub
featureSep 10, 2026

Rebuilding AUTOMATIC1111 with Gradio Workflow

Hugging Face has integrated the AUTOMATIC1111 Stable Diffusion web UI into a native Gradio-based workflow. This transition replaces legacy interface components with Gradio's modular blocks to improve UI responsiveness and component extensibility.

The interface is now rebuilt using Gradio blocks, enabling standardized component interaction, improved state management, and easier integration with the broader Hugging Face Gradio ecosystem.
product launchSep 9, 2026

IBM releases SOTA Granite Time Series PatchTST-FM-r2 model with commercial-friendly license

IBM has released the Granite Time Series PatchTST-FM-r2 model, a foundation model specifically optimized for time-series forecasting. The model is distributed under the Apache 2.0 license, enabling unrestricted commercial use.

Availability of the pre-trained Granite Time Series PatchTST-FM-r2 model under an Apache 2.0 license for commercial deployment.
featureSep 8, 2026

Safety for Whom? Refusing the Right Subset of a Topic, Not the Whole Topic

Hugging Face has introduced a refined safety filtering methodology that enables models to distinguish between harmful and benign sub-topics within a broader category. This approach replaces blanket topic refusals with granular classification to improve model utility while maintaining safety guardrails.

Models utilize granular safety filters capable of distinguishing between harmful and benign sub-topics, allowing for partial topic responses.

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