About 16; min
Open-source AI models need a place to live and run. Hugging Face and Together AI both serve this purpose but from different angles. Hugging Face is the GitHub of machine learning — a model repository, community hub, and inference platform combined. Together AI is a focused inference and fine-tuning provider built for production workloads. Here’s how they compare for developers working with open-source models in 2026.
Quick Comparison
| Feature | Hugging Face | Together AI |
|---|---|---|
| Primary Role | Model hub + community + inference | Inference API + fine-tuning |
| Models Available | 800,000+ (repository) | 200+ (hosted for inference) |
| Inference API | Yes (serverless + dedicated) | Yes (serverless) |
| Fine-Tuning | AutoTrain + manual | Serverless fine-tuning |
| Free Tier | Yes (rate-limited inference) | $5 credit |
| Model Hosting | Inference Endpoints ($0.06/hr+) | Serverless only |
| Spaces (App Hosting) | Yes (Gradio, Streamlit) | No |
| Datasets | 200,000+ datasets | No dataset hosting |
| Community | Largest ML community | Developer-focused |
| Image Models | Yes (Stable Diffusion, FLUX) | Yes (FLUX, SDXL) |
| Embedding Models | Yes | Yes |
Hugging Face Overview
Hugging Face hosts over 800,000 models and 200,000 datasets — the central repository for the entire open-source AI community. The free tier includes rate-limited serverless inference, model hosting, dataset hosting, and Spaces (deploy Gradio or Streamlit apps). The Pro plan at $9 per month adds higher inference rate limits, early access to new features, and private model hosting. Enterprise Hub at $20 per user per month includes SSO, audit logs, and advanced access controls. Inference Endpoints deploy any model as a dedicated API starting at $0.06 per hour for CPU and $0.60 per hour for GPU. Serverless Inference API provides pay-per-request access to popular models. AutoTrain handles fine-tuning through a no-code interface. For the ML community, Hugging Face is where you find models, share research, and collaborate on AI development.
Together AI Overview
Together AI hosts 200+ open-source models for production inference through a single API. Llama 3.1 8B costs $0.18/M tokens, 70B costs $0.88/M, and 405B costs $5.00/M. Qwen, Mistral, DeepSeek, Gemma, and FLUX are all available. Serverless fine-tuning lets you customize any supported model — upload JSONL training data, select a base model, and Together handles GPU allocation, training, and deployment. The fine-tuned model runs at standard inference pricing. Image generation through FLUX and SDXL costs $0.006-0.05 per image. Embedding models cost $0.008/M tokens. The API follows OpenAI’s format, making migration straightforward. For teams building production AI applications that need reliable, fast inference across multiple model families, Together AI provides the most complete API platform for open-source models.
Inference Pricing Comparison
| Model | Hugging Face (Serverless) | Together AI |
|---|---|---|
| Llama 3.1 8B | Rate-limited free / $0.03/1K chars | $0.18/M tokens |
| Llama 3.1 70B | ~$1.00/M tokens (dedicated) | $0.88/M tokens |
| Mistral 7B | Rate-limited free | $0.20/M tokens |
| FLUX (image) | ~$0.01-0.05/image | $0.006-0.05/image |
| Embeddings | Free (rate-limited) | $0.008/M tokens |
| Dedicated GPU | $0.60/hr+ (Endpoints) | Not available (serverless only) |
Key Differences
Model Discovery vs Production Inference
Hugging Face is where you discover and evaluate models. Browse 800,000+ models, read model cards, check benchmarks, and test with free inference. The community discusses model quality, shares fine-tunes, and publishes research. Together AI is where you run models in production. The curated library of 200+ models is pre-tuned for speed and reliability. Every model runs on infrastructure designed for low-latency, high-throughput serving. The difference is research-and-discovery versus production-and-scale.
Fine-Tuning Approach
Hugging Face provides the tools (Transformers library, AutoTrain, PEFT) and the models, but you manage the compute. AutoTrain simplifies the process with a no-code interface, but underlying GPU costs apply. You can also fine-tune locally or on any GPU cloud using Hugging Face’s open-source libraries. Together AI’s serverless fine-tuning handles everything — upload data, select a model, and receive a fine-tuned model hosted for inference. No GPU management, no training infrastructure, no checkpoints to manage. For teams without ML engineering resources, Together’s approach is significantly simpler.
Beyond LLMs
Hugging Face hosts every type of ML model — computer vision (YOLO, SAM), audio (Whisper, Bark), tabular data, reinforcement learning, and scientific models. The Spaces platform hosts interactive demos and applications built with Gradio or Streamlit. Datasets cover every domain from NLP to medical imaging. Together AI focuses primarily on text generation, image generation, and embeddings. If your workload involves non-LLM models (object detection, speech recognition, specialized ML), Hugging Face is the only option that covers the full spectrum.
Who Should Pick What
Pick Hugging Face if:
- You need access to 800K+ models across all AI domains (not just LLMs)
- Model discovery, evaluation, and community discussion inform your choices
- Dedicated GPU endpoints give you control over scaling and latency
- Datasets, Spaces, and the Transformers library are part of your ML workflow
Pick Together AI if:
- Production inference across 200+ open models through a single reliable API
- Serverless fine-tuning without managing GPUs or training infrastructure
- OpenAI-compatible API format simplifies integration and migration
- Competitive per-token pricing beats Hugging Face’s serverless rates for LLMs
Together AI wins for teams building production AI applications. The curated model library, reliable serverless inference, competitive pricing, and serverless fine-tuning provide the complete pipeline from model selection to production deployment. Hugging Face earns runner-up as the essential platform for the broader ML workflow — model discovery, community, datasets, and the open-source tools that the entire AI field depends on. Most teams use both: Hugging Face for research, evaluation, and model discovery; Together AI for production inference and fine-tuning. They’re complementary more than competitive.
FAQ
Can I use Hugging Face models on Together AI?
Together AI hosts popular Hugging Face models (Llama, Qwen, Mistral, etc.) on their inference platform. You discover models on Hugging Face, then run them through Together’s production API. The model weights are the same; the serving infrastructure differs.
Is Hugging Face free?
The platform (model hosting, datasets, Spaces) is free. Serverless inference has free rate-limited access for popular models. Dedicated Inference Endpoints and compute-intensive features are paid. The Pro plan ($9/mo) increases rate limits and adds features.
Which is faster for LLM inference?
Together AI. Their infrastructure is tuned specifically for LLM serving speed. Hugging Face’s serverless inference is convenient but slower. Hugging Face Inference Endpoints (dedicated) match Together’s speed but at higher per-hour cost for comparable GPUs.
Do I need both?
Most AI teams use Hugging Face for the model hub, Transformers library, and community. Production inference runs through Together AI, Groq, or another specialized provider. Using both is the standard workflow in 2026 — Hugging Face for research, a specialized provider for production.
