Together AI vs Hugging Face Inference Providers

Why

Read the diff table on this page as a router against a destination, not as two rivals. Together AI is one of the 18 partners counted on Hugging Face's own docs index — listed there beside Baseten, Cerebras, Fireworks and Groq — and it is one of the three providers named as serving the feature-extraction task, alongside HF Inference and Scaleway. So a great deal of what you can call through the router is Together's own hardware answering. Hugging Face describes itself as "a unified proxy layer that sits between your application and multiple AI providers"; Together's record describes a first-party inference, fine-tuning and GPU platform, "not a router", running open-weight models on its own serverless and dedicated infrastructure. The question is not which is better. It is whether you want the partner or the switchboard.

That makes the fee columns read strangely, and they should be read with care rather than scored. Hugging Face publishes token_markup_pct = 0 and a $0 seat fee, and states the position on its pricing page — "Hugging Face charges you the same rates as the provider, with no additional fees. We just pass through the provider costs directly." That is a meaningful number precisely because it is a routing layer: zero is what it takes on top of somebody else's price. Together publishes no markup figure at all, and its blank is not a disclosure failure. It is the destination, so there is no upstream list price to mark up: its record states that Together "sets its own per-token prices rather than marking up another vendor's list price", with no separate gateway or platform fee, and publishes them outright — MiniMax M3 at $0.30 per 1M input, gpt-oss-120B at $0.15, DeepSeek V4 Flash at $0.14, Kimi K3 at $3.00 input and $15.00 output, embeddings from $0.02 per 1M. Markup is not a coherent question to ask it. The same caution applies to the other two columns: Together publishes no credit-purchase fee and no seat fee because it bills on-demand usage rather than selling seats, and Hugging Face's credit_fee_pct is unset rather than zero — none of the Inference Providers pricing page, the Hub billing FAQ or the Hugging Face pricing page states a percentage or minimum on credit purchases, so the honest reading is unpublished, not confirmed free.

Both records carry model_count = 200, and the collision is a coincidence, not a tie. Together's 200 is its own serving catalogue: "200+ models" on its model directory across chat, code, image, video and audio, with 110 listed individually on the serverless page, the gap being models that dedicated endpoints and GPU clusters will run but the serverless menu does not carry. Hugging Face's 200 is a vendor headline for a routing catalogue, and its own note records the figure moving by page — "hundreds" on the docs index, "250k+ models via API" in the Hub billing FAQ — while the live public router returned 136 chat-completion models across 14 distinct providers on 2026-09-03, against 67,950 Hub rows filtered to inference_provider=all that are dominated by LoRA adapters on image providers (fal-ai 12,818, wavespeed 8,764, replicate 8,650). One number is a menu; the other is an index of everything reachable. Together's provider_count = 40 deserves the same scepticism from the other direction: its own record says Together "does not route to third-party provider APIs, so no provider count is published", which means 40 is counting model publishers or something like them, not a routing pool you can fail over between. Hugging Face's 18 is a counted list of vendors, and 14 of them actually appeared in the live catalogue.

What each side uniquely sells is where the decision actually lands. Going direct buys things the router has no surface for: a native fine-tuning API at $0.48-$2.90 per 1M tokens with a $4.00 minimum per job, dedicated single-tenant endpoints at H100 $5.49 an hour and B200 $8.99 an hour that use the same inference APIs as serverless so the migration needs no code change, GPU clusters from $3.99 per H100 GPU-hour, a native Batch API with its own published batch prices, text-to-speech as well as speech-to-text, and automatic prefix caching whose published effect is large where it lands — Kimi K3 at $0.30 cached against $3.00, MiniMax M3 at $0.06 against $0.30. There is also a genuine per-request control: adding "safety_model": "Meta-Llama/Llama-Guard-7b" makes Together run the safety model and filter the response, chosen by the caller. Going through the router buys the opposite kind of thing: one Hugging Face token across all 18 partners, provider="auto" falling through to an alternative when the primary is flagged unavailable, every mapped model tested every six hours against a sub-five-second time-to-first-token admission bar with failing providers pulled and retested hourly, and a policy change that is a suffix — :fastest, :cheapest, :preferred, or a pin. Neither is a superset. Together has no cross-provider failover documented at all; Hugging Face has no batch endpoint, no text-to-speech task page, no cache of its own, and no user-settable timeout, retry, weight or region.