Hugging Face: what’s new and our verdict

Our verdict

The hub where the ML community shares models, datasets and apps, with PRO, Team and Enterprise tiers.

Tested. Tested 2026-10-26 (America/Chicago). Hugging Face's Hub searched without a login in 0.1 s, a 272 MB model downloaded in 3.61 s, and transformers ran it on CPU. Test environment: Linux x86_64 virtual machine (Debian 13), 8 vCPU, 15 GiB RAM, no GPU; no vendor account, login or API key. The model was local (SmolLM2-135M-Instruct downloaded from the Hugging Face Hub and run with the transformers library on CPU (not the Qwen2.5-3B used in other tests)); we tested the tool, not any vendor's hosted models. Not tested: Gated models, private repositories, uploads, Spaces and Inference Providers: all need an account or token; Download speed for large (multi-GB) models; Any model beyond one 135M-parameter model; quality of its output was not assessed; Timing of generation: the machine was heavily shared. These are smoke tests, not a benchmark. Full log: /tests/hugging-face.

Full test log with exact commands, versions and raw output: Hugging Face huggingface_hub 2.3.0, transformers 5.20.2, torch 2.14.1 (CPU): our test log.

Evidence

Tested (smoke test, 2026-10-26, SmolLM2-135M from the Hub). A smoke test is not a benchmark.

What we ran

Hugging Face's Hub searched without a login in 0.1 s, a 272 MB model downloaded in 3.61 s, and transformers ran it on CPU.

Model used

SmolLM2-135M-Instruct downloaded from the Hugging Face Hub and run with the transformers library on CPU (not the Qwen2.5-3B used in other tests)

Environment

Linux x86_64 virtual machine (Debian 13), 8 vCPU, 15 GiB RAM, no GPU; no vendor account, login or API key

Not tested

Gated models, private repositories, uploads, Spaces and Inference Providers: all need an account or token; Download speed for large (multi-GB) models; Any model beyond one 135M-parameter model; quality of its output was not assessed; Timing of generation: the machine was heavily shared.

Date

Oct 26, 2026 (America/Chicago)

Best for

• Best AI tool for local LLM tools and model platforms: Hub for models, datasets and Spaces with paid compute.

Key facts

Hugging Face's pricing lists PRO at $9 a month, Team at $20 per user a month, a $50 per user a month tier beside Enterprise, storage rates per TB, on-demand GPU hardware starting at $0, and dedicated inference starting at $0.033 an hour.

• License: Varies by repository
• Platforms: Web, API and libraries
• Pricing model: Freemium
• Free tier: Free hub account
• Paid plans: PRO $9/month; Team $20 per user per month; a $50 per user per month tier is listed beside Enterprise; paid compute and storage
• How to start: Create a free account at huggingface.co.

• Catalog entry: facts read from the vendor's pages on 2026-10-25. We do not track release notes for this entry.

How to set it up: Get started with Hugging Face

These steps come from the vendor's own pages as read on 2026-10-25. We have not run Hugging Face.

1. Get access

   Create a free account at huggingface.co.

2. Check the limits and costs first

   Free hub account. Paid: PRO $9/month; Team $20 per user per month; a $50 per user per month tier is listed beside Enterprise; paid compute and storage. Read from huggingface.co/pricing on 2026-10-25.

Pros and cons

Alternatives and comparisons

• Ollama: The common way to run open models locally on macOS, Windows and Linux, with a cloud option and an MIT-licensed open-source core.
• Replicate: Cloud API for running and fine-tuning open models, with per-use pricing.
• Together AI: AI cloud for open models: serverless inference, fine-tuning and GPU clusters, priced per token.
• Cohere: Enterprise AI company: Command models, Rerank and the North platform.

See all alternatives to Hugging Face

FAQ

What we have not verified

• Release date (none published on the pages we read; the date shown is the day we first covered this entry)
• Free-tier quotas

Sources

• Hugging Face (fetched 2026-10-25)
• Hugging Face pricing (fetched 2026-10-25)

Last verified Oct 26, 2026 · Agentic Weekly

Related tools

• Cohere: Enterprise AI company: Command models, Rerank and the North platform.
• DeepSeek: Model API that works with the OpenAI and Anthropic SDK formats and can back agent tools such as Claude Code, GitHub Copilot and OpenCode.
• LiteLLM: Useful as a single interface in front of local and hosted models; check the license files and pin the version because it releases daily.