Comma

Hugging Face Spaces alternatives

Spaces is the best free place on the internet to put a live model demo. Gradio or Streamlit in a repo, hardware attached, a URL anyone can try. Nothing here argues with that.

It is the wrong shape for the other half of ML work: the eval run, the benchmark table, the model card, the red-team summary — artifacts that are read, disputed and cited rather than driven.

Where a Space is the wrong container

  • A runtime you don't need. A results page has no inputs. Paying for a container, a queue and a cold start to display a table is overhead with no upside.
  • Sleep on the free tier. The reader who opens your link on Monday waits for a wake-up they did not ask for.
  • Access is Hub-shaped. Private Spaces are real, but they require the reader to have an account and to be in the right org. Handing an external auditor a Hub invite is a conversation.
  • No review layer. Nobody can flag row 14 of the eval table. The disagreement lands in Slack, re-described in prose.
  • Rebuilt, not versioned. "What did this metric say before the data fix?" is answered by git archaeology, not by the artifact.

The shortlist

1. Comma — publish the eval artifact, keep the Space for the demo

Render the run to HTML and publish it from the same job:

import os, requests

r = requests.post(
    "https://commareports.com/api/v1/reports",
    headers={"Authorization": f"Bearer {os.environ['COMMA_API_TOKEN']}"},
    json={
        "title": "eval — llama-guard v3, 2026-09-06",
        "html": open("out/eval.html").read(),
        "visibility": "team",
    },
)
report_id = r.json()["id"]

No runtime, so it opens instantly and stays open. Visibility is per report — private, team, your email domain, any signed-in user, or a link — so an external reviewer needs a link, not an org seat. Readers select a row in the results table and leave a thread anchored to it, which survives the next publish. PATCH the same id after the next run and the history is diffable.

Agents publish through the same scoped token over REST or MCP, which matters when the eval is kicked off by a coding agent rather than a human.

Not for: running the model. The Space keeps doing that.

Pricing: Free — unlimited reports, viewers, commenters and revisions.

Share an LLM eval report →

2. Keep the Space, add a results destination

The common end state, and the one worth aiming at: the public Space is the try-it-yourself surface; every eval run publishes a private report the team reviews. They answer different questions and neither one has to grow into the other.

3. Weights & Biases / MLflow — when it is really experiment tracking

If the need is comparing hundreds of runs with metrics over time, a tracking platform is built for it. Comma's unit is one rendered artifact you want people to read and annotate; see sharing an MLflow report.

4. Streamlit Community Cloud — same free-demo niche

The direct peer for hosted app demos, with its own sleep and privacy tradeoffs. See Streamlit Community Cloud alternatives.

At a glance

Option Cold start Reader needs an account Comments on results Versions
Comma None No (link) / SSO Yes, anchored Yes
HF Spaces (free) Yes Yes, if private No Rebuilds
Self-hosted app Depends Depends No No
W&B / MLflow None Yes Limited Yes

Checked September 2026. Verify current plans before committing.

How to choose

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